Reduction started at: 20040909-094923 Reading data from rxh3-20040909-024418.fits Reduction ID: default Read keywords into global array holo_keys Read binary table data into global array holo_data Finished reading data Converted positions to arcsec, times to elapsed seconds, and reversed x-axis Pattern extent: min = 2402.6 max = 2438.6 arcsec Nominal defocus setting was 31. mm Using actual defocus setting of 33.870 mm ----------------- Data Summary --------------------- Number of samples: 1466025 This is a 160 GHz map Number of frequencies: 16 Frequencies (GHz): 160.676000 160.680000 160.684000 160.688000 160.692000 160.696000 160.700000 160.704000 160.708000 160.712000 160.716000 160.720000 160.724000 160.728000 160.732000 160.736000 item min max mean loreal -2.94189 2.83691 -0.00262 loimag -3.07861 2.82227 -0.00785 hireal -5.00000 4.99756 -0.00254 hiimag -5.00000 4.99756 0.01218 xpos -2438.58247 2410.75370 -10.95515 ypos -2402.61305 2402.77901 -0.00366 plock160 0.78857 2.16064 1.53561 lorefpwr 1.36719 2.89795 2.45237 losigpwr -4.56787 -0.48340 -4.45493 hirefpwr 1.36963 2.88574 2.46631 hisigpwr -4.48242 4.99756 -1.44716 encltemp 31.54297 32.86133 32.10080 flags 0.00000 256.00000 2.44471 phi-lock -1.34277 -0.00488 -0.70765 sindex 0.00000 254.00000 126.60925 time 0.00000 5876.99381 2937.60485 zeropt -0.00732 -0.00244 -0.00470 !!!Warning!!! philock max less than 0.2 ---------------------------------------------------- Subtracting zeropt channel Data contains a total of 255 rows There are 241 data rows and 14 calibrator rows Calibrator rows: 0 21 42 63 84 105 126 147 168 189 210 231 252 254 Checking pointing along rasters... This map is more horizontally scanned than vertically Mean row spacing = 20.00304 arcsec Mean row spacing = 20.00304 arcsec (alternate estimator) Mean tracking incline = -0.19843 arcsec Mean pointing range = 0.59708 arcsec Mean pointing rms = 0.11469 arcsec This map *probably* has non-inclined rows Applying pointing shifts: (-5.1, 10.6 ) arcsec Applying pointing lags: (0, 0 ) arcsec Deciphering frequencies... Selecting hi/lo channels using method 2 Inverting the phase on this 160 GHz map Doing geometric phase correction Status bits counts: bit: 0 1 2 3 4 5 6 7 8 set: 0 0 0 0 0 0 0 0 14000 Extracting frequencies Selecting all rows from the map (row = -1) Extracted frequency 0: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 1: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 2: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 3: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 4: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 5: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 6: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 7: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 8: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 9: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 10: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 11: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 12: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 13: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 14: 90356 data points Selecting all rows from the map (row = -1) Extracted frequency 15: 90356 data points No calibration requested... Creating template maps for gridding Using a grid cellsize of 20.0 arcseconds Using a grid of 256 points Grid has even number of points Maximum data offset = 2438.58 arcsec Grid extent = 2550 arcsec lambda_min = 0.00186512 scale = 0.00259937 Diffraction scale lambda/D = 25.6569 arcsec Gridding function extent = 153.941 arcsec Using Gaussian * Airy regridding function Gaussian FWHM = 76.9706 arcsec Airy first null at 31.2929 arcsec Gridding frequency index 0 lambda = 0.00186582 metres, scale = 0.0025984 radians per metre Gridding real part of frequency 0... Gridding imag part of frequency 0... Pattern is holo(res.pattern0) Weights in holo(obs.real,wt0) and holo(obs.imag,wt0) Maximum amplitude = 2.80594 at (0.0, 0.0) arcsec Real: mean = -4.39147e-05 sum of squares = 2024.67 Imag: mean = 7.66026e-05 sum of squares = 2020.88 Gridding frequency index 1 lambda = 0.00186577 metres, scale = 0.00259846 radians per metre Gridding real part of frequency 1... Gridding imag part of frequency 1... Pattern is holo(res.pattern1) Weights in holo(obs.real,wt1) and holo(obs.imag,wt1) Maximum amplitude = 2.83743 at (0.0, 0.0) arcsec Real: mean = 0.000128098 sum of squares = 1956.66 Imag: mean = -0.00015572 sum of squares = 2107.47 Gridding frequency index 2 lambda = 0.00186573 metres, scale = 0.00259852 radians per metre Gridding real part of frequency 2... Gridding imag part of frequency 2... Pattern is holo(res.pattern2) Weights in holo(obs.real,wt2) and holo(obs.imag,wt2) Maximum amplitude = 2.88158 at (0.0, 0.0) arcsec Real: mean = 0.0002151 sum of squares = 2060.86 Imag: mean = -0.000175359 sum of squares = 2022.09 Gridding frequency index 3 lambda = 0.00186568 metres, scale = 0.00259859 radians per metre Gridding real part of frequency 3... Gridding imag part of frequency 3... Pattern is holo(res.pattern3) Weights in holo(obs.real,wt3) and holo(obs.imag,wt3) Maximum amplitude = 2.89528 at (0.0, 0.0) arcsec Real: mean = 0.000363834 sum of squares = 2116.76 Imag: mean = -0.000181394 sum of squares = 1990.34 Gridding frequency index 4 lambda = 0.00186563 metres, scale = 0.00259865 radians per metre Gridding real part of frequency 4... Gridding imag part of frequency 4... Pattern is holo(res.pattern4) Weights in holo(obs.real,wt4) and holo(obs.imag,wt4) Maximum amplitude = 2.87642 at (0.0, 0.0) arcsec Real: mean = 0.000462695 sum of squares = 2024.07 Imag: mean = -7.53287e-05 sum of squares = 2111.55 Gridding frequency index 5 lambda = 0.00186559 metres, scale = 0.00259872 radians per metre Gridding real part of frequency 5... Gridding imag part of frequency 5... Pattern is holo(res.pattern5) Weights in holo(obs.real,wt5) and holo(obs.imag,wt5) Maximum amplitude = 2.85041 at (0.0, 0.0) arcsec Real: mean = 0.000456382 sum of squares = 2028.76 Imag: mean = 0.000147727 sum of squares = 2138.73 Gridding frequency index 6 lambda = 0.00186554 metres, scale = 0.00259878 radians per metre Gridding real part of frequency 6... Gridding imag part of frequency 6... Pattern is holo(res.pattern6) Weights in holo(obs.real,wt6) and holo(obs.imag,wt6) Maximum amplitude = 2.82488 at (0.0, 0.0) arcsec Real: mean = 0.000202428 sum of squares = 2156.02 Imag: mean = 0.00019298 sum of squares = 2046.83 Gridding frequency index 7 lambda = 0.00186549 metres, scale = 0.00259885 radians per metre Gridding real part of frequency 7... Gridding imag part of frequency 7... Pattern is holo(res.pattern7) Weights in holo(obs.real,wt7) and holo(obs.imag,wt7) Maximum amplitude = 2.845 at (0.0, 0.0) arcsec Real: mean = 0.000137675 sum of squares = 2154.36 Imag: mean = 7.01391e-05 sum of squares = 2079.67 Gridding frequency index 8 lambda = 0.00186545 metres, scale = 0.00259891 radians per metre Gridding real part of frequency 8... Gridding imag part of frequency 8... Pattern is holo(res.pattern8) Weights in holo(obs.real,wt8) and holo(obs.imag,wt8) Maximum amplitude = 2.88482 at (0.0, 0.0) arcsec Real: mean = 0.000110531 sum of squares = 2057.04 Imag: mean = -1.98728e-05 sum of squares = 2208 Gridding frequency index 9 lambda = 0.0018654 metres, scale = 0.00259898 radians per metre Gridding real part of frequency 9... Gridding imag part of frequency 9... Pattern is holo(res.pattern9) Weights in holo(obs.real,wt9) and holo(obs.imag,wt9) Maximum amplitude = 2.9042 at (0.0, 0.0) arcsec Real: mean = 9.45546e-05 sum of squares = 2135.02 Imag: mean = -0.000150755 sum of squares = 2165.73 Gridding frequency index 10 lambda = 0.00186536 metres, scale = 0.00259904 radians per metre Gridding real part of frequency 10... Gridding imag part of frequency 10... Pattern is holo(res.pattern10) Weights in holo(obs.real,wt10) and holo(obs.imag,wt10) Maximum amplitude = 2.92644 at (0.0, 0.0) arcsec Real: mean = 0.000351851 sum of squares = 2244.65 Imag: mean = -0.000281706 sum of squares = 2095.53 Gridding frequency index 11 lambda = 0.00186531 metres, scale = 0.00259911 radians per metre Gridding real part of frequency 11... Gridding imag part of frequency 11... Pattern is holo(res.pattern11) Weights in holo(obs.real,wt11) and holo(obs.imag,wt11) Maximum amplitude = 2.92926 at (0.0, 0.0) arcsec Real: mean = 0.000529818 sum of squares = 2177.21 Imag: mean = -1.69233e-05 sum of squares = 2204.42 Gridding frequency index 12 lambda = 0.00186526 metres, scale = 0.00259917 radians per metre Gridding real part of frequency 12... Gridding imag part of frequency 12... Pattern is holo(res.pattern12) Weights in holo(obs.real,wt12) and holo(obs.imag,wt12) Maximum amplitude = 2.89966 at (0.0, 0.0) arcsec Real: mean = 0.000371829 sum of squares = 2136.48 Imag: mean = 8.77153e-05 sum of squares = 2287.29 Gridding frequency index 13 lambda = 0.00186522 metres, scale = 0.00259924 radians per metre Gridding real part of frequency 13... Gridding imag part of frequency 13... Pattern is holo(res.pattern13) Weights in holo(obs.real,wt13) and holo(obs.imag,wt13) Maximum amplitude = 2.89299 at (0.0, 0.0) arcsec Real: mean = 0.000394347 sum of squares = 2262.56 Imag: mean = 0.000140215 sum of squares = 2203.5 Gridding frequency index 14 lambda = 0.00186517 metres, scale = 0.0025993 radians per metre Gridding real part of frequency 14... Gridding imag part of frequency 14... Pattern is holo(res.pattern14) Weights in holo(obs.real,wt14) and holo(obs.imag,wt14) Maximum amplitude = 2.92362 at (0.0, 0.0) arcsec Real: mean = 0.000158732 sum of squares = 2322.01 Imag: mean = 0.000260583 sum of squares = 2193.34 Gridding frequency index 15 lambda = 0.00186512 metres, scale = 0.00259937 radians per metre Gridding real part of frequency 15... Gridding imag part of frequency 15... Pattern is holo(res.pattern15) Weights in holo(obs.real,wt15) and holo(obs.imag,wt15) Maximum amplitude = 2.96789 at (0.0, 0.0) arcsec Real: mean = 7.97977e-06 sum of squares = 2227.85 Imag: mean = -1.23355e-05 sum of squares = 2341.76 Masking frequency index 0 Mask scale size = 6.11808 Masking frequency index 1 Mask scale size = 6.11823 Masking frequency index 2 Mask scale size = 6.11838 Masking frequency index 3 Mask scale size = 6.11854 Masking frequency index 4 Mask scale size = 6.11869 Masking frequency index 5 Mask scale size = 6.11884 Masking frequency index 6 Mask scale size = 6.11899 Masking frequency index 7 Mask scale size = 6.11915 Masking frequency index 8 Mask scale size = 6.1193 Masking frequency index 9 Mask scale size = 6.11945 Masking frequency index 10 Mask scale size = 6.1196 Masking frequency index 11 Mask scale size = 6.11975 Masking frequency index 12 Mask scale size = 6.11991 Masking frequency index 13 Mask scale size = 6.12006 Masking frequency index 14 Mask scale size = 6.12021 Masking frequency index 15 Mask scale size = 6.12036 Checking phase lock voltage for frequency 0... Max point-to-point PLL voltage change: 0.180664 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 1... Max point-to-point PLL voltage change: 0.200195 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 2... Max point-to-point PLL voltage change: 0.19043 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 3... Max point-to-point PLL voltage change: 0.187988 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 4... Max point-to-point PLL voltage change: 0.19043 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 5... Max point-to-point PLL voltage change: 0.19043 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 6... Max point-to-point PLL voltage change: 0.19043 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 7... Max point-to-point PLL voltage change: 0.187988 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 8... Max point-to-point PLL voltage change: 0.202637 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 9... Max point-to-point PLL voltage change: 0.195312 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 10... Max point-to-point PLL voltage change: 0.192871 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 11... Max point-to-point PLL voltage change: 0.200195 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 12... Max point-to-point PLL voltage change: 0.185547 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 13... Max point-to-point PLL voltage change: 0.192871 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 14... Max point-to-point PLL voltage change: 0.212402 Median point-to-point PLL voltage change: 0.00976562 Checking phase lock voltage for frequency 15... Max point-to-point PLL voltage change: 0.192871 Median point-to-point PLL voltage change: 0.00976562 Doing FFT of patterns... Normalising FFT patterns... Freq 0: Shift, scale = 2.7879 126.2 Freq 1: Shift, scale = 1.9281 127.26 Freq 2: Shift, scale = 1.0653 128.09 Freq 3: Shift, scale = 0.19867 128.05 Freq 4: Shift, scale = -0.6667 128.35 Freq 5: Shift, scale = -1.5327 128.48 Freq 6: Shift, scale = -2.3932 128.79 Freq 7: Shift, scale = 3.0328 129.57 Freq 8: Shift, scale = 2.1695 130.6 Freq 9: Shift, scale = 1.3047 130.58 Freq 10: Shift, scale = 0.44447 131.42 Freq 11: Shift, scale = -0.42585 132.88 Freq 12: Shift, scale = -1.2965 132.9 Freq 13: Shift, scale = -2.159 132.52 Freq 14: Shift, scale = -3.0207 132.98 Freq 15: Shift, scale = 2.4016 133.78 Calculating phase corrections for index 0 Calculating phase corrections for index 1 Calculating phase corrections for index 2 Calculating phase corrections for index 3 Calculating phase corrections for index 4 Calculating phase corrections for index 5 Calculating phase corrections for index 6 Calculating phase corrections for index 7 Calculating phase corrections for index 8 Calculating phase corrections for index 9 Calculating phase corrections for index 10 Calculating phase corrections for index 11 Calculating phase corrections for index 12 Calculating phase corrections for index 13 Calculating phase corrections for index 14 Calculating phase corrections for index 15 Apply near field corrections for frequency 0 Apply secondary diffraction correction for frequency 0 Apply near field corrections for frequency 1 Apply secondary diffraction correction for frequency 1 Apply near field corrections for frequency 2 Apply secondary diffraction correction for frequency 2 Apply near field corrections for frequency 3 Apply secondary diffraction correction for frequency 3 Apply near field corrections for frequency 4 Apply secondary diffraction correction for frequency 4 Apply near field corrections for frequency 5 Apply secondary diffraction correction for frequency 5 Apply near field corrections for frequency 6 Apply secondary diffraction correction for frequency 6 Apply near field corrections for frequency 7 Apply secondary diffraction correction for frequency 7 Apply near field corrections for frequency 8 Apply secondary diffraction correction for frequency 8 Apply near field corrections for frequency 9 Apply secondary diffraction correction for frequency 9 Apply near field corrections for frequency 10 Apply secondary diffraction correction for frequency 10 Apply near field corrections for frequency 11 Apply secondary diffraction correction for frequency 11 Apply near field corrections for frequency 12 Apply secondary diffraction correction for frequency 12 Apply near field corrections for frequency 13 Apply secondary diffraction correction for frequency 13 Apply near field corrections for frequency 14 Apply secondary diffraction correction for frequency 14 Apply near field corrections for frequency 15 Apply secondary diffraction correction for frequency 15 Fitting piston, pointing and defocus terms Fitting frequency 0 Minimiser fit code = 1 piston: -0.226 radians x offset: -0.000212 arcsec y offset: -0.00141 arcsec defocus: -0.00212 mm Estimated x pointing error is -5.1 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.6 arcsec (used 10.6 arcsec) Estimated defocus error is 2.868 mm (used 2.87 mm) Fitting frequency 1 Minimiser fit code = 1 piston: -0.223 radians x offset: 0.00182 arcsec y offset: -0.0126 arcsec defocus: -0.00345 mm Estimated x pointing error is -5.098 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.59 arcsec (used 10.6 arcsec) Estimated defocus error is 2.867 mm (used 2.87 mm) Fitting frequency 2 Minimiser fit code = 1 piston: -0.223 radians x offset: 0.00317 arcsec y offset: -0.0144 arcsec defocus: -0.00453 mm Estimated x pointing error is -5.097 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.59 arcsec (used 10.6 arcsec) Estimated defocus error is 2.865 mm (used 2.87 mm) Fitting frequency 3 Minimiser fit code = 1 piston: -0.227 radians x offset: 0.00137 arcsec y offset: -0.00726 arcsec defocus: -0.00502 mm Estimated x pointing error is -5.099 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.59 arcsec (used 10.6 arcsec) Estimated defocus error is 2.865 mm (used 2.87 mm) Fitting frequency 4 Minimiser fit code = 3 piston: -0.228 radians x offset: -0.0118 arcsec y offset: 0.0117 arcsec defocus: -0.00395 mm Estimated x pointing error is -5.112 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.61 arcsec (used 10.6 arcsec) Estimated defocus error is 2.866 mm (used 2.87 mm) Fitting frequency 5 Minimiser fit code = 1 piston: -0.229 radians x offset: -0.0235 arcsec y offset: 0.0141 arcsec defocus: -0.00271 mm Estimated x pointing error is -5.123 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.61 arcsec (used 10.6 arcsec) Estimated defocus error is 2.867 mm (used 2.87 mm) Fitting frequency 6 Minimiser fit code = 1 piston: -0.228 radians x offset: -0.0307 arcsec y offset: -0.00485 arcsec defocus: -0.00534 mm Estimated x pointing error is -5.131 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.6 arcsec (used 10.6 arcsec) Estimated defocus error is 2.865 mm (used 2.87 mm) Fitting frequency 7 Minimiser fit code = 3 piston: -0.223 radians x offset: -0.0238 arcsec y offset: -0.0186 arcsec defocus: -0.0065 mm Estimated x pointing error is -5.124 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.58 arcsec (used 10.6 arcsec) Estimated defocus error is 2.863 mm (used 2.87 mm) Fitting frequency 8 Minimiser fit code = 3 piston: -0.224 radians x offset: -0.0305 arcsec y offset: -0.0129 arcsec defocus: -0.0069 mm Estimated x pointing error is -5.13 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.59 arcsec (used 10.6 arcsec) Estimated defocus error is 2.863 mm (used 2.87 mm) Fitting frequency 9 Minimiser fit code = 3 piston: -0.225 radians x offset: -0.0427 arcsec y offset: -0.0107 arcsec defocus: -0.00612 mm Estimated x pointing error is -5.143 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.59 arcsec (used 10.6 arcsec) Estimated defocus error is 2.864 mm (used 2.87 mm) Fitting frequency 10 Minimiser fit code = 1 piston: -0.221 radians x offset: -0.0542 arcsec y offset: -0.00427 arcsec defocus: -0.00616 mm Estimated x pointing error is -5.154 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.6 arcsec (used 10.6 arcsec) Estimated defocus error is 2.864 mm (used 2.87 mm) Fitting frequency 11 Minimiser fit code = 3 piston: -0.229 radians x offset: -0.0618 arcsec y offset: -0.0274 arcsec defocus: -0.00784 mm Estimated x pointing error is -5.162 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.57 arcsec (used 10.6 arcsec) Estimated defocus error is 2.862 mm (used 2.87 mm) Fitting frequency 12 Minimiser fit code = 1 piston: -0.236 radians x offset: -0.059 arcsec y offset: -0.0535 arcsec defocus: -0.00858 mm Estimated x pointing error is -5.159 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.55 arcsec (used 10.6 arcsec) Estimated defocus error is 2.861 mm (used 2.87 mm) Fitting frequency 13 Minimiser fit code = 3 piston: -0.234 radians x offset: -0.0671 arcsec y offset: -0.0489 arcsec defocus: -0.00851 mm Estimated x pointing error is -5.167 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.55 arcsec (used 10.6 arcsec) Estimated defocus error is 2.861 mm (used 2.87 mm) Fitting frequency 14 Minimiser fit code = 3 piston: -0.23 radians x offset: -0.0852 arcsec y offset: -0.0524 arcsec defocus: -0.00764 mm Estimated x pointing error is -5.185 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.55 arcsec (used 10.6 arcsec) Estimated defocus error is 2.862 mm (used 2.87 mm) Fitting frequency 15 Minimiser fit code = 3 piston: -0.226 radians x offset: -0.0914 arcsec y offset: -0.0568 arcsec defocus: -0.00856 mm Estimated x pointing error is -5.191 arcsec (used -5.1 arcsec) Estimated y pointing error is 10.54 arcsec (used 10.6 arcsec) Estimated defocus error is 2.861 mm (used 2.87 mm) Making masked surfaces in microns, and cubes Fitting Zernikes Fitting many Zernikes for frequency 0 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00180 piston 1 1 -0.00293 tilt_x 1 -1 0.01616 tilt_y 2 2 -0.04064 astigmatism_0 2 0 -0.00270 curvature 2 -2 -0.03407 astigmatism45 3 3 -0.04215 trefoil_0 3 1 -0.00950 coma_x 3 -1 0.06092 coma_y 3 -3 -0.00343 trefoil_30 Fitting many Zernikes for frequency 1 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00187 piston 1 1 -0.00345 tilt_x 1 -1 0.01563 tilt_y 2 2 -0.04249 astigmatism_0 2 0 -0.00275 curvature 2 -2 -0.03439 astigmatism45 3 3 -0.04124 trefoil_0 3 1 -0.01145 coma_x 3 -1 0.05950 coma_y 3 -3 -0.00341 trefoil_30 Fitting many Zernikes for frequency 2 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00191 piston 1 1 -0.00371 tilt_x 1 -1 0.01559 tilt_y 2 2 -0.04312 astigmatism_0 2 0 -0.00296 curvature 2 -2 -0.03380 astigmatism45 3 3 -0.03872 trefoil_0 3 1 -0.01294 coma_x 3 -1 0.05975 coma_y 3 -3 -0.00428 trefoil_30 Fitting many Zernikes for frequency 3 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00189 piston 1 1 -0.00334 tilt_x 1 -1 0.01578 tilt_y 2 2 -0.04292 astigmatism_0 2 0 -0.00294 curvature 2 -2 -0.03182 astigmatism45 3 3 -0.03881 trefoil_0 3 1 -0.01181 coma_x 3 -1 0.06029 coma_y 3 -3 -0.00440 trefoil_30 Fitting many Zernikes for frequency 4 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00184 piston 1 1 -0.00296 tilt_x 1 -1 0.01634 tilt_y 2 2 -0.04051 astigmatism_0 2 0 -0.00289 curvature 2 -2 -0.03363 astigmatism45 3 3 -0.04129 trefoil_0 3 1 -0.01014 coma_x 3 -1 0.06194 coma_y 3 -3 -0.00666 trefoil_30 Fitting many Zernikes for frequency 5 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00180 piston 1 1 -0.00266 tilt_x 1 -1 0.01670 tilt_y 2 2 -0.04123 astigmatism_0 2 0 -0.00275 curvature 2 -2 -0.03423 astigmatism45 3 3 -0.04142 trefoil_0 3 1 -0.00920 coma_x 3 -1 0.06384 coma_y 3 -3 -0.00692 trefoil_30 Fitting many Zernikes for frequency 6 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00182 piston 1 1 -0.00254 tilt_x 1 -1 0.01606 tilt_y 2 2 -0.04381 astigmatism_0 2 0 -0.00280 curvature 2 -2 -0.03264 astigmatism45 3 3 -0.03931 trefoil_0 3 1 -0.00906 coma_x 3 -1 0.06185 coma_y 3 -3 -0.00578 trefoil_30 Fitting many Zernikes for frequency 7 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00183 piston 1 1 -0.00215 tilt_x 1 -1 0.01585 tilt_y 2 2 -0.04407 astigmatism_0 2 0 -0.00294 curvature 2 -2 -0.03086 astigmatism45 3 3 -0.03812 trefoil_0 3 1 -0.00795 coma_x 3 -1 0.06088 coma_y 3 -3 -0.00619 trefoil_30 Fitting many Zernikes for frequency 8 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00179 piston 1 1 -0.00220 tilt_x 1 -1 0.01579 tilt_y 2 2 -0.04126 astigmatism_0 2 0 -0.00292 curvature 2 -2 -0.03102 astigmatism45 3 3 -0.03793 trefoil_0 3 1 -0.00810 coma_x 3 -1 0.06002 coma_y 3 -3 -0.00652 trefoil_30 Fitting many Zernikes for frequency 9 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00177 piston 1 1 -0.00219 tilt_x 1 -1 0.01609 tilt_y 2 2 -0.04050 astigmatism_0 2 0 -0.00285 curvature 2 -2 -0.03237 astigmatism45 3 3 -0.03896 trefoil_0 3 1 -0.00814 coma_x 3 -1 0.06105 coma_y 3 -3 -0.00658 trefoil_30 Fitting many Zernikes for frequency 10 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00177 piston 1 1 -0.00193 tilt_x 1 -1 0.01562 tilt_y 2 2 -0.04037 astigmatism_0 2 0 -0.00280 curvature 2 -2 -0.03468 astigmatism45 3 3 -0.04121 trefoil_0 3 1 -0.00718 coma_x 3 -1 0.05945 coma_y 3 -3 -0.00736 trefoil_30 Fitting many Zernikes for frequency 11 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00181 piston 1 1 -0.00136 tilt_x 1 -1 0.01555 tilt_y 2 2 -0.04186 astigmatism_0 2 0 -0.00290 curvature 2 -2 -0.03397 astigmatism45 3 3 -0.03907 trefoil_0 3 1 -0.00585 coma_x 3 -1 0.05958 coma_y 3 -3 -0.00612 trefoil_30 Fitting many Zernikes for frequency 12 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00190 piston 1 1 -0.00132 tilt_x 1 -1 0.01561 tilt_y 2 2 -0.04410 astigmatism_0 2 0 -0.00308 curvature 2 -2 -0.03167 astigmatism45 3 3 -0.03545 trefoil_0 3 1 -0.00628 coma_x 3 -1 0.06028 coma_y 3 -3 -0.00510 trefoil_30 Fitting many Zernikes for frequency 13 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00184 piston 1 1 -0.00127 tilt_x 1 -1 0.01573 tilt_y 2 2 -0.04294 astigmatism_0 2 0 -0.00308 curvature 2 -2 -0.03070 astigmatism45 3 3 -0.03553 trefoil_0 3 1 -0.00603 coma_x 3 -1 0.06077 coma_y 3 -3 -0.00661 trefoil_30 Fitting many Zernikes for frequency 14 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00179 piston 1 1 -0.00138 tilt_x 1 -1 0.01569 tilt_y 2 2 -0.04195 astigmatism_0 2 0 -0.00288 curvature 2 -2 -0.03139 astigmatism45 3 3 -0.03739 trefoil_0 3 1 -0.00599 coma_x 3 -1 0.06061 coma_y 3 -3 -0.00828 trefoil_30 Fitting many Zernikes for frequency 15 Using terms up to Zernike order 3 Fitting 10 Zernike terms: 0 1 2 3 4 5 6 7 8 9 Minimiser fit code = 1 n l coeff name 0 0 -0.00180 piston 1 1 -0.00137 tilt_x 1 -1 0.01530 tilt_y 2 2 -0.04289 astigmatism_0 2 0 -0.00291 curvature 2 -2 -0.03291 astigmatism45 3 3 -0.03875 trefoil_0 3 1 -0.00606 coma_x 3 -1 0.05931 coma_y 3 -3 -0.00763 trefoil_30 Averaged 16 maps to make holo(res.mean_surface) Computing differences from mean surface (phase) Unweighted rms analysis, frequency 0 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.2 20.9 19.3 22.7 23.1 22.8 30.1 24.4 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23 20.6 18.7 22.6 23.2 22.5 28.9 23.9 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.89 3.29 4.12 4.27 3.99 4.59 7.55 5.12 Unweighted rms analysis, frequency 1 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.8 20.9 19.4 23.2 23 22.8 30.2 24.5 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.5 20.7 18.8 23.1 23.1 22.5 29.1 24 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.86 3.24 4.06 4.23 4 4.66 7.57 5.13 Unweighted rms analysis, frequency 2 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.8 20.7 19 23 23.2 22.9 30 24.4 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.6 20.5 18.3 22.8 23.3 22.6 28.9 24 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.87 3.26 4.1 4.25 3.99 4.59 7.5 5.1 Unweighted rms analysis, frequency 3 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.7 20.8 19.2 22.5 23.3 23 29.9 24.4 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.4 20.6 18.5 22.3 23.4 22.7 28.7 23.9 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.88 3.28 4.11 4.25 3.95 4.52 7.43 5.06 Unweighted rms analysis, frequency 4 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.4 21 19.3 22.1 23.1 22.9 29.9 24.3 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.2 20.7 18.7 21.9 23.2 22.5 28.7 23.8 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.92 3.35 4.19 4.32 4 4.56 7.55 5.14 Unweighted rms analysis, frequency 5 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.4 20.8 19.7 21.9 23 22.8 30.1 24.3 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.1 20.5 19 21.7 23.2 22.5 28.8 23.8 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.98 3.44 4.3 4.43 4.08 4.62 7.69 5.24 Unweighted rms analysis, frequency 6 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.7 20.6 19.9 22 23 22.7 30.3 24.4 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.5 20.4 19.2 21.9 23.1 22.4 29.1 23.9 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.92 3.34 4.18 4.33 4.03 4.61 7.58 5.16 Unweighted rms analysis, frequency 7 Total errors: ring: 1 2 3 4 5 6 7 total rms: 24.3 21.2 19.4 22.4 23 22.6 30.1 24.3 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 24 21 18.7 22.3 23.1 22.4 28.9 23.9 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.88 3.28 4.11 4.25 3.96 4.52 7.44 5.06 Unweighted rms analysis, frequency 8 Total errors: ring: 1 2 3 4 5 6 7 total rms: 24.4 21.1 19.2 23 23.2 22.8 29.9 24.4 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 24.1 20.9 18.6 22.9 23.3 22.5 28.7 24 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.86 3.23 4.04 4.17 3.86 4.39 7.26 4.95 Unweighted rms analysis, frequency 9 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.7 21 19.5 22.9 22.9 22.9 30 24.4 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.4 20.7 18.9 22.8 23.1 22.6 28.8 23.9 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.89 3.28 4.11 4.24 3.91 4.44 7.36 5.01 Unweighted rms analysis, frequency 10 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.2 20.8 19.2 22.3 22.9 23 30.1 24.3 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 22.9 20.5 18.5 22.1 23.1 22.7 28.9 23.8 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.84 3.2 4.01 4.17 3.94 4.58 7.47 5.06 Unweighted rms analysis, frequency 11 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.2 20.9 19.8 21.9 23 22.9 30.2 24.4 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 22.9 20.6 19.2 21.8 23.2 22.7 29 23.9 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.84 3.2 4.01 4.17 3.93 4.55 7.42 5.04 Unweighted rms analysis, frequency 12 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.7 20.8 20 21.9 23.1 22.8 30.1 24.3 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.4 20.5 19.3 21.7 23.2 22.6 28.8 23.9 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.86 3.24 4.06 4.2 3.92 4.47 7.32 5 Unweighted rms analysis, frequency 13 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.5 20.7 19.2 22.1 23.2 22.9 30.1 24.3 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23.3 20.4 18.5 21.9 23.3 22.6 28.8 23.8 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.87 3.26 4.08 4.21 3.88 4.39 7.26 4.96 Unweighted rms analysis, frequency 14 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.2 20.9 19 22.3 23 22.8 30.2 24.3 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 23 20.7 18.4 22.2 23.2 22.6 28.9 23.8 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.87 3.25 4.07 4.2 3.89 4.43 7.32 4.98 Unweighted rms analysis, frequency 15 Total errors: ring: 1 2 3 4 5 6 7 total rms: 23.1 21 19.4 22.8 22.9 22.9 30.3 24.4 Small-scale errors: ring: 1 2 3 4 5 6 7 total rms: 22.8 20.7 18.8 22.7 23 22.7 29.1 24 Large-scale errors: ring: 1 2 3 4 5 6 7 total rms: 1.83 3.18 4 4.16 3.93 4.56 7.42 5.03 Total errors on mean aperture: ring: 1 2 3 4 5 6 7 total rms: 22.5 19.9 18.1 21.1 22.2 21.8 29.1 23.3 Mean deviation is -0.25414617541757967 microns Taper = 10 dB, Ruze illumination-weighted rms = 22.8 micron Estimating beam: f = 650GHz Taper = 12dB defocus = 0mm Sigma = 4.51193 (Taper = 12 dB) Added 0.0 of Zernike 4 0 (name=spherical_aberration, index = 12) Added 0.0 of Zernike 3 3 (name=trefoil_0, index = 6) f = 650 GHz Ruze rms = 20.2 micron Centre pixel: 128.0 128.0 Value = 13476.1 (estimate), 15687.6 (perfect) Strehl = 0.737932 Strehl ratio estimate = 0.7379 Estimating beam: f = 900GHz Taper = 12dB defocus = 0mm Sigma = 4.51193 (Taper = 12 dB) Added 0.0 of Zernike 4 0 (name=spherical_aberration, index = 12) Added 0.0 of Zernike 3 3 (name=trefoil_0, index = 6) f = 900 GHz Ruze rms = 19.6 micron Centre pixel: 128.0 128.0 Value = 11943.1 (estimate), 15687.6 (perfect) Strehl = 0.579587 Strehl ratio estimate = 0.5796 Fitting panels... No Zernike terms to subtract before panel fitting edge scale = 0.07514 metres panel scale = 3.00000 metres mean frequency = 160.70600 GHz min edge weight = 0.1 # rng pan adj1 adj2 adj3 qsum 1 1 1 5.0 7.0 1.9 8.8 2 1 2 6.4 11.7 9.1 16.2 3 1 3 4.1 9.8 -0.1 10.7 4 1 4 2.6 12.4 4.0 13.3 5 1 5 -11.6 2.1 7.3 13.9 6 1 6 -8.6 5.0 -5.7 11.5 7 1 7 2.8 -4.6 4.6 7.1 8 1 8 -10.2 3.5 4.3 11.7 9 1 9 -83.3 -5.2 9.5 84.0 10 1 10 4.6 7.3 13.4 15.9 11 1 11 13.7 5.4 -5.8 15.8 12 1 12 4.5 2.1 6.4 8.1 13 2 1 12.6 5.3 9.8 16.8 14 2 2 10.4 4.1 3.3 11.7 15 2 3 6.4 2.3 -2.6 7.2 16 2 4 9.1 -2.1 7.6 12.0 17 2 5 9.7 14.3 3.6 17.6 18 2 6 11.5 11.5 5.2 17.1 19 2 7 -0.5 6.2 6.8 9.2 20 2 8 5.0 18.6 7.4 20.7 21 2 9 5.9 -1.9 3.7 7.3 22 2 10 13.8 2.2 -2.3 14.2 23 2 11 2.6 -7.0 -9.8 12.3 24 2 12 -1.7 -7.1 -20.4 21.7 25 2 13 -3.4 -10.9 -19.9 22.9 26 2 14 -5.4 -28.7 -14.2 32.4 27 2 15 8.2 -20.2 -14.3 26.1 28 2 16 12.4 -4.5 4.8 14.1 29 2 17 3.8 8.9 -1.9 9.9 30 2 18 5.7 3.1 -9.0 11.1 31 2 19 3.8 1.9 8.1 9.1 32 2 20 13.8 4.2 -3.2 14.8 33 2 21 -0.1 -6.4 5.4 8.4 34 2 22 10.7 -1.4 -8.9 14.0 35 2 23 10.3 -3.5 2.6 11.2 36 2 24 8.4 -5.1 -0.3 9.9 37 3 1 16.4 10.0 1.1 19.3 38 3 2 15.9 13.9 4.2 21.5 39 3 3 18.1 14.0 -3.0 23.0 40 3 4 22.0 15.6 6.4 27.7 41 3 5 16.6 16.7 1.9 23.7 42 3 6 4.7 12.4 14.5 19.6 43 3 7 0.4 10.9 5.3 12.2 44 3 8 7.0 13.7 1.3 15.4 45 3 9 12.4 14.4 -5.1 19.7 46 3 10 10.1 6.4 -0.0 11.9 47 3 11 7.9 7.5 -9.6 14.5 48 3 12 10.4 9.4 -6.6 15.5 49 3 13 9.1 3.7 2.1 10.0 50 3 14 23.2 6.7 -1.2 24.2 51 3 15 6.2 3.2 -0.9 7.0 52 3 16 13.8 7.4 0.6 15.6 53 3 17 7.6 7.0 -5.9 11.9 54 3 18 1.6 -1.1 3.9 4.4 55 3 19 2.3 -1.5 -3.8 4.7 56 3 20 -0.0 2.4 -0.6 2.5 57 3 21 -5.1 0.6 -2.4 5.7 58 3 22 -1.1 2.0 2.3 3.3 59 3 23 -11.3 -1.6 -3.8 12.0 60 3 24 -5.2 -7.4 -1.5 9.2 61 3 25 -13.3 -6.6 -16.2 21.9 62 3 26 -26.4 -12.0 -2.2 29.1 63 3 27 -19.6 -13.0 -12.3 26.5 64 3 28 -4.4 -3.2 -14.2 15.2 65 3 29 -8.9 -4.1 -10.4 14.3 66 3 30 -7.0 -30.0 -5.0 31.2 67 3 31 -5.5 -6.9 -7.3 11.5 68 3 32 6.9 7.1 -16.7 19.4 69 3 33 15.7 9.9 7.5 20.0 70 3 34 4.3 -1.0 8.4 9.5 71 3 35 4.9 2.3 -3.7 6.6 72 3 36 3.4 5.6 2.4 7.0 73 3 37 10.8 8.2 -10.9 17.4 74 3 38 2.3 0.6 -12.5 12.7 75 3 39 -6.5 3.3 -13.5 15.3 76 3 40 11.3 9.2 -7.4 16.3 77 3 41 2.5 1.0 -1.2 2.9 78 3 42 -5.6 9.7 6.1 12.7 79 3 43 1.5 6.6 11.3 13.2 80 3 44 12.9 8.8 9.1 18.0 81 3 45 13.5 10.8 -0.7 17.3 82 3 46 15.2 6.3 -0.2 16.4 83 3 47 10.1 7.7 -4.1 13.4 84 3 48 17.6 11.4 -6.0 21.9 85 4 1 2.9 7.7 11.4 14.1 86 4 2 7.5 12.9 1.1 15.0 87 4 3 10.1 12.8 12.1 20.3 88 4 4 5.7 11.4 5.0 13.7 89 4 5 4.9 9.3 2.5 10.9 90 4 6 4.3 3.8 -8.2 10.0 91 4 7 11.9 -0.2 -11.0 16.2 92 4 8 9.9 3.3 -15.3 18.5 93 4 9 3.9 2.0 -15.6 16.2 94 4 10 -12.4 0.6 -16.7 20.8 95 4 11 -5.8 -2.8 -19.6 20.7 96 4 12 -10.0 -0.9 -14.3 17.4 97 4 13 -1.7 -0.5 -9.2 9.3 98 4 14 5.5 -7.2 -15.3 17.7 99 4 15 -6.3 -4.1 -15.5 17.2 100 4 16 -6.9 -3.6 -8.7 11.7 101 4 17 -0.9 -2.1 -17.0 17.2 102 4 18 -3.0 -2.6 1.0 4.1 103 4 19 -5.0 -2.7 -6.0 8.3 104 4 20 5.4 7.3 -4.6 10.2 105 4 21 4.3 4.4 13.4 14.7 106 4 22 -13.6 8.8 16.7 23.2 107 4 23 -9.2 9.4 22.8 26.3 108 4 24 -2.0 8.2 20.6 22.3 109 4 25 -12.9 13.3 55.6 58.6 110 4 26 -19.7 2.2 18.9 27.3 111 4 27 -12.6 -3.3 11.9 17.6 112 4 28 -1.9 -0.4 8.7 8.9 113 4 29 -10.2 7.5 -49.8 51.4 114 4 30 -8.0 -5.4 3.5 10.2 115 4 31 -5.9 -6.3 -8.0 11.8 116 4 32 -8.8 -5.6 -6.9 12.5 117 4 33 -6.2 -6.1 -5.1 10.1 118 4 34 3.1 1.3 -9.7 10.3 119 4 35 -16.8 1.5 -18.9 25.4 120 4 36 0.5 -0.1 -7.3 7.3 121 4 37 3.1 -7.3 -10.3 13.0 122 4 38 -1.8 -8.8 -4.6 10.1 123 4 39 -3.5 -3.9 -7.8 9.4 124 4 40 2.1 -1.5 -8.7 9.1 125 4 41 4.0 5.8 5.3 8.9 126 4 42 12.0 0.6 -9.8 15.5 127 4 43 7.5 -0.5 -14.6 16.5 128 4 44 3.0 5.5 -7.9 10.0 129 4 45 5.5 2.8 -2.8 6.8 130 4 46 7.9 3.6 -7.3 11.4 131 4 47 3.1 2.8 0.1 4.1 132 4 48 -0.6 6.1 -1.8 6.4 133 5 1 5.3 1.6 -0.4 5.5 134 5 2 10.1 6.0 0.6 11.8 135 5 3 5.6 7.5 9.5 13.4 136 5 4 6.7 12.4 2.8 14.3 137 5 5 6.1 9.7 8.9 14.5 138 5 6 11.7 4.4 1.5 12.6 139 5 7 -9.9 9.7 0.8 13.9 140 5 8 -10.7 -6.6 -8.8 15.3 141 5 9 -8.6 -12.6 -14.1 20.8 142 5 10 -9.6 -8.9 -17.5 21.9 143 5 11 -6.1 -12.9 -14.5 20.3 144 5 12 -9.8 -3.4 -16.3 19.3 145 5 13 -11.7 -4.1 -18.1 22.0 146 5 14 -14.0 -10.0 -21.0 27.2 147 5 15 -9.0 -12.2 -22.1 26.8 148 5 16 -11.9 -6.2 -18.0 22.5 149 5 17 -3.1 -0.5 2.4 3.9 150 5 18 -3.3 -0.7 227.3 227.3 151 5 19 -9.5 -0.7 -7.8 12.3 152 5 20 7.7 2.2 -16.0 17.9 153 5 21 13.8 4.5 -9.8 17.5 154 5 22 4.0 0.7 -5.7 7.0 155 5 23 -5.4 -3.5 -7.8 10.1 156 5 24 -0.1 -7.9 -13.2 15.4 157 5 25 -0.1 -6.9 -16.8 18.1 158 5 26 8.1 1.0 -5.4 9.8 159 5 27 15.0 -4.8 -18.4 24.2 160 5 28 14.1 10.2 -10.3 20.3 161 5 29 25.7 8.3 1.3 27.0 162 5 30 13.1 9.1 20.9 26.3 163 5 31 -1.1 12.0 13.8 18.3 164 5 32 -7.5 -1.5 12.3 14.5 165 5 33 -9.6 1.3 -4.6 10.7 166 5 34 -6.1 -2.3 -9.0 11.1 167 5 35 3.6 -0.8 -9.4 10.1 168 5 36 -2.6 0.1 -17.5 17.7 169 5 37 -1.5 -10.2 1.9 10.5 170 5 38 -0.1 -17.4 -6.4 18.5 171 5 39 0.6 0.8 2.9 3.1 172 5 40 -8.1 4.4 0.5 9.2 173 5 41 -2.5 0.8 -4.4 5.2 174 5 42 -2.8 -1.4 -2.1 3.8 175 5 43 -0.5 -1.5 -14.2 14.2 176 5 44 -4.5 -2.6 -10.2 11.4 177 5 45 -4.0 -3.5 -11.5 12.7 178 5 46 -6.0 -4.1 -11.5 13.7 179 5 47 6.5 -7.9 -18.4 21.1 180 5 48 -6.6 2.8 -13.4 15.2 181 6 1 13.5 -4.5 -14.8 20.6 182 6 2 11.5 -1.3 -10.7 15.8 183 6 3 16.6 1.7 -2.4 16.9 184 6 4 10.5 11.1 -9.8 18.1 185 6 5 15.1 10.7 22.9 29.4 186 6 6 14.7 13.0 13.8 24.0 187 6 7 -0.8 6.6 10.2 12.2 188 6 8 -2.5 -5.5 16.8 17.9 189 6 9 -14.6 -6.0 -11.7 19.6 190 6 10 -16.9 -8.7 -10.9 21.9 191 6 11 -19.3 -7.3 -18.7 27.8 192 6 12 -8.8 -10.7 -18.5 23.1 193 6 13 -16.2 -17.1 -23.1 33.0 194 6 14 -22.7 -13.4 -13.1 29.4 195 6 15 -20.2 -17.0 -12.2 29.1 196 6 16 -2.6 -1.8 -12.1 12.5 197 6 17 -12.5 -6.8 -12.2 18.7 198 6 18 -8.0 -4.9 8.6 12.7 199 6 19 -7.9 -9.3 -8.5 14.8 200 6 20 -1.7 -8.2 -12.4 15.0 201 6 21 -9.0 -4.2 -4.2 10.7 202 6 22 -15.7 -11.7 -0.1 19.6 203 6 23 -16.6 -4.7 4.5 17.8 204 6 24 -22.0 1.7 6.7 23.1 205 6 25 -22.1 -0.3 -7.0 23.2 206 6 26 -21.9 -10.4 1.6 24.3 207 6 27 -11.4 -10.4 -1.4 15.5 208 6 28 -11.0 -1.3 -5.8 12.5 209 6 29 -0.4 12.7 -12.4 17.8 210 6 30 8.6 17.1 49.4 53.0 211 6 31 22.9 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12 47 0 12 48 1 12 49 -1 12 50 2 12 51 3 12 52 0 12 53 -1 12 54 2 12 55 0 12 56 1 12 57 4 12 58 0 12 59 1 12 60 1 12 61 0 12 62 3 12 63 4 12 64 1 12 65 3 12 66 0 12 67 -1 12 68 3 12 69 5 Adjuster movements: rms = 17.9 micron Looking for bad motors No bad motor file specified Finished panel fit Evaluating simulated dish from adjuster moves Reduction ended at: 20040909-101647 Creating HTML output file of plots Plotting summary text Saving results to disk - level = 1