diff --git a/test_data/IPTS-34735/exp813/GY_scan_log.rtf b/test_data/IPTS-34735/exp813/GY_scan_log.rtf deleted file mode 100644 index 7267d2a9..00000000 Binary files a/test_data/IPTS-34735/exp813/GY_scan_log.rtf and /dev/null differ diff --git a/test_data/IPTS-34735/exp813/GY_scan_log.txt b/test_data/IPTS-34735/exp813/GY_scan_log.txt deleted file mode 100644 index b555e445..00000000 --- a/test_data/IPTS-34735/exp813/GY_scan_log.txt +++ /dev/null @@ -1,51 +0,0 @@ -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.05/0.95, mcu = 60" -scan h 1.05 k 0.0 l 0.95 e 16 0.5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.1/0.9, mcu = 60" -scan h 1.1 k 0.0 l 0.9 e 25 4 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.15/0.85, mcu = 60" -scan h 1.15 k 0.0 l 0.85 e 32 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.2/0.8, mcu = 60" -scan h 1.2 k 0.0 l 0.8 e 35 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.25/0.75, mcu = 90, E = 35 to 25 mev" -scan h 1.25 k 0.0 l 0.75 e 35 25 0.25 ef 14.7 preset mcu 90 -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.25/0.75, mcu = 60, E = 25 to 5 mev" -scan h 1.25 k 0.0 l 0.75 e 24.75 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.3/0.7, mcu = 120, E = 37.6 to 25 mev" -scan h 1.3 k 0.0 l 0.7 e 37.6 25 0.2 ef 14.7 preset mcu 120 -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.3/0.7, mcu = 60, E = 25 to 5 mev" -scan h 1.3 k 0.0 l 0.7 e 24.75 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.325/0.675, mcu = 120, E = 38.4 to 25 mev" -scan h 1.325 k 0.0 l 0.675 e 38.4 25 0.2 ef 14.7 preset mcu 120 -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.325/0.675, mcu = 60, E = 25 to 5 mev" -scan h 1.325 k 0.0 l 0.675 e 24.75 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.35/0.65, mcu = 120, E = 39.2 to 25 mev" -scan h 1.35 k 0.0 l 0.65 e 39.2 25 0.2 ef 14.7 preset mcu 120 -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.35/0.65, mcu = 60, E = 25 to 5 mev" -scan h 1.35 k 0.0 l 0.65 e 24.75 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.375/0.625, mcu = 120, E = 40 to 30 mev" -scan h 1.375 k 0.0 l 0.625 e 40 30 0.2 ef 14.7 preset mcu 120 -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.375/0.625, mcu = 60, E = 30 to 5 mev" -scan h 1.375 k 0.0 l 1.625 e 29.75 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.4/0.6, mcu = 120, E = 40 to 30 mev" -scan h 1.4 k 0.0 l 0.6 e 40 30 0.2 ef 14.7 preset mcu 120 -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.4/0.6, mcu = 60, E = 30 to 5 mev" -scan h 1.4 k 0.0 l 0.6 e 29.75 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.45/0.55, mcu = 120, E = 40 to 33 mev" -scan h 1.45 k 0.0 l 0.55 e 40 33 0.2 ef 14.7 preset mcu 120 -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.45/0.55, mcu = 60, E = 30 to 5 mev" -scan h 1.45 k 0.0 l 0.55 e 32.75 5 0.25 ef 14.7 preset mcu 60 - -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.5/0.5, mcu = 120, E = 40 to 33 mev" -scan h 1.5 k 0.0 l 0.5 e 40 33 0.2 ef 14.7 preset mcu 120 -scantitle "-H0L scans along (101) to (1.5, 0, 0.5) at H/L = 1.5/0.5, mcu = 60, E = 30 to 0.5 mev" -scan h 1.5 k 0.0 l 0.5 e 32.75 0.5 0.25 ef 14.7 preset mcu 60 diff --git a/test_data/IPTS-34735/exp813/Shared/H0L_1.0.txt b/test_data/IPTS-34735/exp813/Shared/H0L_1.0.txt deleted file mode 100644 index 4c602343..00000000 --- a/test_data/IPTS-34735/exp813/Shared/H0L_1.0.txt +++ /dev/null @@ -1,59 +0,0 @@ -# Group 1 -# x y yerr ModelFit Func1 Func2 Func3 - 1.50010 22.03035 4.69412 20.05805 2.74515 1.98705 15.32585 - 1.75010 22.00783 4.69078 20.68257 3.20000 2.15481 15.32776 - 2.00010 22.96751 4.79198 21.44470 3.77145 2.34358 15.32968 - 2.25010 21.09227 4.59306 22.38824 4.49987 2.55677 15.33159 - 2.50010 24.80215 4.97973 23.57460 5.44262 2.79848 15.33351 - 2.75010 19.25565 4.38977 25.09034 6.68137 3.07356 15.33542 - 3.00010 24.05293 4.90436 27.05618 8.33102 3.38783 15.33733 - 3.25010 29.74768 5.45265 29.63322 10.54574 3.74823 15.33925 - 3.50010 28.73597 5.36192 33.00947 13.50529 4.16302 15.34116 - 3.75010 44.55339 6.67361 37.31800 17.33299 4.64193 15.34308 - 4.00010 49.78206 7.05490 42.38958 21.84828 5.19630 15.34499 - 4.25010 49.57600 7.04038 47.31246 26.12652 5.83904 15.34691 - 4.50010 42.53185 6.52226 50.33513 28.40205 6.58426 15.34882 - 4.75010 44.73954 6.68995 50.14155 27.34443 7.44639 15.35074 - 5.00010 56.56459 7.52097 47.39765 23.60681 8.43819 15.35265 - 5.25010 48.95955 6.99650 43.94069 19.01894 9.56719 15.35456 - 5.50010 40.76747 6.38489 41.06137 14.87519 10.82969 15.35648 - 5.75010 41.07940 6.40932 39.14781 11.58732 12.20210 15.35839 - 6.00010 32.77072 5.72453 38.09745 9.10707 13.63007 15.36031 - 6.25010 32.68435 5.71674 37.64178 7.26023 15.01933 15.36222 - 6.50010 39.58706 6.29323 37.47844 5.87900 16.23530 15.36414 - 6.75010 42.82616 6.54307 37.32084 4.83359 17.12119 15.36605 - 7.00010 42.54368 6.52102 36.93635 4.03059 17.53779 15.36797 - 7.25010 31.85117 5.64547 36.18607 3.40428 17.41191 15.36988 - 7.50010 33.87292 5.81924 35.04648 2.90849 16.76620 15.37180 - 7.75010 31.03170 5.56982 33.59385 2.51052 15.70961 15.37371 - 8.00010 39.48586 6.28472 31.95804 2.18697 14.39544 15.37562 - 8.25010 32.42125 5.69413 30.27236 1.92081 12.97401 15.37754 - 8.50010 26.92595 5.18909 28.64133 1.69953 11.56236 15.37945 - 8.75010 27.76301 5.26959 27.13078 1.51374 10.23567 15.38137 - 9.00010 26.24978 5.12428 25.77249 1.35638 9.03283 15.38328 - 9.25010 17.29911 4.15848 24.57427 1.22200 7.96708 15.38520 - 9.50010 24.72271 4.97026 23.52955 1.10640 7.03604 15.38711 - 9.75010 27.60020 5.25400 22.62452 1.00628 6.22921 15.38903 - 10.00010 26.44252 5.14439 21.84270 0.91902 5.53274 15.39094 - 10.25010 21.70486 4.66009 21.16757 0.84253 4.93218 15.39286 - 10.50010 17.73195 4.20986 20.58378 0.77512 4.41389 15.39477 - 10.75010 24.07394 4.90529 20.07778 0.71543 3.96566 15.39668 - 11.00010 21.08990 4.59286 19.63783 0.66233 3.57690 15.39860 - 11.25010 22.86257 4.78227 19.25397 0.61488 3.23858 15.40051 - 11.50010 18.54626 4.30609 18.91780 0.57233 2.94305 15.40243 - 11.75010 18.04287 4.24627 18.62228 0.53401 2.68392 15.40434 - 12.00010 15.96562 3.99636 18.36149 0.49940 2.45583 15.40626 - 12.25010 17.99747 4.24330 18.13049 0.46803 2.25429 15.40817 - 12.50010 22.32511 4.72406 17.92513 0.43951 2.07553 15.41009 - 12.75010 17.83220 4.22203 17.74192 0.41351 1.91641 15.41200 - 13.00010 15.21233 3.89969 17.57792 0.38974 1.77427 15.41392 - 13.25010 16.77581 4.09747 17.43064 0.36795 1.64686 15.41583 - 13.50010 15.88592 3.98705 17.29797 0.34794 1.53229 15.41774 - 13.75010 17.18823 4.14397 17.17810 0.32950 1.42894 15.41966 - 14.00010 19.08640 4.36934 17.06949 0.31249 1.33543 15.42157 - 14.25010 16.88034 4.10990 16.97083 0.29676 1.25059 15.42349 - 14.50010 13.76822 3.70983 16.88098 0.28218 1.17340 15.42540 - 14.75010 18.39081 4.28834 16.79896 0.26865 1.10299 15.42732 - 15.00010 16.91918 4.11388 16.72391 0.25607 1.03861 15.42923 - 15.25010 16.04499 4.00562 16.65509 0.24435 0.97960 15.43115 - diff --git a/test_data/IPTS-34735/exp813/Shared/H0L_1.05.txt b/test_data/IPTS-34735/exp813/Shared/H0L_1.05.txt deleted file mode 100644 index 9e9dd54c..00000000 --- a/test_data/IPTS-34735/exp813/Shared/H0L_1.05.txt +++ /dev/null @@ -1,66 +0,0 @@ -# Group 1 -# x y yerr ModelFit Func1 Func2 Func3 - 0.50010 106.00000 10.29563 11.90113 -625.13202 0.64886 636.38428 - 0.75000 51.00000 7.14143 10.60833 -637.75781 0.68457 647.68158 - 1.00000 22.00000 4.69042 9.41824 -650.38867 0.72331 659.08362 - 1.25000 9.00000 3.00000 8.33003 -663.01953 0.76541 670.58417 - 1.49990 2.00000 1.41421 7.34265 -675.64532 0.81124 682.17676 - 1.75000 13.00000 3.60555 6.45352 -688.28125 0.86132 693.87347 - 1.99990 8.00000 2.82843 5.66264 -700.90704 0.91609 705.65363 - 2.24990 3.00000 1.73205 4.96756 -713.53790 0.97623 717.52924 - 2.50000 1.00000 1.00000 4.36698 -726.17383 1.04246 729.49835 - 2.74980 10.00000 3.16228 3.86038 -738.79456 1.11549 741.53949 - 3.00040 8.00000 2.82843 3.44451 -751.45575 1.19664 753.70361 - 3.24980 8.00000 2.82843 3.12127 -764.05627 1.28641 765.89117 - 3.50010 3.00000 1.73205 2.88673 -776.70233 1.38690 778.20215 - 3.75010 5.00000 2.23607 2.74146 -789.33319 1.49930 790.57532 - 3.99990 7.00000 2.64575 2.68453 -801.95392 1.62560 803.01288 - 4.25000 10.00000 3.16228 2.71560 -814.58984 1.76843 815.53699 - 4.50000 6.00000 2.44949 2.83489 -827.22070 1.93046 828.12512 - 4.75020 3.00000 1.73205 3.04333 -839.86163 2.11547 840.78949 - 5.00000 7.00000 2.64575 3.34182 -852.48242 2.32733 853.49689 - 5.25010 6.00000 2.44949 3.73363 -865.11829 2.57208 866.27985 - 5.49980 7.00000 2.64575 4.22139 -877.73401 2.85580 879.09961 - 5.75020 3.00000 1.73205 4.81295 -890.38507 3.18844 892.00958 - 5.99980 3.00000 1.73205 5.51251 -902.99573 3.57894 904.92932 - 6.25020 11.00000 3.31662 6.33544 -915.64679 4.04394 917.93829 - 6.49980 6.00000 2.44949 7.29146 -928.25745 4.59872 930.95020 - 6.75030 8.00000 2.82843 8.40770 -940.91357 5.27105 944.05023 - 6.99970 7.00000 2.64575 9.70290 -953.51410 6.08666 957.13037 - 7.25020 7.00000 2.64575 11.22634 -966.17023 7.09413 970.30243 - 7.50000 11.00000 3.31662 13.01819 -978.79095 8.34082 983.46832 - 7.75010 19.00000 4.35890 15.15157 -991.42688 9.90136 996.67712 - 7.99990 27.00000 5.19615 17.70543 -1004.04761 11.85976 1009.89331 - 8.25000 21.00000 4.58258 20.78402 -1016.68353 14.32259 1023.14496 - 8.49990 27.00000 5.19615 24.47482 -1029.30933 17.38236 1036.40186 - 8.75010 33.00000 5.74456 28.82212 -1041.95032 21.08595 1049.68652 - 9.00000 28.00000 5.29150 33.68429 -1054.57605 25.29718 1062.96326 - 9.25030 38.00000 6.16441 38.60773 -1067.22217 29.56464 1076.26526 - 9.49990 49.00000 7.00000 42.66964 -1079.83276 32.97240 1089.53003 - 9.74980 42.00000 6.48074 44.80304 -1092.45862 34.45494 1102.80664 - 9.99990 48.00000 6.92820 44.41781 -1105.09448 33.42654 1116.08569 - 10.25030 39.00000 6.24500 41.91283 -1117.74561 30.29002 1129.36841 - 10.50010 34.00000 5.83095 38.34469 -1130.36633 26.10844 1142.60254 - 10.75000 35.00000 5.91608 34.66918 -1142.99207 21.84007 1155.82117 - 11.00000 35.00000 5.91608 31.42365 -1155.62292 18.02657 1169.02002 - 11.25010 29.00000 5.38516 28.78067 -1168.25891 14.84489 1182.19470 - 11.50010 31.00000 5.56776 26.71841 -1180.88977 12.27799 1195.33020 - 11.75000 26.00000 5.09902 25.13985 -1193.51550 10.23321 1208.42212 - 11.99970 26.00000 5.09902 23.93751 -1206.13123 8.60762 1221.46106 - 12.25030 27.00000 5.19615 23.01186 -1218.79248 7.30447 1234.49988 - 12.49990 19.00000 4.35890 22.29194 -1231.40308 6.26011 1247.43494 - 12.75000 19.00000 4.35890 21.71134 -1244.03894 5.41055 1260.33972 - 13.00000 20.00000 4.47214 21.22383 -1256.66980 4.71487 1273.17883 - 13.24960 16.00000 4.00000 20.79227 -1269.28052 4.14052 1285.93225 - 13.50030 32.00000 5.65685 20.38451 -1281.94666 3.65937 1298.67188 - 13.75030 16.00000 4.00000 19.97970 -1294.57751 3.25586 1311.30139 - 13.99940 27.00000 5.19615 19.55887 -1307.16296 2.91485 1323.80701 - 14.25060 19.00000 4.35890 19.10064 -1319.85437 2.62112 1336.33398 - 14.50000 24.00000 4.89898 18.59846 -1332.45496 2.37034 1348.68311 - 14.74960 16.00000 4.00000 18.03710 -1345.06567 2.15300 1360.94971 - 15.00040 17.00000 4.12311 17.40349 -1357.73682 1.96283 1373.17749 - 15.25000 17.00000 4.12311 16.69455 -1370.34753 1.79711 1385.24500 - 15.49980 9.00000 3.00000 15.89850 -1382.96826 1.65109 1397.21570 - 15.75000 19.00000 4.35890 15.00680 -1395.60925 1.52172 1409.09436 - 16.00120 24.00000 4.89898 14.00942 -1408.30078 1.40635 1420.90381 - diff --git a/test_data/IPTS-34735/exp813/Shared/H0L_1.05_fitparams.fit b/test_data/IPTS-34735/exp813/Shared/H0L_1.05_fitparams.fit deleted file mode 100644 index e706b537..00000000 --- a/test_data/IPTS-34735/exp813/Shared/H0L_1.05_fitparams.fit +++ /dev/null @@ -1,20 +0,0 @@ -############################### -Group: 1 -############################### ------------------ -Curve 1: BACKGROUND -#0: offset: -5.999e+02 +/- 6.137e+03,0,0,0.000e+00,0,0.000e+00,0 -#1: slope: -5.052e+01 +/- 2.832e+02,0,0,0.000e+00,0,0.000e+00,0 ------------------ -Curve 2: LORENTZIAN -#2: area: 1.391e+02 +/- 3.671e+01,0,0,0.000e+00,0,0.000e+00,0 -#3: center: 9.773e+00 +/- 1.201e-01,0,0,0.000e+00,0,0.000e+00,0 -#4: FWHM: 2.569e+00 +/- 5.724e-01,0,0,0.000e+00,0,0.000e+00,0 ------------------ -Curve 3: GAUSSIAN -#5: area: 9.007e+04 +/- 9.787e+05,0,0,0.000e+00,0,0.000e+00,0 -#6: center: 2.950e+01 +/- 5.600e+01,0,0,0.000e+00,0,0.000e+00,0 -#7: FWHM: 4.769e+01 +/- 1.336e+02,0,0,0.000e+00,0,0.000e+00,0 - -Chi-squared: 3.967 -############################### diff --git a/test_data/IPTS-34735/exp813/Shared/H0L_1.0_fitparams.fit b/test_data/IPTS-34735/exp813/Shared/H0L_1.0_fitparams.fit deleted file mode 100644 index 9415e0d3..00000000 --- a/test_data/IPTS-34735/exp813/Shared/H0L_1.0_fitparams.fit +++ /dev/null @@ -1,20 +0,0 @@ -############################### -Group: 1 -############################### ------------------ -Curve 1: LORENTZIAN -#0: area: 8.906e+01 +/- 9.371e+00,0,1,2.403e+01,1,1.682e+02,0 -#1: center: 4.548e+00 +/- 1.723e-01,0,1,1.149e+00,1,8.042e+00,0 -#2: FWHM: 1.992e+00 +/- 2.967e-01,0,1,5.206e-01,1,3.644e+00,0 ------------------ -Curve 2: LORENTZIAN -#3: area: 1.097e+02 +/- 1.214e+01,0,1,2.528e+01,1,1.770e+02,0 -#4: center: 7.068e+00 +/- 3.790e-01,0,1,1.805e+00,1,1.263e+01,0 -#5: FWHM: 3.978e+00 +/- 6.479e-01,0,1,9.389e-01,1,6.573e+00,0 ------------------ -Curve 3: BACKGROUND -#6: offset: 1.531e+01 +/- 8.011e-01,0,1,3.815e+00,1,2.670e+01,0 -#7: slope: 7.658e-03 +/- 3.335e-02,0,1,2.375e-03,1,1.663e-02,0 - -Chi-squared: 0.521 -############################### diff --git a/test_data/IPTS-34735/exp813/Shared/all_files_spectra.disv b/test_data/IPTS-34735/exp813/Shared/all_files_spectra.disv deleted file mode 100644 index c6cb11b0..00000000 Binary files a/test_data/IPTS-34735/exp813/Shared/all_files_spectra.disv and /dev/null differ diff --git a/test_data/IPTS-34735/exp813/Shared/contour.spe b/test_data/IPTS-34735/exp813/Shared/contour.spe deleted file mode 100644 index 587bf484..00000000 --- a/test_data/IPTS-34735/exp813/Shared/contour.spe +++ /dev/null @@ -1,1037 +0,0 @@ - 21 183 -### H (arb) - 1.0000 1.0250 1.0500 1.0816 1.1000 1.1250 1.1500 1.1750 - 1.2000 1.2250 1.2500 1.2750 1.3000 1.3250 1.3500 1.3750 - 1.4000 1.4250 1.4500 1.4763 1.5000 0.0000 -### E - 0.50010 0.75000 1.0000 1.2499 1.5000 1.7500 1.9999 2.2499 - 2.5000 2.7498 3.0004 3.2498 3.5001 3.7501 3.9999 4.2500 - 4.4998 4.7501 5.0000 5.2501 5.4999 5.7501 5.9999 6.2501 - 6.4998 6.7504 6.9996 7.2501 7.5001 7.7499 7.9999 8.2501 - 8.4999 8.7501 9.0000 9.2503 9.4998 9.7498 9.9999 10.250 - 10.500 10.750 11.000 11.250 11.500 11.750 12.000 12.250 - 12.500 12.750 13.000 13.250 13.500 13.750 13.999 14.251 - 14.500 14.750 15.000 15.250 15.500 15.750 16.000 16.250 - 16.500 16.750 17.000 17.250 17.500 17.750 18.000 18.250 - 18.500 18.750 19.000 19.250 19.500 19.750 20.000 20.250 - 20.500 20.750 21.000 21.250 21.499 21.750 22.000 22.249 - 22.501 22.750 23.000 23.250 23.500 23.750 24.000 24.250 - 24.500 24.750 25.000 25.250 25.501 25.750 26.000 26.250 - 26.499 26.750 27.001 27.250 27.500 27.751 28.000 28.250 - 28.500 28.750 28.999 29.250 29.500 29.750 30.000 30.251 - 30.500 30.750 31.000 31.250 31.500 31.750 32.002 33.003 - 33.084 33.250 33.500 33.750 34.003 34.249 34.500 34.750 - 35.003 35.249 35.501 35.750 36.000 36.250 36.500 36.750 - 37.000 37.251 37.500 37.750 38.002 38.003 38.251 38.500 - 38.751 39.000 39.250 39.501 39.750 40.003 40.201 40.399 - 40.601 40.799 41.001 41.201 41.400 41.600 41.800 42.001 - 42.200 42.403 42.601 42.799 43.000 43.200 43.400 43.605 - 43.799 44.000 44.200 44.401 44.604 44.801 45.005 0.0000 -### Corrected Counts - 1.e+03 523. 162. 45.7 22.8 18.8 17.8 17.5 - 17.8 17.7 19.5 22.4 26.3 32.8 37.3 37.4 - 35.8 38.4 43.2 42.4 38.4 35.9 34.2 33.7 - 35.4 38.0 37.8 33.7 31.9 33.1 35.1 32.6 - 29.0 28.1 26.3 23.3 25.1 28.0 27.2 23.0 - 20.8 21.9 21.8 21.6 20.1 18.9 18.2 19.8 - 21.2 19.1 17.1 17.0 16.6 17.2 18.2 17.0 - 15.7 16.8 16.3 14.6 11.9 10.8 12.5 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -### Errors - 31.6 22.9 12.7 6.76 4.77 4.33 4.22 4.18 - 4.22 4.21 4.41 4.74 5.12 5.72 6.11 6.11 - 5.99 6.20 6.57 6.51 6.19 5.99 5.84 5.81 - 5.95 6.16 6.15 5.81 5.65 5.75 5.92 5.71 - 5.38 5.30 5.12 4.82 5.01 5.29 5.21 4.80 - 4.56 4.68 4.66 4.64 4.49 4.34 4.27 4.45 - 4.61 4.37 4.14 4.12 4.07 4.15 4.26 4.13 - 3.96 4.10 4.03 3.82 3.44 3.28 3.54 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Corrected Counts - 454. 257. 89.6 28.2 15.3 13.6 12.0 10.6 - 11.3 12.0 13.0 14.5 16.4 19.7 22.6 22.9 - 22.0 24.4 28.8 30.2 28.0 26.4 28.0 27.8 - 26.7 28.5 30.1 27.6 26.7 30.0 31.8 31.1 - 28.6 29.3 28.9 28.3 29.6 32.9 32.1 27.1 - 24.7 24.3 23.8 23.5 22.7 21.6 20.9 21.9 - 21.9 19.9 18.4 18.5 18.0 18.4 18.9 18.3 - 17.2 16.7 15.8 14.0 12.4 12.7 13.8 14.6 - 14.0 12.9 12.5 11.2 10.0 10.2 10.2 9.64 - 9.11 8.78 8.41 9.32 8.37 8.52 8.86 9.00 - 9.89 8.70 7.50 8.02 9.56 11.2 10.4 9.55 - 10.3 11.4 11.5 10.2 10.1 11.2 13.4 13.9 - 12.1 11.8 10.9 12.3 12.5 11.8 12.3 12.1 - 11.3 10.6 10.7 10.4 10.1 10.8 11.2 10.3 - 9.13 9.64 10.2 8.40 6.68 6.23 7.14 7.46 - 7.68 9.69 13.4 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -### Errors - 21.3 16.0 9.47 5.31 3.91 3.68 3.47 3.26 - 3.36 3.46 3.61 3.80 4.05 4.44 4.75 4.78 - 4.69 4.94 5.37 5.50 5.29 5.14 5.30 5.27 - 5.17 5.34 5.48 5.25 5.17 5.47 5.64 5.58 - 5.35 5.42 5.37 5.32 5.44 5.74 5.67 5.20 - 4.97 4.93 4.88 4.85 4.76 4.64 4.57 4.68 - 4.68 4.46 4.29 4.30 4.24 4.29 4.35 4.28 - 4.14 4.08 3.97 3.74 3.52 3.57 3.71 3.83 - 3.75 3.60 3.54 3.34 3.16 3.19 3.20 3.10 - 3.02 2.96 2.90 3.05 2.89 2.92 2.98 3.00 - 3.14 2.95 2.74 2.83 3.09 3.35 3.22 3.09 - 3.21 3.38 3.39 3.19 3.18 3.35 3.65 3.73 - 3.48 3.44 3.30 3.51 3.54 3.44 3.51 3.47 - 3.36 3.26 3.27 3.22 3.17 3.29 3.35 3.21 - 3.02 3.11 3.20 2.90 2.58 2.50 2.67 2.73 - 2.77 3.11 3.67 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Corrected Counts - 154. 95.6 41.4 16.3 9.63 10.2 8.63 6.15 - 6.29 8.51 9.23 9.16 8.90 10.2 12.2 12.9 - 11.9 12.7 15.8 17.3 16.5 15.5 17.1 18.5 - 17.1 17.8 18.9 18.5 20.0 24.6 28.0 27.6 - 28.2 30.0 30.7 33.4 37.0 38.6 38.1 33.3 - 29.8 29.0 28.1 26.8 26.0 24.4 23.8 23.6 - 21.7 19.9 19.1 19.5 21.2 19.9 20.7 19.9 - 19.1 17.2 15.9 14.4 12.9 15.1 16.5 15.0 - 14.7 14.8 13.9 11.8 11.1 9.98 9.58 9.44 - 10.0 10.1 9.65 8.97 7.67 7.98 8.64 9.41 - 9.90 9.34 8.95 8.75 10.4 10.8 10.7 10.0 - 10.6 12.9 11.5 10.2 10.2 11.2 12.8 13.5 - 13.1 11.6 10.9 12.0 12.5 11.2 11.8 12.6 - 12.9 12.7 11.8 10.3 10.8 11.6 12.5 11.8 - 9.16 9.13 9.57 7.95 7.07 6.66 7.32 7.52 - 6.72 7.72 9.32 9.19 9.45 15.8 21.3 12.5 - 5.11 0.900 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Errors - 12.4 9.78 6.44 4.04 3.10 3.20 2.94 2.48 - 2.51 2.92 3.04 3.03 2.98 3.20 3.49 3.59 - 3.45 3.57 3.98 4.16 4.06 3.94 4.14 4.30 - 4.14 4.22 4.35 4.30 4.47 4.96 5.29 5.25 - 5.31 5.48 5.54 5.78 6.08 6.21 6.17 5.77 - 5.46 5.38 5.31 5.18 5.10 4.94 4.88 4.86 - 4.65 4.46 4.37 4.41 4.60 4.46 4.55 4.46 - 4.37 4.14 3.99 3.79 3.60 3.89 4.07 3.87 - 3.83 3.85 3.73 3.44 3.33 3.16 3.10 3.07 - 3.16 3.18 3.11 3.00 2.77 2.83 2.94 3.07 - 3.15 3.06 2.99 2.96 3.22 3.29 3.27 3.17 - 3.25 3.59 3.39 3.19 3.19 3.35 3.57 3.68 - 3.62 3.40 3.30 3.47 3.53 3.34 3.44 3.55 - 3.59 3.56 3.43 3.21 3.28 3.41 3.54 3.43 - 3.03 3.02 3.09 2.82 2.66 2.58 2.70 2.74 - 2.59 2.78 3.05 3.03 3.07 3.98 4.64 3.62 - 2.44 1.37 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 0.993 0.943 0.866 - 0.943 0.993 1.00 1.00 1.00 1.00 1.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 15.5 - 7.87 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -### Errors - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.98 - 2.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.194 1.78 5.58 8.08 - 8.24 5.07 4.20 4.26 4.37 4.23 3.72 3.80 - 4.67 5.91 5.62 5.17 5.85 5.80 5.30 5.03 - 5.93 6.52 5.73 4.67 4.49 5.20 6.40 6.10 - 5.77 5.04 5.65 6.46 5.46 5.28 5.05 6.10 - 6.74 5.47 4.71 4.35 4.78 6.62 8.51 9.29 - 9.54 11.5 14.1 16.3 16.4 17.3 19.0 19.3 - 22.0 23.2 23.2 22.8 25.5 25.1 23.2 23.1 - 22.9 23.1 26.6 27.1 25.1 25.0 25.7 24.0 - 22.0 20.3 19.0 20.3 20.3 18.0 15.7 14.5 - 14.7 14.9 14.1 14.1 13.3 12.3 12.3 10.9 - 10.5 11.3 10.6 9.04 8.49 9.61 11.0 11.3 - 11.2 11.1 12.2 13.3 12.2 11.7 12.5 12.7 - 14.3 14.1 13.6 11.4 10.8 10.8 12.0 11.4 - 11.5 11.6 10.8 9.22 8.59 9.45 11.6 11.3 - 8.59 6.47 6.12 8.44 10.7 5.96 3.36 5.34 - 7.13 4.26 0.500 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.08 1.59 2.42 2.85 - 2.87 2.25 2.05 2.06 2.09 2.06 1.93 1.95 - 2.16 2.43 2.37 2.27 2.42 2.41 2.30 2.24 - 2.44 2.55 2.39 2.16 2.12 2.28 2.53 2.47 - 2.40 2.24 2.38 2.54 2.34 2.30 2.25 2.47 - 2.60 2.34 2.17 2.09 2.19 2.60 2.97 3.07 - 3.09 3.39 3.76 4.03 4.05 4.16 4.35 4.39 - 4.69 4.82 4.81 4.78 5.04 5.01 4.81 4.81 - 4.78 4.81 5.16 5.21 5.01 5.00 5.07 4.90 - 4.69 4.50 4.36 4.50 4.51 4.24 3.96 3.81 - 3.84 3.86 3.76 3.76 3.64 3.49 3.41 3.00 - 2.77 2.84 2.86 2.87 2.90 3.10 3.32 3.36 - 3.35 3.34 3.49 3.65 3.49 3.42 3.54 3.56 - 3.78 3.76 3.69 3.37 3.29 3.29 3.47 3.38 - 3.39 3.40 3.29 3.04 2.93 3.07 3.41 3.37 - 2.95 2.56 2.47 2.91 3.30 2.53 1.99 2.44 - 2.80 2.26 1.22 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 0.993 0.943 0.866 - 0.943 0.993 1.00 1.00 1.00 1.00 1.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 3.92 4.44 4.13 3.70 3.62 4.02 - 4.65 5.73 5.79 5.45 5.93 6.13 5.48 4.97 - 5.48 6.16 5.35 4.44 4.28 5.12 5.84 5.98 - 5.81 4.85 4.96 5.74 5.43 5.59 6.22 7.38 - 7.20 5.84 4.57 4.00 4.86 7.23 8.41 8.34 - 8.91 9.24 10.0 10.8 10.4 11.4 12.5 13.9 - 14.9 15.2 14.6 14.9 16.4 16.4 17.2 19.8 - 19.9 19.7 23.1 24.8 23.7 24.3 24.5 24.9 - 24.1 22.1 21.1 21.6 22.4 21.1 19.6 18.7 - 19.5 19.2 18.8 18.0 18.0 17.7 16.0 13.7 - 12.2 12.9 13.3 11.9 11.4 12.0 12.6 12.6 - 13.0 12.3 12.4 13.0 12.8 12.7 13.4 13.0 - 12.8 14.2 13.6 13.1 11.8 12.6 14.3 14.5 - 13.2 13.0 11.7 10.3 9.60 9.63 10.4 9.28 - 7.96 7.24 6.75 7.31 8.16 6.64 5.43 8.07 - 13.0 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -### Errors - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 1.98 2.11 2.03 1.92 1.90 2.01 - 2.16 2.39 2.41 2.33 2.43 2.48 2.34 2.23 - 2.34 2.48 2.31 2.11 2.07 2.26 2.42 2.44 - 2.41 2.20 2.23 2.40 2.33 2.36 2.49 2.72 - 2.68 2.42 2.14 2.00 2.21 2.70 2.92 2.90 - 2.99 3.04 3.16 3.28 3.23 3.37 3.54 3.72 - 3.86 3.89 3.81 3.87 4.05 4.05 4.15 4.45 - 4.46 4.44 4.80 4.97 4.87 4.93 4.95 4.99 - 4.91 4.70 4.59 4.65 4.73 4.60 4.43 4.33 - 4.41 4.39 4.33 4.25 4.24 4.20 3.96 3.59 - 3.31 3.40 3.50 3.39 3.36 3.46 3.54 3.56 - 3.60 3.51 3.52 3.61 3.57 3.56 3.66 3.61 - 3.57 3.76 3.68 3.62 3.44 3.55 3.79 3.81 - 3.63 3.61 3.42 3.21 3.10 3.10 3.23 3.05 - 2.82 2.69 2.60 2.70 2.87 2.61 2.38 2.89 - 3.66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 4.25 4.77 4.74 3.92 3.62 3.71 - 3.98 4.82 5.38 5.70 6.29 5.95 5.06 5.18 - 5.70 6.53 6.54 5.36 4.76 5.44 6.05 6.16 - 5.74 4.97 4.89 5.25 5.62 6.01 6.42 6.55 - 5.94 5.54 4.44 4.21 5.27 6.78 7.05 6.77 - 7.31 6.90 6.92 7.16 6.75 6.94 8.75 9.46 - 9.02 7.99 7.69 8.73 9.08 9.06 10.8 12.7 - 11.8 11.7 13.3 14.3 15.2 15.0 15.5 16.6 - 17.1 16.7 15.9 16.8 18.3 19.1 19.2 18.9 - 19.5 20.4 21.6 21.2 21.5 21.3 20.2 17.4 - 15.5 16.5 18.4 19.0 18.2 17.3 16.9 16.2 - 16.7 16.9 15.4 15.3 14.3 13.9 15.0 14.2 - 13.8 13.8 13.8 13.2 12.7 13.1 14.8 15.3 - 14.5 14.1 13.0 11.1 10.5 10.8 10.9 9.76 - 8.75 8.84 8.93 8.06 7.67 7.49 6.97 8.19 - 10.4 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -### Errors - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 2.06 2.18 2.18 1.98 1.90 1.93 - 2.00 2.20 2.32 2.39 2.51 2.44 2.25 2.28 - 2.39 2.56 2.56 2.31 2.18 2.33 2.46 2.48 - 2.40 2.23 2.21 2.29 2.37 2.45 2.53 2.56 - 2.44 2.35 2.11 2.05 2.30 2.61 2.66 2.60 - 2.71 2.63 2.63 2.68 2.60 2.63 2.96 3.08 - 3.00 2.83 2.77 2.95 3.01 3.01 3.28 3.57 - 3.44 3.42 3.65 3.78 3.90 3.87 3.94 4.07 - 4.14 4.08 3.99 4.10 4.28 4.37 4.38 4.35 - 4.41 4.51 4.64 4.61 4.63 4.62 4.49 4.16 - 3.92 4.04 4.27 4.35 4.27 4.16 4.11 4.02 - 4.09 4.12 3.93 3.91 3.78 3.73 3.88 3.77 - 3.71 3.71 3.71 3.64 3.56 3.62 3.85 3.92 - 3.80 3.76 3.61 3.33 3.23 3.28 3.31 3.12 - 2.96 2.97 2.99 2.84 2.77 2.74 2.65 2.87 - 3.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 4.28 4.55 5.28 4.68 3.96 3.77 - 4.30 4.86 4.70 5.12 5.20 4.99 4.69 5.38 - 6.37 7.41 7.65 6.67 6.11 6.53 6.53 6.22 - 5.44 5.67 5.85 5.50 5.56 6.43 6.47 5.82 - 5.00 5.68 5.68 5.34 5.75 6.35 6.02 6.01 - 6.40 5.86 6.24 6.90 6.82 6.89 8.17 8.22 - 7.11 6.55 6.18 6.55 7.29 7.69 8.55 8.22 - 7.31 6.59 7.08 8.06 8.38 8.18 8.24 9.54 - 10.3 9.77 9.62 10.4 11.4 12.0 12.1 11.8 - 12.3 14.3 15.3 16.1 16.5 17.5 18.2 17.8 - 16.3 17.3 19.3 21.4 22.4 21.2 20.3 20.1 - 22.0 22.1 19.6 17.6 17.4 17.4 18.2 18.3 - 17.6 16.7 16.4 15.3 14.2 13.7 14.1 14.8 - 14.8 14.5 14.2 12.2 11.3 12.4 13.5 12.1 - 10.8 10.6 10.5 9.35 9.19 8.73 8.70 8.18 - 8.32 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -### Errors - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 2.09 2.14 2.30 2.16 1.99 1.94 - 2.07 2.20 2.17 2.26 2.28 2.23 2.16 2.32 - 2.52 2.72 2.77 2.58 2.47 2.56 2.55 2.49 - 2.33 2.38 2.42 2.34 2.36 2.53 2.54 2.41 - 2.24 2.38 2.38 2.31 2.40 2.52 2.45 2.45 - 2.53 2.42 2.50 2.63 2.61 2.62 2.86 2.87 - 2.67 2.56 2.49 2.56 2.70 2.77 2.92 2.87 - 2.70 2.57 2.66 2.84 2.89 2.86 2.87 3.09 - 3.22 3.13 3.10 3.23 3.38 3.46 3.48 3.44 - 3.50 3.78 3.91 4.02 4.06 4.18 4.26 4.21 - 4.03 4.15 4.38 4.62 4.73 4.60 4.50 4.48 - 4.68 4.69 4.41 4.17 4.14 4.14 4.23 4.24 - 4.16 4.04 4.00 3.86 3.72 3.64 3.69 3.79 - 3.81 3.77 3.73 3.46 3.34 3.50 3.64 3.44 - 3.24 3.22 3.21 3.02 3.00 2.92 2.92 2.83 - 2.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.178 1.44 3.28 4.54 5.32 4.83 3.99 4.51 - 5.16 5.11 4.95 5.21 5.27 4.34 4.00 4.95 - 6.16 6.94 6.72 6.74 7.11 7.21 6.92 5.99 - 5.62 6.66 6.94 5.60 5.18 5.25 6.00 5.30 - 5.00 6.28 7.32 6.82 6.21 6.13 6.20 6.10 - 5.97 5.92 6.82 7.31 6.29 6.89 7.65 7.13 - 7.16 7.46 7.17 6.83 7.42 8.08 7.76 6.95 - 6.95 6.55 6.83 8.30 7.32 6.26 6.40 7.69 - 7.95 7.64 8.02 8.08 7.86 7.74 7.23 7.17 - 7.80 8.95 9.06 8.72 9.75 10.8 11.9 11.6 - 11.3 11.7 13.1 15.2 16.3 16.1 16.2 17.7 - 19.7 20.2 18.2 17.9 20.4 21.8 21.5 22.2 - 22.3 21.9 22.1 21.0 19.3 18.0 17.9 18.4 - 17.9 16.4 15.6 15.0 13.6 14.1 15.1 13.9 - 12.7 12.4 12.1 11.4 11.4 11.1 11.6 10.3 - 9.10 9.06 9.69 9.08 7.81 7.46 7.04 6.88 - 6.87 6.07 5.85 6.06 5.90 3.22 1.20 0.490 - 0.365 0.390 0.458 0.488 0.431 0.288 0.121 0.0229 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.07 1.45 1.87 2.14 2.31 2.20 2.00 2.12 - 2.27 2.26 2.23 2.28 2.30 2.08 2.00 2.22 - 2.48 2.63 2.59 2.60 2.67 2.69 2.63 2.45 - 2.37 2.58 2.63 2.37 2.28 2.29 2.45 2.30 - 2.24 2.51 2.71 2.61 2.49 2.48 2.49 2.47 - 2.44 2.43 2.61 2.70 2.51 2.62 2.77 2.67 - 2.68 2.73 2.68 2.61 2.72 2.84 2.79 2.64 - 2.64 2.56 2.61 2.88 2.71 2.50 2.53 2.77 - 2.82 2.76 2.83 2.84 2.80 2.78 2.69 2.68 - 2.79 2.99 3.01 2.95 3.12 3.29 3.44 3.41 - 3.36 3.40 3.56 3.84 3.98 3.96 3.97 4.14 - 4.37 4.41 4.17 4.11 4.37 4.49 4.45 4.50 - 4.48 4.43 4.45 4.31 4.08 3.91 3.90 3.97 - 3.92 3.75 3.68 3.64 3.49 3.56 3.68 3.52 - 3.35 3.30 3.24 3.13 3.15 3.12 3.20 2.98 - 2.81 2.81 2.92 2.79 2.55 2.53 2.48 2.47 - 2.47 2.31 2.27 2.31 2.30 1.77 1.30 1.12 - 1.09 1.10 1.12 1.13 1.11 1.08 1.03 1.01 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 0.991 0.940 0.878 - 0.940 0.991 1.00 1.00 1.00 1.00 1.00 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.172 1.21 3.35 5.20 5.65 4.65 3.89 4.56 - 5.22 5.19 5.31 5.43 5.04 3.85 3.24 4.20 - 5.44 5.55 5.02 5.61 5.89 5.57 5.46 5.46 - 6.00 7.10 6.57 5.44 4.65 4.89 5.52 5.18 - 5.55 7.00 7.46 7.28 5.67 5.79 6.67 6.25 - 5.48 5.91 6.94 6.34 5.39 6.56 7.44 7.29 - 7.54 8.29 8.16 7.76 8.14 7.64 7.10 7.23 - 6.90 6.50 6.79 8.17 7.93 6.63 6.59 7.42 - 7.42 7.82 7.68 7.20 6.93 6.98 7.11 6.34 - 6.55 8.03 7.13 6.24 6.78 7.71 7.86 7.47 - 7.73 8.02 8.18 8.65 8.94 8.96 9.48 10.9 - 11.8 12.4 12.4 13.1 16.1 18.6 18.7 19.1 - 21.0 21.3 21.8 22.7 23.0 22.9 23.1 23.7 - 23.0 22.0 21.5 19.8 17.9 17.4 17.4 16.0 - 15.0 15.0 15.3 15.4 14.5 14.0 14.3 13.6 - 11.4 10.6 10.3 10.2 9.73 8.71 8.05 7.43 - 6.98 6.44 5.85 6.12 5.76 4.53 3.06 2.35 - 2.03 2.17 2.54 2.67 2.35 1.51 0.557 0.0733 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.06 1.39 1.91 2.30 2.39 2.17 1.98 2.14 - 2.29 2.29 2.31 2.34 2.25 1.97 1.81 2.06 - 2.34 2.36 2.25 2.38 2.44 2.38 2.36 2.35 - 2.45 2.66 2.56 2.33 2.16 2.21 2.35 2.27 - 2.36 2.65 2.73 2.70 2.38 2.41 2.58 2.50 - 2.34 2.43 2.63 2.52 2.32 2.56 2.73 2.70 - 2.75 2.88 2.86 2.79 2.85 2.76 2.66 2.69 - 2.63 2.55 2.61 2.86 2.82 2.58 2.57 2.72 - 2.72 2.80 2.77 2.68 2.63 2.64 2.67 2.52 - 2.56 2.83 2.67 2.50 2.60 2.78 2.80 2.73 - 2.77 2.76 2.67 2.70 2.75 2.77 2.85 3.05 - 3.18 3.25 3.22 3.30 3.63 3.86 3.86 3.89 - 4.04 4.06 4.10 4.15 4.12 4.08 4.09 4.15 - 4.09 3.98 3.94 3.81 3.64 3.60 3.59 3.45 - 3.33 3.32 3.34 3.34 3.25 3.21 3.24 3.10 - 2.79 2.75 2.77 2.75 2.66 2.53 2.45 2.37 - 2.31 2.21 2.11 2.16 2.10 1.88 1.63 1.51 - 1.44 1.47 1.55 1.58 1.52 1.35 1.14 1.02 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 0.990 0.938 0.883 - 0.938 0.990 1.00 1.00 1.00 1.00 1.00 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.140 1.02 2.85 4.23 4.67 3.63 3.40 3.63 - 3.93 3.90 4.09 4.29 3.48 3.10 2.60 2.88 - 3.82 3.95 3.81 3.80 3.81 3.72 4.14 5.10 - 5.96 5.97 5.73 5.78 5.49 5.74 6.12 5.83 - 6.11 7.06 7.37 6.15 5.12 5.27 6.05 5.55 - 4.90 5.84 6.77 6.61 6.05 6.63 7.64 8.02 - 7.58 8.36 9.24 8.69 7.99 7.33 7.12 7.40 - 6.93 6.13 6.28 7.50 7.85 7.29 7.12 7.63 - 7.76 7.77 6.98 6.21 5.82 6.52 7.01 6.37 - 6.17 7.43 7.69 7.39 7.55 7.20 6.72 6.90 - 7.76 7.62 6.83 6.81 6.61 7.13 7.71 7.74 - 7.62 7.86 8.50 8.71 10.1 11.3 11.6 12.6 - 13.6 13.7 15.1 17.3 18.5 19.0 20.2 22.7 - 23.6 24.6 25.4 24.8 23.1 21.8 20.5 18.9 - 18.3 18.6 19.3 18.4 16.7 16.1 16.1 15.8 - 14.7 14.0 13.7 12.8 12.3 12.1 11.5 10.6 - 9.26 8.23 7.65 7.30 6.51 6.00 6.35 6.05 - 5.50 5.72 6.37 6.63 5.76 3.58 1.22 0.386 - 0.310 0.301 0.293 0.313 0.339 0.351 0.333 0.760 - 2.14 3.02 1.99 0.494 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.05 1.35 1.82 2.12 2.22 1.97 1.91 1.97 - 2.04 2.03 2.08 2.13 1.93 1.83 1.68 1.77 - 2.01 2.05 2.01 2.01 2.01 2.00 2.10 2.28 - 2.44 2.44 2.39 2.41 2.34 2.39 2.47 2.41 - 2.47 2.66 2.71 2.48 2.26 2.29 2.46 2.36 - 2.21 2.42 2.60 2.57 2.46 2.58 2.76 2.83 - 2.75 2.89 3.04 2.95 2.83 2.71 2.67 2.72 - 2.63 2.48 2.51 2.74 2.80 2.70 2.67 2.76 - 2.79 2.79 2.64 2.49 2.41 2.55 2.65 2.52 - 2.48 2.73 2.77 2.72 2.75 2.68 2.59 2.63 - 2.77 2.65 2.31 2.18 2.14 2.22 2.31 2.32 - 2.31 2.35 2.43 2.47 2.66 2.80 2.84 2.95 - 3.06 3.07 3.22 3.44 3.54 3.58 3.69 3.91 - 3.98 4.07 4.13 4.08 3.94 3.82 3.71 3.56 - 3.50 3.52 3.58 3.50 3.34 3.27 3.26 3.16 - 2.93 2.96 3.01 2.92 2.86 2.83 2.77 2.65 - 2.48 2.34 2.25 2.20 2.08 2.01 2.09 2.05 - 1.97 2.00 2.11 2.16 2.04 1.70 1.27 1.08 - 1.06 1.05 1.05 1.06 1.06 1.07 1.06 1.16 - 1.42 1.58 1.41 1.12 1.00 0.990 0.938 0.883 - 0.938 0.990 1.00 1.00 1.00 1.00 1.00 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.122 0.812 2.06 3.05 3.12 2.66 2.66 3.08 - 3.21 2.97 2.91 2.96 2.79 2.53 2.18 2.02 - 2.12 2.41 2.79 2.81 2.58 3.27 5.15 5.77 - 5.73 5.31 5.36 6.00 5.99 6.41 6.55 5.68 - 5.86 6.69 6.77 5.86 4.98 5.25 5.89 5.33 - 4.84 6.00 7.79 7.85 7.25 6.76 6.59 6.47 - 7.26 8.42 9.02 8.51 7.31 7.61 7.71 7.44 - 6.94 6.71 6.99 7.90 8.02 7.34 7.44 7.46 - 8.02 8.18 6.98 5.47 4.88 5.62 6.57 6.39 - 6.13 6.94 7.91 7.99 7.44 7.13 6.62 6.44 - 7.03 6.92 7.03 6.65 6.82 7.20 7.76 7.46 - 6.94 7.07 7.28 7.13 7.29 7.76 7.93 8.67 - 8.75 8.77 9.65 10.6 11.2 12.2 13.4 15.7 - 17.4 19.0 20.5 20.3 20.4 20.6 21.1 22.1 - 22.9 23.3 22.2 19.8 18.3 18.1 17.9 17.6 - 17.0 17.0 17.2 16.2 15.4 15.9 16.1 14.7 - 13.1 11.5 10.9 11.0 9.92 9.19 9.05 9.19 - 8.78 8.56 8.52 8.69 7.75 4.66 2.38 2.01 - 2.26 2.15 2.07 2.21 2.32 2.36 2.25 3.86 - 13.1 20.4 12.2 2.25 0.304 3.20 11.7 17.3 - 11.4 2.85 0.00 0.00 0.00 0.00 0.00 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.05 1.28 1.63 1.88 1.89 1.76 1.76 1.88 - 1.91 1.85 1.83 1.84 1.80 1.72 1.62 1.57 - 1.60 1.69 1.80 1.80 1.74 1.91 2.32 2.42 - 2.40 2.30 2.32 2.45 2.45 2.53 2.56 2.38 - 2.42 2.59 2.60 2.42 2.23 2.29 2.43 2.31 - 2.20 2.45 2.79 2.80 2.69 2.60 2.57 2.54 - 2.69 2.90 3.00 2.92 2.70 2.76 2.78 2.73 - 2.63 2.59 2.64 2.81 2.83 2.71 2.73 2.73 - 2.83 2.86 2.64 2.34 2.21 2.37 2.56 2.53 - 2.48 2.63 2.81 2.83 2.73 2.67 2.57 2.54 - 2.63 2.49 2.26 2.04 2.05 2.12 2.21 2.15 - 2.07 2.09 2.12 2.10 2.12 2.18 2.21 2.33 - 2.35 2.35 2.47 2.59 2.68 2.79 2.93 3.19 - 3.35 3.50 3.64 3.60 3.61 3.62 3.65 3.72 - 3.78 3.81 3.71 3.49 3.33 3.31 3.28 3.21 - 3.13 3.18 3.25 3.17 3.07 3.12 3.13 2.99 - 2.82 2.63 2.56 2.56 2.42 2.33 2.32 2.36 - 2.31 2.28 2.29 2.32 2.21 1.78 1.39 1.29 - 1.33 1.31 1.29 1.32 1.34 1.36 1.34 1.61 - 2.68 3.30 2.63 1.42 1.02 1.25 1.75 2.00 - 1.74 1.23 1.00 1.00 1.00 1.00 1.00 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.167 1.17 3.07 4.18 3.97 3.43 3.55 4.33 - 4.94 4.41 4.07 4.10 4.27 3.78 3.26 2.90 - 2.56 2.80 3.78 3.95 3.61 4.15 5.71 5.89 - 5.65 5.51 5.28 5.58 5.94 6.23 6.72 6.21 - 5.65 5.73 6.14 5.94 5.16 4.66 5.05 5.93 - 6.16 6.45 7.26 6.91 5.94 5.42 5.44 5.29 - 5.93 7.28 7.85 7.59 6.95 7.62 8.89 8.14 - 7.83 7.36 7.37 7.20 7.57 7.23 7.28 7.45 - 7.57 8.11 7.37 5.71 5.01 5.52 6.22 6.69 - 7.05 7.73 7.97 8.14 7.54 6.67 6.31 5.96 - 5.75 6.25 6.58 7.03 6.71 6.63 6.95 7.10 - 6.71 6.73 7.01 6.81 6.91 7.52 7.59 7.31 - 7.21 7.36 7.75 7.73 7.76 8.28 8.83 9.51 - 10.5 11.5 12.3 13.2 14.2 15.5 17.8 20.9 - 22.0 22.0 21.8 20.6 20.7 21.3 21.2 20.3 - 19.4 19.3 19.1 18.0 17.6 18.2 18.5 17.5 - 15.8 14.4 13.9 14.5 14.6 13.9 13.0 12.5 - 11.8 11.0 10.4 10.6 9.86 6.96 4.53 5.29 - 6.25 5.92 5.61 5.94 5.78 5.72 5.44 7.99 - 23.6 44.0 21.7 4.87 2.08 13.9 70.9 115. - 68.9 11.7 0.578 1.56 2.17 1.41 0.348 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.07 1.40 1.88 2.11 2.05 1.92 1.95 2.14 - 2.28 2.16 2.08 2.08 2.12 2.01 1.87 1.77 - 1.67 1.74 2.00 2.05 1.96 2.08 2.41 2.43 - 2.38 2.35 2.30 2.36 2.44 2.50 2.59 2.49 - 2.38 2.40 2.48 2.44 2.27 2.16 2.25 2.43 - 2.48 2.54 2.70 2.63 2.44 2.33 2.33 2.30 - 2.44 2.70 2.80 2.75 2.64 2.76 2.98 2.85 - 2.80 2.71 2.71 2.68 2.75 2.69 2.70 2.73 - 2.75 2.85 2.72 2.39 2.24 2.35 2.49 2.59 - 2.66 2.78 2.82 2.85 2.75 2.58 2.51 2.44 - 2.37 2.33 2.09 1.98 1.91 1.92 1.97 1.97 - 1.91 1.92 1.95 1.92 1.93 2.00 2.02 2.00 - 2.00 2.01 2.07 2.07 2.08 2.16 2.24 2.34 - 2.46 2.59 2.67 2.75 2.85 2.96 3.17 3.42 - 3.50 3.50 3.47 3.34 3.33 3.37 3.35 3.29 - 3.21 3.22 3.22 3.14 3.09 3.15 3.16 3.07 - 2.92 2.78 2.72 2.78 2.78 2.70 2.62 2.57 - 2.51 2.42 2.36 2.38 2.30 1.96 1.58 1.61 - 1.70 1.65 1.60 1.66 1.66 1.67 1.63 1.97 - 3.41 4.69 3.32 1.65 1.14 1.88 3.76 4.74 - 3.72 1.79 1.12 1.32 1.43 1.30 1.08 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.233 1.68 4.36 5.32 4.98 4.78 4.52 5.11 - 5.94 5.79 5.22 5.40 5.23 4.58 4.43 4.18 - 4.19 4.24 4.80 5.09 5.21 5.46 5.78 6.16 - 6.03 5.70 5.19 5.17 5.27 5.85 6.42 6.29 - 5.25 4.93 5.45 5.27 4.58 3.99 4.51 5.76 - 6.46 6.05 5.97 6.05 5.18 5.18 5.81 5.92 - 5.52 6.22 6.98 7.17 7.62 7.89 8.87 8.62 - 8.50 8.12 6.71 6.61 6.58 7.10 7.70 7.18 - 7.33 7.56 7.20 6.75 6.10 5.97 6.60 7.82 - 7.97 7.69 8.00 7.74 7.91 7.44 6.38 5.39 - 5.26 5.48 6.04 6.50 6.23 6.24 6.41 6.49 - 6.19 6.25 6.49 6.49 6.71 6.99 6.91 6.95 - 6.84 7.10 7.47 7.17 6.79 6.74 6.76 6.90 - 7.16 7.39 7.74 8.46 9.46 10.9 13.9 17.1 - 18.1 18.1 18.3 18.9 20.0 21.1 21.0 20.9 - 20.5 20.9 21.1 20.2 19.1 19.0 19.2 18.7 - 17.5 16.1 15.8 16.6 17.5 17.1 15.9 15.4 - 14.8 13.7 13.1 13.7 13.0 9.78 7.43 8.65 - 9.81 9.29 8.79 8.80 8.09 7.29 7.24 9.45 - 18.4 25.2 16.9 7.00 5.38 23.1 121. 244. - 116. 18.8 3.02 9.63 14.6 8.66 1.66 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.09 1.55 2.16 2.32 2.24 2.20 2.14 2.27 - 2.45 2.42 2.29 2.33 2.30 2.15 2.11 2.05 - 2.06 2.07 2.20 2.27 2.29 2.34 2.41 2.48 - 2.45 2.39 2.28 2.27 2.30 2.42 2.54 2.52 - 2.31 2.23 2.34 2.30 2.14 2.00 2.12 2.40 - 2.55 2.48 2.46 2.46 2.28 2.28 2.41 2.43 - 2.35 2.49 2.64 2.68 2.76 2.81 2.98 2.94 - 2.92 2.85 2.59 2.57 2.57 2.66 2.78 2.68 - 2.71 2.75 2.68 2.60 2.47 2.44 2.57 2.80 - 2.82 2.77 2.83 2.78 2.81 2.73 2.53 2.32 - 2.27 2.17 1.98 1.87 1.80 1.81 1.84 1.85 - 1.81 1.82 1.85 1.84 1.86 1.90 1.89 1.90 - 1.89 1.93 1.98 1.94 1.89 1.88 1.88 1.89 - 1.92 1.95 2.00 2.09 2.20 2.37 2.66 2.95 - 3.04 3.03 3.05 3.09 3.18 3.26 3.25 3.25 - 3.21 3.25 3.26 3.19 3.11 3.10 3.12 3.07 - 2.97 2.85 2.82 2.89 2.96 2.93 2.83 2.78 - 2.73 2.62 2.56 2.62 2.56 2.23 1.93 1.93 - 1.99 1.93 1.87 1.87 1.82 1.75 1.75 2.01 - 2.91 3.46 2.82 1.74 1.46 2.34 4.87 6.83 - 4.78 2.18 1.49 2.35 2.84 2.28 1.33 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.230 1.67 4.40 5.89 5.86 5.51 5.63 5.94 - 5.59 5.27 5.52 5.14 4.58 4.38 4.52 5.05 - 5.04 5.17 5.66 6.00 6.31 6.20 6.12 6.01 - 5.48 5.83 5.73 5.66 5.36 5.59 5.90 5.95 - 4.88 4.90 5.08 5.17 4.78 4.47 4.61 5.06 - 5.65 5.31 5.74 5.87 6.04 5.96 6.28 5.84 - 5.25 6.07 7.45 7.53 6.93 7.29 7.98 8.17 - 9.01 8.30 7.17 6.82 6.61 7.21 7.86 7.08 - 7.62 8.25 7.52 6.85 6.19 5.70 6.31 7.48 - 7.85 7.78 7.52 6.97 6.91 7.37 6.56 5.41 - 4.78 5.25 5.94 6.14 5.84 6.09 6.49 6.62 - 6.48 6.53 6.66 6.70 6.31 6.00 6.20 6.53 - 6.90 7.37 7.63 6.88 6.32 5.94 5.66 6.26 - 6.76 6.77 6.65 6.73 7.08 8.05 9.91 12.0 - 13.6 14.4 14.8 15.3 16.5 17.7 18.5 18.6 - 19.3 20.6 20.8 20.0 18.9 18.8 19.2 19.0 - 17.7 16.9 16.6 17.2 17.5 17.1 16.5 17.0 - 17.4 16.7 16.1 16.3 15.8 13.6 12.3 12.7 - 12.9 12.4 11.4 11.2 10.4 9.10 9.31 10.3 - 10.5 10.6 9.47 7.66 7.51 19.3 76.8 121. - 73.8 16.0 6.33 17.6 31.7 15.8 4.04 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.09 1.55 2.17 2.44 2.43 2.36 2.38 2.45 - 2.37 2.30 2.36 2.28 2.15 2.10 2.13 2.26 - 2.25 2.28 2.38 2.45 2.51 2.49 2.47 2.45 - 2.34 2.42 2.39 2.38 2.32 2.36 2.43 2.46 - 2.26 2.24 2.26 2.27 2.19 2.11 2.15 2.25 - 2.40 2.35 2.42 2.43 2.46 2.44 2.51 2.42 - 2.29 2.46 2.73 2.74 2.63 2.70 2.82 2.86 - 3.00 2.88 2.68 2.61 2.57 2.69 2.80 2.66 - 2.76 2.87 2.74 2.62 2.49 2.39 2.51 2.73 - 2.80 2.79 2.74 2.64 2.63 2.71 2.56 2.33 - 2.17 2.16 2.08 2.00 1.94 1.98 2.05 2.09 - 2.08 2.09 2.10 2.07 1.98 1.90 1.92 1.99 - 2.08 2.17 2.21 2.12 2.03 1.94 1.81 1.80 - 1.84 1.84 1.82 1.82 1.87 2.00 2.22 2.45 - 2.60 2.68 2.71 2.77 2.87 2.97 3.04 3.05 - 3.11 3.20 3.22 3.16 3.07 3.06 3.09 3.08 - 2.97 2.90 2.88 2.93 2.96 2.92 2.87 2.91 - 2.95 2.89 2.83 2.84 2.80 2.62 2.47 2.46 - 2.44 2.39 2.27 2.26 2.18 2.05 2.08 2.18 - 2.20 2.21 2.09 1.86 1.83 2.35 4.02 4.91 - 3.91 2.15 1.86 3.02 4.03 2.92 1.65 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.155 1.14 3.24 4.86 5.26 5.06 5.03 4.78 - 3.93 3.51 4.07 3.59 3.04 3.11 3.29 3.85 - 4.21 5.20 6.23 6.40 6.08 5.74 5.20 4.45 - 4.13 4.84 5.95 6.21 5.70 5.13 4.93 5.56 - 5.42 5.01 4.95 5.24 5.65 4.99 4.78 4.57 - 4.70 5.57 5.50 5.46 5.80 5.66 5.43 4.65 - 4.80 5.93 7.30 7.69 6.13 6.06 6.66 7.28 - 7.95 8.14 7.55 6.62 6.42 7.27 7.58 6.74 - 7.32 8.15 7.42 6.90 6.39 6.09 6.15 7.09 - 7.91 7.17 6.61 6.20 6.22 6.73 6.93 5.96 - 5.28 5.28 5.69 5.90 5.89 6.06 6.27 6.52 - 6.60 7.25 7.03 6.28 5.38 4.66 4.79 5.46 - 6.37 6.99 7.13 6.56 6.19 5.69 4.78 5.88 - 6.98 7.22 7.00 6.60 6.61 6.93 7.84 9.29 - 10.6 11.8 12.5 12.9 13.5 14.7 15.4 15.5 - 17.1 18.4 18.5 17.2 16.6 17.4 17.7 17.7 - 17.0 16.2 16.0 16.7 16.9 16.7 16.7 18.2 - 19.4 19.0 17.8 18.4 19.7 18.9 17.5 16.7 - 16.7 16.7 15.8 14.8 14.2 12.3 12.1 12.4 - 11.6 10.6 9.34 8.80 9.06 12.0 20.8 25.8 - 19.2 9.81 7.86 14.6 19.5 13.4 6.74 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.06 1.39 1.93 2.27 2.35 2.31 2.31 2.25 - 2.05 1.95 2.09 1.97 1.82 1.84 1.89 2.03 - 2.12 2.32 2.52 2.54 2.47 2.40 2.29 2.12 - 2.04 2.21 2.45 2.50 2.40 2.27 2.23 2.38 - 2.36 2.26 2.24 2.30 2.38 2.24 2.20 2.15 - 2.19 2.39 2.37 2.35 2.42 2.39 2.34 2.16 - 2.20 2.44 2.71 2.78 2.48 2.47 2.59 2.71 - 2.83 2.86 2.76 2.58 2.54 2.70 2.76 2.60 - 2.71 2.86 2.73 2.63 2.53 2.48 2.49 2.67 - 2.82 2.68 2.58 2.50 2.50 2.60 2.64 2.45 - 2.30 2.25 2.26 2.27 2.27 2.30 2.33 2.38 - 2.41 2.54 2.48 2.32 2.12 1.94 1.96 2.12 - 2.32 2.44 2.47 2.38 2.33 2.23 1.89 1.82 - 1.90 1.92 1.88 1.80 1.79 1.84 1.97 2.15 - 2.30 2.42 2.50 2.54 2.60 2.70 2.77 2.78 - 2.92 3.03 3.04 2.93 2.87 2.95 2.97 2.97 - 2.92 2.85 2.83 2.88 2.91 2.88 2.88 3.01 - 3.11 3.08 2.97 3.02 3.13 3.07 2.95 2.88 - 2.88 2.87 2.80 2.70 2.65 2.47 2.44 2.48 - 2.39 2.28 2.15 2.08 2.11 2.23 2.49 2.57 - 2.35 1.99 1.99 2.71 3.14 2.63 1.91 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.0650 0.458 1.25 1.96 2.22 2.19 2.09 1.85 - 1.46 1.33 1.47 1.35 1.14 1.15 1.25 1.44 - 1.84 3.18 5.34 5.49 4.47 4.35 4.22 3.04 - 2.66 3.10 4.15 5.41 4.90 3.98 3.40 4.01 - 4.76 4.59 4.32 4.42 4.26 4.12 3.97 3.47 - 3.48 4.27 4.45 4.22 4.12 4.34 4.17 3.49 - 3.57 4.38 5.50 5.45 4.81 4.27 5.13 5.74 - 6.46 6.64 6.22 5.19 5.28 6.14 6.04 5.23 - 5.66 6.41 5.89 5.62 5.96 5.55 5.38 6.05 - 6.28 5.75 4.92 4.95 4.99 5.44 5.66 5.41 - 5.00 4.08 4.49 4.50 5.02 5.17 4.76 4.41 - 4.90 6.07 5.78 4.58 3.46 3.32 3.28 3.69 - 4.77 5.22 5.20 4.91 5.44 5.88 4.96 5.81 - 6.84 7.00 6.69 6.73 6.94 7.04 7.60 8.99 - 9.90 10.5 11.1 11.5 11.6 12.0 12.4 13.1 - 15.1 16.4 16.5 15.5 14.8 15.7 16.2 16.2 - 15.4 15.1 15.0 15.6 16.9 17.0 16.2 16.3 - 17.3 17.5 17.3 19.5 22.6 22.6 20.4 19.8 - 21.9 25.4 25.6 23.0 19.9 16.6 16.6 19.0 - 19.7 17.7 13.9 11.8 11.2 11.3 11.0 10.8 - 10.3 9.69 9.53 10.2 10.5 9.08 8.42 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.03 1.17 1.43 1.64 1.71 1.71 1.68 1.60 - 1.47 1.43 1.48 1.44 1.36 1.37 1.40 1.47 - 1.60 1.94 2.39 2.40 2.17 2.14 2.11 1.81 - 1.70 1.83 2.09 2.38 2.27 2.05 1.91 2.07 - 2.24 2.20 2.14 2.16 2.12 2.09 2.05 1.93 - 1.93 2.13 2.17 2.11 2.09 2.14 2.10 1.93 - 1.95 2.15 2.40 2.38 2.25 2.12 2.32 2.45 - 2.59 2.62 2.54 2.33 2.35 2.53 2.51 2.34 - 2.43 2.58 2.48 2.42 2.49 2.41 2.37 2.51 - 2.55 2.45 2.27 2.28 2.29 2.38 2.43 2.38 - 2.29 2.07 2.15 2.15 2.27 2.30 2.21 2.13 - 2.24 2.49 2.43 2.16 1.89 1.85 1.84 1.94 - 2.20 2.30 2.30 2.23 2.33 2.42 2.10 1.98 - 2.06 2.07 1.95 1.87 1.88 1.90 1.97 2.13 - 2.22 2.28 2.35 2.39 2.41 2.45 2.48 2.55 - 2.74 2.87 2.87 2.78 2.72 2.80 2.84 2.84 - 2.77 2.75 2.74 2.79 2.91 2.91 2.84 2.84 - 2.93 2.96 2.94 3.10 3.35 3.36 3.19 3.15 - 3.31 3.56 3.58 3.39 3.15 2.89 2.88 3.09 - 3.14 2.97 2.64 2.42 2.37 2.35 2.21 2.06 - 2.13 2.18 2.18 2.26 2.30 2.14 2.07 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.0125 0.0700 0.178 0.280 0.328 0.328 0.305 0.260 - 0.205 0.188 0.200 0.188 0.163 0.160 0.175 0.202 - 0.440 1.63 3.35 4.00 3.90 3.87 3.46 2.70 - 2.19 2.31 3.14 3.92 3.64 2.89 2.69 2.98 - 3.35 3.44 3.26 2.96 2.76 3.05 3.17 2.59 - 2.38 2.91 3.13 2.88 2.86 3.20 3.46 3.45 - 3.29 3.17 3.14 3.18 3.21 3.27 3.66 4.02 - 4.31 4.50 4.25 3.76 3.78 4.23 4.25 3.78 - 3.73 4.04 4.01 4.19 4.29 3.60 3.30 3.81 - 4.19 4.22 4.00 3.58 3.32 3.50 3.71 3.57 - 3.03 2.60 2.88 3.44 3.87 3.76 2.92 2.39 - 2.95 3.70 3.58 3.13 3.06 3.03 2.50 2.15 - 2.84 3.62 3.71 4.07 5.40 6.41 5.99 5.94 - 7.00 7.53 6.89 6.87 6.98 7.00 7.64 8.81 - 9.39 9.41 9.63 10.0 10.2 10.4 10.3 11.9 - 13.8 14.7 14.7 14.0 14.0 14.9 15.2 15.0 - 14.2 13.9 13.9 14.8 17.0 17.2 15.2 13.9 - 13.7 14.1 14.6 15.7 17.4 18.6 18.4 19.9 - 25.1 31.0 34.0 29.8 24.0 21.3 26.7 38.9 - 46.2 38.9 24.2 17.6 19.4 22.8 22.9 18.7 - 15.0 13.0 11.8 11.0 10.4 9.31 8.83 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.03 1.07 1.11 1.13 1.13 1.12 1.10 - 1.08 1.07 1.08 1.07 1.06 1.06 1.07 1.08 - 1.17 1.56 1.99 2.11 2.08 2.08 1.98 1.77 - 1.63 1.66 1.89 2.09 2.02 1.83 1.77 1.85 - 1.95 1.97 1.93 1.85 1.79 1.87 1.90 1.74 - 1.68 1.83 1.89 1.83 1.82 1.91 1.98 1.98 - 1.93 1.90 1.89 1.91 1.91 1.93 2.03 2.12 - 2.18 2.23 2.17 2.05 2.06 2.16 2.17 2.06 - 2.05 2.12 2.11 2.16 2.18 2.01 1.94 2.06 - 2.15 2.16 2.11 2.01 1.94 1.99 2.04 2.00 - 1.87 1.75 1.83 1.97 2.08 2.05 1.84 1.68 - 1.85 2.04 2.01 1.89 1.87 1.87 1.72 1.61 - 1.81 2.02 2.04 2.10 2.35 2.54 2.40 2.28 - 2.45 2.51 2.25 2.10 2.10 2.10 2.10 2.14 - 2.17 2.17 2.19 2.23 2.25 2.26 2.26 2.43 - 2.62 2.71 2.70 2.64 2.64 2.73 2.76 2.73 - 2.66 2.63 2.63 2.72 2.91 2.93 2.75 2.63 - 2.61 2.65 2.69 2.79 2.95 3.05 3.04 3.15 - 3.55 3.94 4.12 3.86 3.47 3.27 3.65 4.41 - 4.81 4.43 3.54 3.16 3.55 4.02 3.94 3.27 - 2.75 2.59 2.51 2.44 2.36 2.24 2.18 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.184 1.38 3.72 4.70 5.58 5.75 4.49 3.83 - 3.09 3.15 4.22 4.91 4.39 3.30 3.83 3.82 - 3.89 3.98 3.81 3.26 2.96 3.87 4.42 3.18 - 2.61 3.61 3.73 3.23 3.38 3.97 4.69 5.33 - 5.06 3.89 3.12 3.00 3.96 3.91 4.62 4.26 - 4.60 4.78 4.91 4.29 4.33 4.64 5.09 4.51 - 3.91 4.33 4.36 5.07 5.07 3.26 2.69 3.83 - 4.51 5.22 5.44 4.30 3.38 3.59 4.02 3.54 - 2.75 2.36 3.24 4.37 4.97 4.55 2.98 2.08 - 3.06 3.84 3.47 3.72 4.69 4.78 3.27 1.88 - 2.71 4.63 4.49 5.46 6.61 6.75 6.28 6.79 - 8.07 8.58 7.60 6.78 6.88 7.31 7.30 8.79 - 9.08 8.56 8.80 9.25 9.55 9.67 9.58 10.8 - 12.7 12.9 12.6 12.9 13.6 13.9 13.8 14.0 - 13.5 12.9 12.7 14.0 16.5 16.9 14.9 12.8 - 11.9 11.1 12.1 12.5 12.6 13.7 15.1 17.9 - 21.5 25.4 27.4 25.0 22.2 24.6 38.5 66.2 - 80.6 59.7 31.8 27.6 46.1 72.4 75.2 46.2 - 22.2 18.9 25.0 28.8 24.0 15.6 8.42 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.08 1.49 2.06 2.25 2.43 2.47 2.20 2.04 - 1.85 1.87 2.13 2.29 2.18 1.91 2.04 2.04 - 2.06 2.08 2.04 1.90 1.81 2.05 2.18 1.88 - 1.72 1.99 2.02 1.89 1.93 2.08 2.24 2.38 - 2.32 2.06 1.86 1.83 2.07 2.06 2.23 2.14 - 2.22 2.26 2.29 2.15 2.16 2.23 2.33 2.20 - 2.06 2.16 2.17 2.32 2.33 1.90 1.74 2.04 - 2.20 2.36 2.40 2.15 1.93 1.98 2.09 1.97 - 1.76 1.64 1.89 2.17 2.30 2.21 1.82 1.55 - 1.84 2.04 1.95 2.02 2.24 2.26 1.90 1.49 - 1.75 2.23 2.19 2.39 2.59 2.60 2.50 2.58 - 2.81 2.86 2.59 2.35 2.36 2.43 2.24 2.18 - 2.13 2.07 2.09 2.15 2.18 2.19 2.18 2.32 - 2.52 2.54 2.51 2.54 2.61 2.64 2.62 2.64 - 2.59 2.54 2.51 2.64 2.87 2.90 2.72 2.53 - 2.44 2.35 2.46 2.50 2.51 2.61 2.75 2.99 - 3.28 3.57 3.70 3.54 3.33 3.51 4.39 5.76 - 6.38 5.56 4.27 4.53 6.34 8.15 8.23 6.08 - 3.73 3.36 3.86 4.12 3.79 3.11 2.32 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 36.1 25.2 32.8 69.7 122. 135. 69.1 - 27.6 38.2 89.8 127. 94.0 42.4 -1.00e+20 -### Errors - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 4.50 4.07 5.29 8.10 10.8 11.3 7.64 - 4.33 4.75 7.02 8.24 7.16 4.98 0.00 -### Corrected Counts - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.211 1.83 5.76 8.39 7.38 6.56 5.34 4.96 - 6.35 6.50 6.25 7.08 7.06 8.83 10.2 9.75 - 7.86 5.89 6.05 5.63 5.00 4.33 3.89 4.99 - 5.55 5.04 4.47 4.20 4.73 7.72 9.70 8.35 - 5.30 4.64 5.25 5.07 5.19 4.76 6.28 6.49 - 5.94 5.94 6.18 5.28 5.07 5.16 4.68 5.31 - 6.74 5.79 3.79 3.38 4.59 5.75 7.69 8.55 - 8.23 7.70 6.84 6.35 6.85 6.09 4.25 4.36 - 6.82 8.64 8.65 9.07 7.23 5.53 6.29 6.67 - 6.89 6.60 6.06 7.25 7.10 6.84 6.77 6.74 - 7.03 7.08 7.86 8.05 7.56 8.80 7.48 4.83 - 4.37 4.99 5.31 6.46 7.47 8.96 10.4 7.21 - 6.36 8.15 8.87 8.78 9.23 8.35 6.81 7.31 - 9.80 12.1 12.0 12.2 12.7 11.7 11.2 11.9 - 12.1 11.7 11.5 10.9 9.78 9.75 10.8 11.2 - 10.6 9.97 8.60 8.02 8.23 9.51 11.7 11.2 - 9.95 10.7 12.8 13.7 13.2 12.5 11.0 9.81 - 11.9 14.6 16.1 25.3 49.7 81.4 85.7 51.7 - 28.0 64.0 190. 301. 204. 72.6 22.2 -### Errors - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.08 1.60 2.45 2.90 2.72 2.56 2.31 2.23 - 2.52 2.55 2.50 2.66 2.66 2.97 3.20 3.12 - 2.80 2.43 2.46 2.37 2.24 2.08 1.97 2.23 - 2.36 2.24 2.11 2.05 2.17 2.78 3.11 2.89 - 2.30 2.15 2.29 2.25 2.28 2.18 2.51 2.55 - 2.44 2.44 2.49 2.30 2.25 2.27 2.16 2.30 - 2.60 2.41 1.95 1.84 2.14 2.40 2.77 2.92 - 2.87 2.77 2.61 2.52 2.62 2.47 2.06 2.09 - 2.61 2.94 2.94 3.01 2.69 2.35 2.51 2.58 - 2.63 2.57 2.46 2.69 2.67 2.61 2.60 2.60 - 2.65 2.66 2.80 2.84 2.75 2.97 2.74 2.20 - 2.09 2.23 2.30 2.54 2.73 2.98 3.15 2.37 - 1.84 2.01 2.10 2.09 2.14 2.04 1.84 1.91 - 2.21 2.46 2.44 2.47 2.52 2.41 2.37 2.44 - 2.46 2.42 2.40 2.33 2.21 2.21 2.33 2.37 - 2.30 2.23 2.07 2.00 2.03 2.18 2.41 2.36 - 2.23 2.31 2.53 2.62 2.57 2.50 2.35 2.23 - 2.55 2.93 3.29 4.54 6.70 8.70 8.82 6.51 - 4.19 5.84 9.89 12.4 10.2 6.17 3.47 diff --git a/test_data/IPTS-34735/exp816/Shared/collected.spe b/test_data/IPTS-34735/exp816/Shared/collected.spe deleted file mode 100644 index 414664d8..00000000 --- a/test_data/IPTS-34735/exp816/Shared/collected.spe +++ /dev/null @@ -1,989 +0,0 @@ - 20 177 -### H (arb) - 1.0000 1.0250 1.0500 1.0750 1.1000 1.1250 1.1500 1.1750 - 1.2000 1.2250 1.2500 1.2750 1.3000 1.3250 1.3500 1.3750 - 1.4000 1.4250 1.4500 1.4750 0.0000 -### E - 0.50000 0.60000 0.79990 0.99980 1.2000 1.4000 1.6000 1.7999 - 2.0000 2.1999 2.3999 2.6001 2.7998 3.0002 3.2002 3.3998 - 3.5999 3.8002 3.9998 4.2000 4.4000 4.6000 4.8001 5.0001 - 5.1998 5.4001 5.5999 5.7998 5.9999 6.2001 6.4000 6.6002 - 6.7998 6.9999 7.2002 7.3997 7.6002 7.7999 7.9999 8.2001 - 8.3998 8.6001 8.7999 8.9999 9.2000 9.4002 9.5996 9.8001 - 10.000 10.200 10.400 10.600 10.800 11.000 11.200 11.400 - 11.600 11.800 12.000 12.200 12.400 12.600 12.800 13.000 - 13.200 13.400 13.600 13.800 14.000 14.200 14.400 14.600 - 14.800 15.000 15.200 15.400 15.600 15.800 16.000 16.200 - 16.400 16.600 16.800 17.000 17.201 17.400 17.600 17.800 - 18.000 18.199 18.401 18.600 18.799 19.001 19.200 19.400 - 19.600 19.800 20.000 20.200 20.400 20.600 20.799 21.000 - 21.200 21.400 21.600 21.800 22.000 22.200 22.400 22.601 - 22.800 23.000 23.200 23.400 23.600 23.800 24.000 24.200 - 24.400 24.600 24.800 25.000 25.200 25.400 25.600 25.801 - 26.000 26.200 26.399 26.600 26.800 27.001 27.200 27.400 - 27.600 27.800 28.001 28.199 28.399 28.601 28.800 29.000 - 29.200 29.399 29.599 29.801 30.002 30.500 30.999 31.501 - 31.999 32.500 33.000 33.500 34.003 34.500 35.000 35.499 - 36.003 36.499 37.000 37.503 38.001 39.000 40.003 40.499 - 41.000 41.499 42.005 42.500 43.000 43.499 44.005 44.500 - 45.005 0.0000 -### Corrected Counts - -1.00e+20 1.20e+03 577. 404. 372. 316. 287. 269. - 242. 302. 404. 378. 379. 284. 287. 288. - 266. 240. 218. 236. 227. 172. 158. 166. - 153. 129. 129. 118. 100. 98.0 95.0 94.0 - 100. 79.0 66.0 58.0 75.0 74.0 65.0 57.0 - 57.0 44.0 45.0 41.0 38.0 36.0 31.0 27.0 - 31.0 35.0 26.0 24.0 23.0 31.0 22.0 31.0 - 22.0 20.0 22.0 25.0 19.0 21.0 16.0 24.0 - 11.0 16.0 11.0 20.0 11.0 13.0 20.0 20.0 - 11.0 15.0 8.00 14.0 6.00 11.0 18.0 12.0 - 10.0 9.00 8.00 14.0 12.0 13.0 14.0 11.0 - 11.0 10.0 6.00 16.0 22.0 13.0 7.00 15.0 - 17.0 9.00 14.0 12.0 14.0 11.0 11.0 11.0 - 14.0 12.0 11.0 19.0 6.00 17.0 14.0 14.0 - 17.0 11.0 18.0 15.0 16.0 11.0 20.0 20.0 - 21.0 12.0 15.0 10.0 12.0 14.0 17.0 19.0 - 4.00 18.0 12.0 10.0 15.0 13.0 6.00 10.0 - 11.0 13.0 17.0 6.00 10.0 6.00 8.00 7.00 - 18.0 16.0 10.0 80.0 524. -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -### Errors - 0.00 34.6 24.0 20.1 19.3 17.8 16.9 16.4 - 15.6 17.4 20.1 19.4 19.5 16.9 16.9 17.0 - 16.3 15.5 14.8 15.4 15.1 13.1 12.6 12.9 - 12.4 11.4 11.4 10.9 10.0 9.90 9.75 9.70 - 10.0 8.89 8.12 7.62 8.66 8.60 8.06 7.55 - 7.55 6.63 6.71 6.40 6.16 6.00 5.57 5.20 - 5.57 5.92 5.10 4.90 4.80 5.57 4.69 5.57 - 4.69 4.47 4.69 5.00 4.36 4.58 4.00 4.90 - 3.32 4.00 3.32 4.47 3.32 3.61 4.47 4.47 - 3.32 3.87 2.83 3.74 2.45 3.32 4.24 3.46 - 3.16 3.00 2.83 3.74 3.46 3.61 3.74 3.32 - 3.32 3.16 2.45 4.00 4.69 3.61 2.65 3.87 - 4.12 3.00 3.74 3.46 3.74 3.32 3.32 3.32 - 3.74 3.46 3.32 4.36 2.45 4.12 3.74 3.74 - 4.12 3.32 4.24 3.87 4.00 3.32 4.47 4.47 - 4.58 3.46 3.87 3.16 3.46 3.74 4.12 4.36 - 2.00 4.24 3.46 3.16 3.87 3.61 2.45 3.16 - 3.32 3.61 4.12 2.45 3.16 2.45 2.83 2.65 - 4.24 4.00 3.16 8.94 22.9 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Corrected Counts - 0.00 0.00 0.00 61.0 63.0 55.9 61.2 68.0 - 76.0 82.4 90.9 105. 124. 148. 179. 203. - 213. 212. 206. 199. 193. 188. 181. 171. - 153. 139. 134. 135. 129. 118. 111. 104. - 94.0 80.8 69.7 66.2 68.8 73.1 72.3 66.8 - 61.6 55.6 49.6 45.8 44.7 45.0 44.0 40.4 - 34.0 30.2 29.2 30.4 31.8 30.3 26.0 24.9 - 25.4 25.8 24.3 21.7 20.5 19.5 18.1 16.8 - 18.1 18.0 16.7 15.6 16.8 18.3 18.2 16.6 - 14.8 13.8 14.9 15.4 14.8 14.0 15.0 15.8 - 15.1 14.5 14.7 15.7 15.1 12.3 9.70 8.65 - 9.25 9.65 10.7 11.1 11.0 11.0 13.2 14.8 - 15.7 16.0 16.0 14.4 12.8 11.7 11.5 12.5 - 14.5 15.3 15.3 15.1 15.7 15.5 14.0 12.5 - 11.7 11.7 10.8 10.7 12.5 15.7 18.7 19.1 - 18.2 16.8 16.7 20.3 22.6 19.5 15.8 14.4 - 16.0 15.6 15.5 16.0 16.7 16.5 15.5 13.9 - 12.3 11.3 11.8 12.3 11.5 9.64 7.70 6.50 - 5.90 5.75 5.85 6.00 6.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Errors - 1.00 1.00 1.00 7.81 7.94 5.28 5.52 5.82 - 6.15 6.41 6.73 7.20 7.84 8.57 9.42 10.0 - 10.3 10.3 10.1 9.97 9.82 9.68 9.51 9.25 - 8.72 8.32 8.20 8.21 8.02 7.67 7.45 7.20 - 6.84 6.34 5.89 5.75 5.86 6.04 6.00 5.78 - 5.54 5.26 4.97 4.78 4.73 4.74 4.68 4.48 - 4.11 3.88 3.82 3.90 3.98 3.87 3.60 3.52 - 3.56 3.59 3.47 3.29 3.20 3.12 3.00 2.89 - 3.01 2.99 2.88 2.79 2.89 3.03 3.01 2.88 - 2.71 2.62 2.73 2.77 2.72 2.64 2.73 2.80 - 2.74 2.69 2.71 2.80 2.74 2.44 2.17 2.07 - 2.15 2.19 2.30 2.36 2.34 2.34 2.56 2.71 - 2.80 2.83 2.83 2.68 2.52 2.42 2.39 2.49 - 2.69 2.76 2.77 2.75 2.80 2.78 2.64 2.49 - 2.42 2.42 2.31 2.30 2.47 2.78 3.06 3.09 - 3.01 2.89 2.87 3.14 3.34 3.09 2.77 2.68 - 2.83 2.79 2.78 2.83 2.89 2.87 2.78 2.63 - 2.47 2.38 2.42 2.48 2.38 2.18 1.95 1.80 - 1.72 1.69 1.71 2.45 2.45 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 -### Corrected Counts - 110. 95.6 66.8 38.0 31.6 25.2 22.8 24.4 - 26.0 24.0 22.0 20.6 19.8 19.0 19.4 19.8 - 22.8 28.4 34.0 42.4 50.8 60.0 70.0 80.0 - 81.6 83.2 97.0 123. 149. 139. 130. 122. - 115. 108. 105. 102. 97.0 89.0 81.0 83.0 - 85.0 82.6 75.8 69.0 62.2 55.4 50.6 47.8 - 45.0 40.2 35.4 33.6 34.8 36.0 33.6 31.2 - 27.8 23.4 19.0 20.6 22.2 20.6 15.8 11.0 - 14.6 18.2 18.0 14.0 10.0 11.2 12.4 11.8 - 9.41 7.00 10.2 13.4 14.2 12.6 11.0 9.40 - 7.80 8.40 11.2 14.0 13.6 13.2 12.0 10.0 - 8.00 11.2 14.4 14.6 11.8 9.00 11.4 13.8 - 13.8 11.4 9.00 10.6 12.2 12.8 12.4 12.0 - 14.4 16.8 16.4 13.2 10.0 9.60 9.20 9.40 - 10.2 11.0 13.0 15.0 16.6 17.8 19.0 19.0 - 19.0 17.6 14.8 12.0 14.4 16.8 16.8 14.4 - 12.0 12.8 13.6 14.2 14.6 15.0 12.6 10.2 - 9.00 9.00 9.00 8.20 7.40 7.20 7.60 8.00 - 7.60 7.20 7.99 10.0 12.0 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -### Errors - 10.5 9.62 7.89 6.17 5.57 4.98 4.77 4.94 - 5.10 4.89 4.69 4.54 4.45 4.36 4.40 4.45 - 4.74 5.29 5.83 6.47 7.10 7.72 8.33 8.94 - 9.03 9.12 9.77 11.0 12.2 11.8 11.4 11.0 - 10.7 10.4 10.3 10.1 9.84 9.42 9.00 9.11 - 9.22 9.08 8.69 8.31 7.87 7.43 7.11 6.91 - 6.71 6.32 5.94 5.80 5.90 6.00 5.79 5.58 - 5.25 4.81 4.36 4.53 4.71 4.50 3.91 3.32 - 3.78 4.24 4.21 3.69 3.16 3.34 3.52 3.41 - 3.03 2.65 3.14 3.63 3.76 3.54 3.32 3.05 - 2.78 2.87 3.30 3.74 3.69 3.63 3.45 3.14 - 2.83 3.30 3.77 3.80 3.40 3.00 3.35 3.70 - 3.70 3.35 3.00 3.24 3.48 3.58 3.52 3.46 - 3.78 4.09 4.03 3.59 3.16 3.10 3.03 3.06 - 3.19 3.32 3.59 3.86 4.07 4.22 4.36 4.36 - 4.36 4.18 3.82 3.46 3.77 4.09 4.09 3.78 - 3.46 3.58 3.69 3.77 3.82 3.87 3.52 3.17 - 3.00 3.00 3.00 2.86 2.72 2.68 2.76 2.83 - 2.76 2.68 2.81 3.14 3.46 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Corrected Counts - 0.00 0.00 0.00 29.0 27.0 21.6 20.6 19.3 - 17.2 16.9 17.8 19.1 19.8 18.7 18.9 18.8 - 18.0 17.2 19.0 19.2 16.8 14.9 15.0 17.0 - 18.2 18.3 18.3 18.3 18.2 19.9 23.9 28.1 - 31.1 32.5 35.9 40.8 46.8 53.5 60.5 66.3 - 70.9 71.8 69.2 66.0 67.4 66.8 67.4 70.2 - 74.7 68.9 59.2 50.7 45.3 42.8 42.6 42.8 - 42.7 41.8 40.3 37.3 35.4 33.9 32.2 30.0 - 30.8 31.1 29.9 26.7 22.8 21.0 21.5 22.6 - 22.9 22.5 21.3 20.1 19.1 18.2 17.3 19.7 - 20.7 18.8 15.2 14.0 14.8 14.0 13.3 13.8 - 14.7 14.8 15.7 16.5 16.8 15.7 14.4 12.5 - 11.0 11.0 14.0 16.4 14.6 11.8 10.4 12.3 - 15.0 15.4 13.4 10.5 9.75 11.9 13.5 14.7 - 15.3 15.5 13.3 11.5 11.6 13.2 14.2 13.1 - 13.1 14.6 16.6 17.2 14.7 13.3 13.0 14.0 - 15.8 17.0 15.5 14.2 14.6 17.2 16.9 16.3 - 16.8 17.9 17.2 14.1 11.9 11.1 11.1 10.5 - 10.1 10.3 10.2 10.2 9.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Errors - 1.00 1.00 1.00 5.39 5.19 3.29 3.21 3.10 - 2.94 2.90 2.98 3.09 3.14 3.06 3.07 3.06 - 2.99 2.93 3.06 3.08 2.89 2.72 2.73 2.91 - 3.01 3.03 3.02 3.02 3.02 3.14 3.43 3.73 - 3.94 4.03 4.23 4.50 4.82 5.16 5.49 5.75 - 5.95 5.99 5.87 5.74 5.80 5.78 5.80 5.92 - 6.11 5.85 5.42 5.02 4.76 4.62 4.61 4.63 - 4.62 4.57 4.48 4.31 4.21 4.12 4.01 3.87 - 3.92 3.94 3.86 3.65 3.36 3.23 3.28 3.36 - 3.38 3.35 3.26 3.17 3.09 3.02 2.94 3.11 - 3.19 3.04 2.73 2.64 2.72 2.64 2.57 2.61 - 2.72 2.72 2.79 2.87 2.89 2.80 2.67 2.49 - 2.34 2.33 2.60 2.85 2.67 2.39 2.28 2.46 - 2.73 2.75 2.56 2.28 2.20 2.43 2.60 2.71 - 2.76 2.78 2.56 2.39 2.40 2.56 2.67 2.55 - 2.55 2.69 2.87 2.93 2.70 2.57 2.55 2.64 - 2.79 2.90 2.77 2.64 2.69 2.93 2.89 2.85 - 2.89 2.98 2.93 2.63 2.43 2.36 2.35 2.29 - 2.24 2.27 2.26 3.19 3.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 28.0 22.0 16.0 14.6 17.8 - 21.0 15.8 10.6 9.80 13.4 17.0 18.2 19.4 - 17.8 13.4 9.00 10.6 12.2 13.6 14.8 16.0 - 17.6 19.2 17.6 12.8 8.00 10.4 12.8 13.8 - 13.4 13.0 16.2 19.4 20.0 18.0 16.0 20.8 - 25.6 29.6 32.8 36.0 35.2 34.4 33.2 31.6 - 30.0 29.6 29.2 30.8 34.4 38.0 38.8 39.6 - 43.2 49.6 56.0 47.6 39.2 37.6 42.8 48.0 - 39.2 30.4 27.2 29.6 32.0 28.0 24.0 21.6 - 20.8 20.0 19.2 18.4 19.0 21.0 23.0 20.6 - 18.2 17.8 19.4 21.0 19.0 17.0 16.0 16.0 - 16.0 13.6 11.2 10.8 12.4 14.0 12.0 10.0 - 9.40 10.2 11.0 11.4 11.8 12.2 12.6 13.0 - 11.4 9.80 9.40 10.2 11.0 11.8 12.6 13.2 - 13.6 14.0 12.0 9.99 11.0 15.0 19.0 18.2 - 17.4 15.2 11.6 8.01 8.80 9.60 10.8 12.4 - 14.0 14.4 14.8 15.2 15.6 16.0 17.6 19.2 - 19.8 19.4 19.0 17.0 15.0 14.8 16.4 18.0 - 16.0 14.0 12.2 10.6 8.99 6.00 9.00 8.00 - 5.00 8.00 8.00 10.0 829. -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -### Errors - 0.00 0.00 0.00 5.29 4.62 3.94 3.80 4.19 - 4.58 3.88 3.18 3.09 3.60 4.12 4.26 4.40 - 4.18 3.59 3.00 3.24 3.48 3.68 3.84 4.00 - 4.19 4.38 4.14 3.49 2.83 3.19 3.56 3.71 - 3.66 3.61 4.00 4.39 4.47 4.23 4.00 4.52 - 5.03 5.43 5.72 6.00 5.93 5.86 5.76 5.62 - 5.48 5.44 5.40 5.54 5.85 6.16 6.23 6.29 - 6.56 7.02 7.48 6.86 6.23 6.12 6.52 6.93 - 6.20 5.47 5.21 5.43 5.66 5.27 4.88 4.65 - 4.56 4.47 4.38 4.29 4.35 4.57 4.80 4.53 - 4.26 4.22 4.40 4.58 4.35 4.12 4.00 4.00 - 4.00 3.67 3.33 3.28 3.51 3.74 3.45 3.15 - 3.06 3.19 3.32 3.38 3.43 3.49 3.55 3.61 - 3.36 3.12 3.06 3.19 3.32 3.43 3.55 3.63 - 3.69 3.74 3.45 3.15 3.27 3.81 4.36 4.26 - 4.17 3.86 3.35 2.83 2.96 3.09 3.28 3.51 - 3.74 3.79 3.85 3.90 3.95 4.00 4.19 4.38 - 4.45 4.40 4.36 4.11 3.87 3.84 4.04 4.24 - 3.99 3.73 3.49 3.24 3.00 2.45 3.00 2.83 - 2.24 2.83 2.83 3.16 28.8 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Corrected Counts - 0.00 0.00 0.00 39.0 30.6 21.9 21.3 21.0 - 16.7 13.0 11.7 12.3 14.3 17.8 18.0 15.5 - 12.4 9.90 8.50 9.30 10.1 10.1 9.90 11.5 - 13.7 13.5 12.6 11.9 11.3 9.65 10.3 12.0 - 13.3 12.5 11.9 11.3 10.5 9.80 10.0 10.4 - 11.7 13.3 14.2 12.8 10.9 10.0 10.1 11.3 - 13.5 13.9 13.2 14.6 18.5 22.5 20.9 19.7 - 19.9 21.7 25.2 28.8 30.1 31.4 33.3 34.2 - 30.9 30.0 30.8 32.0 31.8 32.5 31.3 30.3 - 31.2 35.3 35.9 35.1 33.3 30.9 28.8 28.1 - 27.8 27.4 26.2 23.0 19.8 18.7 18.7 19.4 - 20.7 21.7 20.2 18.8 19.3 22.8 22.8 20.5 - 18.7 18.4 19.0 18.8 17.4 15.4 13.8 14.3 - 14.3 12.9 12.0 12.4 14.2 14.4 13.9 14.3 - 15.7 16.5 14.5 13.4 13.4 14.3 15.5 15.5 - 13.7 12.0 11.6 13.2 13.8 13.4 12.9 12.7 - 12.5 12.9 13.9 14.1 13.5 13.5 15.7 17.5 - 18.0 17.1 15.5 15.9 14.8 13.0 12.1 14.3 - 16.6 17.7 17.6 16.5 14.5 13.5 10.0 10.8 - 12.5 9.00 14.5 10.8 4.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Errors - 1.00 1.00 1.00 6.25 5.44 3.28 3.25 3.22 - 2.85 2.53 2.42 2.47 2.66 2.97 2.98 2.76 - 2.47 2.22 2.06 2.14 2.24 2.25 2.21 2.37 - 2.61 2.59 2.50 2.43 2.36 2.19 2.24 2.42 - 2.58 2.50 2.44 2.37 2.29 2.21 2.23 2.28 - 2.41 2.57 2.66 2.51 2.33 2.24 2.25 2.37 - 2.59 2.63 2.57 2.67 2.99 3.35 3.23 3.14 - 3.15 3.28 3.53 3.79 3.88 3.95 4.07 4.13 - 3.92 3.86 3.92 4.00 3.98 4.03 3.94 3.87 - 3.94 4.19 4.23 4.19 4.08 3.93 3.79 3.75 - 3.73 3.70 3.61 3.38 3.14 3.06 3.06 3.11 - 3.22 3.29 3.17 3.05 3.10 3.36 3.36 3.19 - 3.05 3.03 3.08 3.07 2.94 2.76 2.63 2.66 - 2.66 2.53 2.44 2.49 2.66 2.68 2.63 2.67 - 2.79 2.87 2.69 2.58 2.59 2.67 2.78 2.78 - 2.60 2.44 2.41 2.57 2.63 2.59 2.54 2.52 - 2.50 2.54 2.63 2.65 2.59 2.59 2.79 2.95 - 2.99 2.92 2.78 2.82 2.69 2.52 2.45 2.65 - 2.88 2.97 2.97 2.87 2.68 2.60 2.23 2.29 - 2.48 2.12 2.69 2.32 2.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 36.0 29.6 23.2 20.4 21.2 - 22.0 22.8 23.6 21.8 17.4 13.0 12.2 11.4 - 11.4 12.2 13.0 13.4 13.8 13.4 12.2 11.0 - 9.80 8.60 7.60 6.80 6.00 10.8 15.6 17.6 - 16.8 16.0 15.6 15.2 14.6 13.8 13.0 9.40 - 5.81 4.80 6.40 8.00 9.20 10.4 11.4 12.2 - 13.0 16.2 19.4 19.0 15.0 11.0 14.6 18.2 - 18.8 16.4 14.0 13.2 12.4 12.8 14.4 16.0 - 18.0 20.0 22.8 26.4 30.0 30.4 30.8 29.8 - 27.4 25.0 26.2 27.4 26.0 22.0 18.0 21.2 - 24.4 26.2 26.6 27.0 27.4 27.8 27.0 25.0 - 23.0 24.2 25.4 25.0 23.0 21.0 22.6 24.2 - 25.6 26.8 28.0 24.4 20.8 17.4 14.2 11.0 - 16.2 21.4 21.2 15.6 10.0 14.0 18.0 19.2 - 17.6 16.0 14.4 12.8 13.6 16.8 20.0 15.6 - 11.2 9.40 10.2 11.0 9.41 7.81 9.00 13.0 - 17.0 15.0 13.0 12.8 14.4 16.0 16.0 16.0 - 15.0 13.0 11.0 12.6 14.2 15.2 15.6 16.0 - 14.8 13.6 12.6 11.8 11.0 17.0 12.0 13.0 - 20.0 9.00 9.00 8.00 11.9 4.00 8.00 11.0 - 750. -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -### Errors - 0.00 0.00 0.00 6.00 5.39 4.78 4.52 4.60 - 4.69 4.77 4.86 4.64 4.12 3.61 3.49 3.37 - 3.37 3.49 3.61 3.66 3.71 3.66 3.49 3.32 - 3.12 2.93 2.75 2.60 2.45 3.17 3.88 4.19 - 4.10 4.00 3.95 3.90 3.82 3.71 3.60 2.96 - 2.32 2.17 2.50 2.83 3.02 3.22 3.37 3.49 - 3.61 4.00 4.39 4.33 3.82 3.32 3.78 4.24 - 4.33 4.03 3.74 3.63 3.52 3.57 3.79 4.00 - 4.23 4.47 4.76 5.12 5.48 5.51 5.55 5.45 - 5.23 5.00 5.12 5.23 5.08 4.66 4.24 4.59 - 4.93 5.12 5.16 5.20 5.23 5.27 5.19 4.99 - 4.80 4.92 5.04 5.00 4.79 4.58 4.75 4.92 - 5.06 5.17 5.29 4.92 4.54 4.15 3.74 3.32 - 3.95 4.58 4.55 3.86 3.16 3.69 4.21 4.38 - 4.19 4.00 3.79 3.57 3.67 4.07 4.47 3.88 - 3.29 3.06 3.19 3.32 3.05 2.78 2.94 3.53 - 4.12 3.86 3.60 3.57 3.79 4.00 4.00 4.00 - 3.86 3.59 3.32 3.54 3.76 3.90 3.95 4.00 - 3.84 3.68 3.55 3.43 3.32 4.12 3.46 3.61 - 4.47 3.00 3.00 2.83 3.45 2.00 2.83 3.32 - 27.4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Corrected Counts - 0.00 0.00 0.00 26.0 22.8 19.7 17.9 17.3 - 17.2 14.7 13.2 13.3 13.9 13.2 13.2 13.4 - 12.9 12.1 12.5 14.3 14.2 12.6 10.8 10.3 - 10.4 11.1 12.2 13.1 12.5 11.3 11.0 10.9 - 10.6 9.75 9.15 8.85 8.75 8.75 8.75 9.35 - 11.4 12.9 12.4 10.0 9.80 10.1 10.1 9.80 - 10.0 10.0 9.10 7.90 7.15 7.75 11.0 13.7 - 14.5 13.1 10.5 9.70 11.6 15.0 18.1 18.5 - 15.7 13.2 11.5 10.9 11.5 12.1 12.5 13.8 - 15.6 17.2 17.6 16.7 15.1 14.3 16.2 18.1 - 18.0 17.9 17.9 17.2 16.8 18.1 20.0 22.2 - 24.7 27.7 28.4 27.2 25.7 25.2 23.9 22.0 - 22.3 24.9 27.5 25.1 21.9 20.0 20.0 22.3 - 21.1 18.2 16.2 15.8 16.2 17.2 18.5 18.8 - 17.6 15.0 15.8 17.7 17.7 15.8 13.7 13.9 - 13.1 11.5 10.0 10.0 11.2 11.2 10.7 10.2 - 10.3 10.9 11.9 12.4 12.2 12.0 12.2 11.6 - 10.5 9.10 8.50 9.89 11.9 14.0 15.4 15.2 - 13.7 13.5 14.8 16.8 18.0 15.8 18.2 11.2 - 12.7 15.7 17.0 17.2 9.48 5.00 9.75 11.8 - 7.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Errors - 1.00 1.00 1.00 5.10 4.76 3.13 2.98 2.94 - 2.93 2.69 2.56 2.57 2.63 2.57 2.57 2.59 - 2.54 2.45 2.49 2.67 2.65 2.50 2.32 2.26 - 2.29 2.35 2.46 2.55 2.49 2.37 2.35 2.33 - 2.30 2.20 2.14 2.10 2.09 2.09 2.09 2.16 - 2.36 2.50 2.47 2.23 2.21 2.24 2.24 2.21 - 2.23 2.23 2.13 1.98 1.88 1.96 2.30 2.60 - 2.69 2.54 2.28 2.20 2.38 2.71 3.00 3.04 - 2.79 2.56 2.39 2.33 2.39 2.46 2.50 2.61 - 2.79 2.94 2.97 2.88 2.74 2.66 2.83 3.00 - 3.00 2.99 2.99 2.93 2.90 3.00 3.16 3.32 - 3.51 3.72 3.76 3.69 3.58 3.55 3.45 3.31 - 3.33 3.51 3.71 3.53 3.30 3.15 3.16 3.33 - 3.22 3.00 2.84 2.81 2.85 2.93 3.04 3.06 - 2.96 2.73 2.79 2.96 2.97 2.79 2.62 2.64 - 2.55 2.39 2.23 2.23 2.36 2.36 2.31 2.26 - 2.26 2.33 2.44 2.49 2.47 2.45 2.47 2.41 - 2.28 2.12 2.06 2.21 2.43 2.64 2.77 2.76 - 2.61 2.60 2.72 2.89 3.00 2.80 3.02 2.36 - 2.52 2.81 2.91 2.93 2.18 1.54 2.17 2.42 - 2.65 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 33.0 22.6 12.2 8.60 11.8 - 15.0 14.2 13.4 12.0 10.0 8.00 10.0 12.0 - 12.4 11.2 10.0 10.0 10.0 10.0 10.0 10.0 - 9.20 8.40 7.80 7.40 7.00 9.40 11.8 13.0 - 13.0 13.0 9.80 6.60 5.80 7.40 9.00 7.80 - 6.60 7.00 9.00 11.0 11.8 12.6 12.8 12.4 - 12.0 12.8 13.6 12.6 9.80 7.00 8.60 10.2 - 10.4 9.20 8.00 9.20 10.4 10.4 9.20 8.00 - 8.00 8.00 8.00 8.00 8.00 11.2 14.4 14.8 - 12.4 10.0 10.4 10.8 12.0 14.0 16.0 15.2 - 14.4 14.2 14.6 15.0 14.2 13.4 14.2 16.6 - 19.0 20.2 21.4 21.4 20.2 19.0 21.0 23.0 - 24.4 25.2 26.0 27.2 28.4 28.2 26.6 25.0 - 19.8 14.6 14.0 18.0 22.0 17.2 12.4 11.4 - 14.2 17.0 14.2 11.4 9.40 8.20 7.00 11.0 - 15.0 15.4 12.2 9.00 8.60 8.20 9.40 12.2 - 15.0 12.2 9.40 9.20 11.6 14.0 12.4 10.8 - 10.0 10.0 10.0 10.4 10.8 12.0 14.0 16.0 - 14.4 12.8 12.8 14.4 16.0 14.0 18.0 25.0 - 15.0 13.0 26.0 15.0 8.98 6.00 11.0 10.0 - 7.99 7.00 10.0 13.0 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -### Errors - 0.00 0.00 0.00 5.75 4.51 3.27 2.89 3.38 - 3.87 3.77 3.66 3.45 3.14 2.83 3.14 3.45 - 3.52 3.34 3.16 3.16 3.16 3.16 3.16 3.16 - 3.03 2.90 2.79 2.72 2.65 3.03 3.41 3.61 - 3.61 3.61 3.06 2.51 2.39 2.69 3.00 2.78 - 2.56 2.62 2.97 3.32 3.43 3.55 3.58 3.52 - 3.46 3.58 3.69 3.52 3.08 2.65 2.91 3.18 - 3.22 3.02 2.83 3.02 3.22 3.22 3.02 2.83 - 2.83 2.83 2.83 2.83 2.83 3.30 3.77 3.83 - 3.50 3.16 3.22 3.29 3.45 3.73 4.00 3.90 - 3.79 3.77 3.82 3.87 3.77 3.66 3.76 4.06 - 4.36 4.49 4.62 4.62 4.49 4.36 4.57 4.79 - 4.94 5.02 5.10 5.21 5.33 5.31 5.15 5.00 - 4.39 3.77 3.71 4.20 4.69 4.08 3.47 3.36 - 3.74 4.12 3.74 3.35 3.06 2.85 2.65 3.24 - 3.83 3.90 3.45 3.00 2.93 2.86 3.04 3.46 - 3.87 3.45 3.04 3.01 3.38 3.74 3.51 3.28 - 3.16 3.16 3.16 3.22 3.29 3.45 3.73 4.00 - 3.79 3.57 3.57 3.79 4.00 3.74 4.24 5.00 - 3.87 3.61 5.10 3.87 3.00 2.45 3.32 3.16 - 2.83 2.65 3.16 3.61 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Corrected Counts - 0.00 0.00 0.00 24.0 20.8 23.5 20.1 17.8 - 17.0 15.6 13.2 11.5 11.4 13.3 13.3 14.0 - 15.4 16.2 14.3 11.4 10.7 11.2 12.0 13.0 - 14.6 14.2 12.8 11.3 11.5 12.3 13.1 13.3 - 12.5 10.5 9.50 9.70 9.50 8.60 8.00 9.80 - 11.0 11.1 9.90 7.50 6.10 6.95 9.05 11.3 - 12.2 10.5 9.40 9.80 10.8 10.3 7.85 7.40 - 8.00 8.55 7.75 8.55 10.2 11.7 12.2 11.2 - 10.3 10.3 11.0 12.3 14.5 14.1 13.2 12.4 - 11.3 8.25 8.85 11.8 13.7 13.6 13.0 13.6 - 13.0 12.0 11.2 11.0 12.8 16.5 20.3 22.5 - 21.5 18.9 17.2 16.1 15.0 13.0 11.4 12.1 - 13.5 14.7 15.5 18.3 19.2 17.2 14.0 13.0 - 15.6 17.5 17.7 16.3 13.5 12.7 13.4 15.0 - 17.5 21.5 21.9 19.7 18.3 18.0 17.0 13.4 - 11.9 12.2 13.4 14.0 14.4 14.8 15.1 15.5 - 16.5 16.3 15.4 14.4 13.5 11.5 9.50 9.30 - 9.30 8.50 6.50 5.90 6.65 8.66 11.3 13.2 - 13.2 12.1 9.95 7.90 7.52 14.0 15.5 11.3 - 12.5 9.75 15.8 14.3 16.5 11.5 9.00 7.25 - 9.02 12.5 11.8 10.0 0.00 0.00 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Errors - 1.00 1.00 1.00 4.90 4.54 3.41 3.16 2.98 - 2.91 2.79 2.55 2.37 2.38 2.57 2.57 2.63 - 2.77 2.84 2.65 2.38 2.31 2.36 2.45 2.55 - 2.70 2.65 2.51 2.38 2.40 2.48 2.56 2.58 - 2.49 2.28 2.17 2.20 2.17 2.05 1.99 2.20 - 2.34 2.35 2.21 1.90 1.73 1.84 2.10 2.36 - 2.47 2.26 2.15 2.20 2.32 2.25 1.96 1.90 - 1.99 2.07 1.97 2.04 2.25 2.42 2.47 2.35 - 2.26 2.27 2.34 2.47 2.68 2.64 2.56 2.48 - 2.35 2.00 2.05 2.39 2.60 2.60 2.55 2.61 - 2.55 2.45 2.37 2.35 2.51 2.84 3.17 3.35 - 3.27 3.07 2.93 2.84 2.73 2.53 2.38 2.44 - 2.58 2.71 2.78 3.01 3.07 2.91 2.62 2.54 - 2.78 2.95 2.98 2.84 2.58 2.51 2.59 2.73 - 2.94 3.26 3.29 3.13 3.02 2.99 2.89 2.57 - 2.43 2.46 2.59 2.65 2.68 2.72 2.75 2.78 - 2.87 2.85 2.77 2.69 2.59 2.38 2.17 2.15 - 2.14 2.04 1.79 1.71 1.81 2.05 2.34 2.57 - 2.57 2.44 2.21 1.97 1.93 2.64 2.78 2.37 - 2.50 2.21 2.79 2.65 2.87 2.40 2.12 1.90 - 2.12 2.49 2.41 3.16 1.00 1.00 1.00 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 -### Corrected Counts - 90.0 83.4 70.2 37.5 30.9 24.3 21.1 21.1 - 21.2 20.4 19.5 17.8 15.3 12.8 11.5 10.4 - 9.80 9.90 10.0 9.60 9.20 9.05 9.15 9.25 - 9.95 10.7 11.3 12.0 12.7 13.3 13.9 14.0 - 13.5 13.0 11.0 9.00 7.65 6.95 6.25 7.05 - 7.85 8.20 8.10 8.00 7.80 7.60 7.70 8.10 - 8.50 8.70 8.90 8.90 8.70 8.50 8.90 9.30 - 10.2 11.4 12.7 13.3 13.8 13.9 13.7 13.5 - 12.9 12.3 11.5 10.5 9.50 9.90 10.3 11.1 - 12.1 13.3 13.4 13.5 13.7 14.1 14.5 15.3 - 16.1 16.4 16.1 15.8 15.2 14.8 14.4 14.0 - 13.8 13.4 12.9 13.1 13.8 14.5 15.6 16.7 - 17.0 16.5 16.0 14.4 12.8 11.8 11.4 11.0 - 12.8 14.6 16.5 18.3 20.2 20.4 20.4 20.1 - 19.5 18.8 18.5 18.1 18.0 18.1 18.2 17.2 - 16.1 14.6 12.8 11.0 10.8 10.6 10.7 11.0 - 11.2 10.7 10.3 9.80 9.40 9.00 9.90 10.8 - 11.8 12.9 14.0 13.6 13.2 12.4 11.2 10.0 - 9.60 9.20 9.30 9.90 10.5 12.3 15.5 18.5 - 16.0 13.2 16.0 15.7 10.0 9.75 16.7 21.0 - 17.5 12.5 11.0 12.8 14.2 9.25 413. 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Errors - 9.49 9.03 8.11 4.18 3.79 3.42 3.24 3.25 - 3.26 3.18 3.10 2.95 2.71 2.48 2.36 2.26 - 2.21 2.22 2.23 2.19 2.14 2.12 2.14 2.15 - 2.22 2.30 2.38 2.45 2.52 2.58 2.64 2.64 - 2.58 2.53 2.29 2.06 1.89 1.81 1.75 1.86 - 1.97 2.02 2.00 1.99 1.97 1.94 1.96 2.01 - 2.06 2.08 2.11 2.11 2.08 2.06 2.10 2.15 - 2.24 2.37 2.51 2.56 2.62 2.64 2.62 2.60 - 2.53 2.47 2.38 2.28 2.17 2.21 2.25 2.33 - 2.45 2.57 2.58 2.59 2.61 2.65 2.69 2.76 - 2.83 2.86 2.83 2.80 2.76 2.71 2.68 2.65 - 2.62 2.58 2.54 2.55 2.62 2.68 2.78 2.88 - 2.91 2.86 2.82 2.66 2.51 2.41 2.37 2.34 - 2.50 2.66 2.83 3.00 3.18 3.19 3.19 3.17 - 3.12 3.06 3.04 3.01 3.00 3.01 3.02 2.91 - 2.80 2.67 2.50 2.34 2.31 2.29 2.30 2.34 - 2.37 2.31 2.26 2.21 2.16 2.12 2.21 2.30 - 2.41 2.52 2.64 2.60 2.56 2.48 2.35 2.22 - 2.18 2.14 2.15 2.22 2.29 2.47 2.77 3.04 - 2.80 2.57 2.81 2.78 2.19 2.17 2.84 3.24 - 2.92 2.49 2.35 2.52 2.67 2.15 20.3 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 -### Corrected Counts - 0.00 0.00 0.00 21.0 22.6 21.0 18.0 14.6 - 14.0 16.2 17.3 17.9 17.8 16.7 15.3 14.3 - 13.6 13.4 13.0 13.6 15.1 15.3 14.0 12.8 - 12.6 12.3 12.7 13.6 13.7 12.7 11.0 9.50 - 8.70 8.50 8.50 9.70 11.2 12.3 12.2 11.2 - 10.4 9.40 8.30 7.50 9.30 9.90 9.30 8.35 - 8.75 8.75 8.90 9.80 11.2 12.0 12.8 12.4 - 11.2 10.3 11.2 10.8 9.25 8.05 7.85 8.25 - 8.65 9.05 9.35 9.55 9.75 11.3 13.1 13.7 - 13.0 11.8 11.6 11.4 12.0 13.6 15.7 15.3 - 14.5 13.7 12.8 10.7 10.1 11.2 12.0 12.2 - 13.2 14.6 13.6 12.9 13.4 15.0 14.2 13.1 - 12.4 12.3 12.7 12.3 12.2 12.8 13.5 13.7 - 13.2 12.2 12.1 12.7 13.5 13.3 14.0 14.1 - 12.9 10.3 9.05 9.05 10.2 12.1 13.8 14.0 - 14.0 14.5 15.3 15.5 14.1 13.9 14.2 14.7 - 15.5 16.1 14.5 12.4 11.3 12.8 15.1 15.4 - 14.0 11.8 10.7 10.2 9.25 8.85 9.40 11.0 - 13.2 14.3 14.3 13.0 11.3 13.5 6.75 11.5 - 10.8 15.5 13.0 8.75 13.5 14.7 16.7 10.0 - 15.3 14.8 9.75 12.2 11.0 8.75 10.0 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Errors - 1.00 1.00 1.00 4.58 4.75 3.22 2.98 2.68 - 2.63 2.84 2.94 2.99 2.98 2.89 2.77 2.67 - 2.61 2.59 2.54 2.60 2.74 2.76 2.63 2.52 - 2.50 2.48 2.52 2.60 2.62 2.52 2.33 2.17 - 2.08 2.06 2.06 2.19 2.35 2.47 2.47 2.37 - 2.28 2.15 2.01 1.94 2.13 2.20 2.14 2.04 - 2.09 2.09 2.10 2.20 2.36 2.45 2.53 2.47 - 2.35 2.26 2.36 2.31 2.14 2.00 1.98 2.03 - 2.08 2.13 2.16 2.19 2.21 2.37 2.55 2.61 - 2.54 2.42 2.40 2.38 2.44 2.59 2.80 2.77 - 2.69 2.62 2.52 2.30 2.24 2.36 2.44 2.46 - 2.56 2.70 2.60 2.52 2.57 2.74 2.66 2.56 - 2.49 2.48 2.52 2.48 2.47 2.52 2.60 2.62 - 2.56 2.47 2.45 2.51 2.60 2.58 2.64 2.64 - 2.52 2.26 2.12 2.12 2.25 2.45 2.62 2.64 - 2.65 2.69 2.76 2.78 2.65 2.63 2.66 2.71 - 2.78 2.83 2.67 2.47 2.38 2.51 2.74 2.77 - 2.63 2.42 2.32 2.25 2.15 2.10 2.16 2.34 - 2.56 2.67 2.67 2.55 2.36 2.60 1.84 2.39 - 2.32 2.78 2.54 2.09 2.56 2.71 2.89 2.24 - 2.76 2.71 2.21 2.47 2.34 2.09 3.16 1.00 - 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 - 1.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 35.0 27.8 20.6 16.4 15.2 - 14.0 16.0 18.0 17.8 15.4 13.0 15.8 18.6 - 18.6 15.8 13.0 13.8 14.6 14.8 14.4 14.0 - 12.4 10.8 10.4 11.2 12.0 14.4 16.8 17.8 - 17.4 17.0 15.0 13.0 11.0 9.00 7.00 7.00 - 7.00 7.60 8.80 10.0 8.80 7.60 7.40 8.20 - 9.00 10.2 11.4 12.4 13.2 14.0 12.4 10.8 - 9.60 8.80 8.00 8.40 8.80 9.60 10.8 12.0 - 11.6 11.2 10.2 8.60 7.00 10.2 13.4 14.6 - 13.8 13.0 13.4 13.8 13.4 12.2 11.0 11.8 - 12.6 12.6 11.8 11.0 9.40 7.80 8.40 11.2 - 14.0 13.2 12.4 10.8 8.41 6.01 10.0 14.0 - 14.6 11.8 9.00 9.80 10.6 10.2 8.61 7.00 - 11.4 15.8 17.0 15.0 13.0 9.41 5.81 5.21 - 7.60 10.0 11.6 13.2 13.4 12.2 11.0 9.80 - 8.60 9.60 12.8 16.0 12.8 9.62 9.80 13.4 - 17.0 15.0 13.0 12.4 13.2 14.0 13.2 12.4 - 11.6 10.8 10.0 12.4 14.8 14.0 10.0 6.00 - 5.60 5.20 6.19 8.61 11.0 12.0 11.0 10.0 - 15.0 18.0 16.0 13.0 8.07 17.0 12.0 17.0 - 14.0 12.0 14.0 13.0 11.0 3.01 5.03 9.00 - 13.0 6.00 59.0 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 -1.00e+20 - -1.00e+20 -### Errors - 0.00 0.00 0.00 5.92 5.20 4.48 4.05 3.89 - 3.74 3.99 4.24 4.21 3.91 3.61 3.95 4.30 - 4.30 3.95 3.61 3.71 3.82 3.85 3.79 3.74 - 3.51 3.28 3.22 3.34 3.46 3.78 4.09 4.22 - 4.17 4.12 3.86 3.60 3.30 2.97 2.65 2.65 - 2.65 2.75 2.96 3.16 2.96 2.75 2.72 2.86 - 3.00 3.19 3.37 3.52 3.63 3.74 3.51 3.28 - 3.10 2.96 2.83 2.90 2.97 3.09 3.28 3.46 - 3.41 3.35 3.18 2.91 2.65 3.14 3.63 3.82 - 3.71 3.61 3.66 3.71 3.66 3.49 3.32 3.43 - 3.55 3.55 3.43 3.32 3.05 2.78 2.86 3.30 - 3.74 3.63 3.52 3.26 2.86 2.45 3.07 3.69 - 3.80 3.40 3.00 3.13 3.25 3.18 2.92 2.65 - 3.28 3.92 4.11 3.86 3.61 2.96 2.32 2.23 - 2.70 3.16 3.39 3.63 3.66 3.49 3.32 3.12 - 2.93 3.06 3.53 4.00 3.53 3.07 3.09 3.61 - 4.12 3.86 3.60 3.52 3.63 3.74 3.63 3.52 - 3.40 3.28 3.16 3.50 3.83 3.69 3.07 2.45 - 2.36 2.28 2.45 2.89 3.32 3.46 3.32 3.16 - 3.87 4.24 4.00 3.61 2.84 4.12 3.46 4.12 - 3.74 3.46 3.74 3.60 3.32 1.73 2.24 3.00 - 3.61 2.45 7.68 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Corrected Counts - 0.00 0.00 0.00 191. 132. 70.2 40.5 25.5 - 17.2 14.7 15.6 16.9 16.7 14.5 16.1 18.3 - 18.6 16.8 15.0 14.4 15.0 16.6 18.3 18.5 - 19.1 19.3 18.5 16.9 15.5 13.1 10.4 8.70 - 8.65 10.2 11.9 12.3 11.3 10.0 10.0 11.8 - 11.5 10.4 9.60 10.0 9.40 8.80 8.50 8.30 - 7.50 6.50 7.30 8.80 9.95 9.75 8.75 7.75 - 7.25 7.25 7.25 7.25 7.40 8.10 9.35 10.8 - 11.1 11.4 10.9 9.65 8.25 9.05 10.9 12.5 - 13.0 12.0 10.2 9.30 10.0 12.1 14.5 15.9 - 17.3 17.8 16.9 14.5 12.3 10.6 9.65 9.80 - 11.0 13.2 14.5 14.2 12.8 11.8 12.0 11.4 - 10.8 11.0 12.7 14.6 15.3 15.8 16.2 15.7 - 13.8 13.1 13.4 14.1 14.5 15.1 13.9 12.1 - 11.0 12.2 12.5 11.9 11.5 11.8 13.0 14.8 - 15.4 14.9 13.6 11.7 12.3 13.7 13.5 11.3 - 8.50 7.30 6.85 7.75 9.99 13.0 13.4 13.0 - 13.1 13.5 12.5 9.31 7.31 7.20 9.00 12.0 - 13.2 12.6 11.5 11.1 12.3 12.8 8.99 10.0 - 10.5 9.00 7.00 12.0 10.0 13.8 13.8 12.8 - 10.8 15.0 15.8 16.8 21.0 12.8 11.5 8.00 - 11.2 12.2 10.0 0.00 0.00 0.00 0.00 0.00 - 0.00 -### Errors - 1.00 1.00 1.00 13.8 10.9 5.66 4.37 3.51 - 2.90 2.69 2.78 2.89 2.88 2.69 2.81 3.01 - 3.04 2.89 2.74 2.68 2.73 2.87 3.02 3.04 - 3.09 3.10 3.03 2.90 2.78 2.54 2.26 2.07 - 2.07 2.25 2.43 2.47 2.37 2.22 2.22 2.42 - 2.38 2.26 2.19 2.24 2.17 2.10 2.06 2.03 - 1.92 1.80 1.87 2.06 2.23 2.21 2.08 1.96 - 1.90 1.90 1.90 1.90 1.92 2.00 2.15 2.32 - 2.36 2.39 2.33 2.19 2.03 2.12 2.32 2.49 - 2.55 2.44 2.25 2.15 2.22 2.44 2.69 2.82 - 2.94 2.98 2.90 2.69 2.47 2.29 2.19 2.21 - 2.34 2.56 2.69 2.66 2.52 2.42 2.44 2.38 - 2.32 2.33 2.51 2.69 2.77 2.81 2.84 2.80 - 2.62 2.55 2.59 2.65 2.69 2.75 2.61 2.44 - 2.35 2.47 2.49 2.44 2.40 2.43 2.54 2.72 - 2.77 2.73 2.60 2.42 2.47 2.61 2.58 2.35 - 2.05 1.91 1.85 1.95 2.20 2.54 2.58 2.55 - 2.56 2.59 2.48 2.13 1.89 1.88 2.09 2.43 - 2.56 2.51 2.39 2.35 2.47 2.47 2.11 2.23 - 2.29 2.12 1.87 2.43 2.23 2.62 2.62 2.52 - 2.30 2.74 2.80 2.89 3.23 2.52 2.39 2.00 - 2.36 2.46 3.16 1.00 1.00 1.00 1.00 1.00 - 1.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 261. 169. 77.8 29.4 24.2 - 19.0 17.0 15.0 13.2 11.6 10.0 12.4 14.8 - 16.4 17.2 18.0 17.6 17.2 17.4 18.2 19.0 - 15.8 12.6 11.0 11.0 11.0 13.0 15.0 17.2 - 19.6 22.0 18.4 14.8 12.2 10.6 9.00 9.80 - 10.6 11.0 11.0 11.0 11.0 11.0 10.6 9.80 - 9.00 10.6 12.2 13.0 13.0 13.0 12.6 12.2 - 13.0 15.0 17.0 13.4 9.80 8.00 8.00 8.00 - 8.80 9.60 10.4 11.2 12.0 8.80 5.60 5.41 - 8.19 11.0 10.2 9.40 9.60 10.8 12.0 12.0 - 12.0 11.6 10.8 10.0 10.0 10.0 11.8 15.4 - 19.0 17.0 15.0 15.0 17.0 19.0 15.8 12.6 - 9.80 7.40 5.00 9.01 13.0 14.2 12.6 11.0 - 10.2 9.40 9.20 9.60 10.0 8.80 7.60 8.01 - 10.0 12.0 10.8 9.60 8.40 7.20 6.00 10.0 - 14.0 14.4 11.2 8.00 11.6 15.2 15.6 12.8 - 10.0 12.4 14.8 14.0 10.0 6.00 8.79 11.6 - 11.8 9.41 7.00 7.80 8.60 9.00 9.00 9.00 - 9.80 10.6 10.2 8.59 6.99 3.00 11.0 6.00 - 14.0 10.0 6.00 13.0 9.98 8.00 13.0 13.0 - 10.0 19.0 9.01 10.1 25.0 23.0 17.9 8.00 - 8.00 8.00 10.9 5.00 5.00 7.00 9.00 -1.00e+20 - -1.00e+20 -### Errors - 0.00 0.00 0.00 16.2 12.0 7.76 5.40 4.88 - 4.36 4.11 3.87 3.63 3.39 3.16 3.50 3.83 - 4.05 4.15 4.24 4.19 4.15 4.17 4.26 4.36 - 3.94 3.52 3.32 3.32 3.32 3.59 3.86 4.14 - 4.41 4.69 4.26 3.82 3.48 3.24 3.00 3.13 - 3.25 3.32 3.32 3.32 3.32 3.32 3.25 3.13 - 3.00 3.24 3.48 3.61 3.61 3.61 3.55 3.49 - 3.60 3.86 4.12 3.60 3.09 2.83 2.83 2.83 - 2.96 3.10 3.22 3.34 3.46 2.88 2.29 2.26 - 2.79 3.32 3.19 3.06 3.09 3.28 3.46 3.46 - 3.46 3.40 3.28 3.16 3.16 3.16 3.40 3.88 - 4.36 4.11 3.86 3.87 4.11 4.36 3.94 3.53 - 3.10 2.67 2.24 2.89 3.55 3.76 3.54 3.32 - 3.19 3.06 3.03 3.10 3.16 2.96 2.75 2.81 - 3.14 3.46 3.28 3.09 2.89 2.67 2.45 3.07 - 3.69 3.77 3.30 2.83 3.34 3.86 3.93 3.55 - 3.16 3.50 3.83 3.69 3.07 2.45 2.91 3.37 - 3.42 3.03 2.65 2.79 2.93 3.00 3.00 3.00 - 3.13 3.25 3.18 2.91 2.64 1.73 3.32 2.45 - 3.74 3.16 2.45 3.60 3.16 2.83 3.61 3.61 - 3.17 4.36 3.00 3.18 5.00 4.79 4.23 2.83 - 2.83 2.83 3.31 2.24 2.24 2.65 3.00 0.00 - 0.00 -### Corrected Counts - 0.00 0.00 0.00 42.0 34.0 27.7 25.9 25.7 - 24.5 22.3 21.0 19.9 18.6 17.0 17.8 17.4 - 15.2 12.7 13.2 16.7 18.1 17.6 15.4 12.0 - 11.4 13.4 14.8 14.3 11.5 11.1 12.8 13.7 - 12.4 9.00 9.60 11.5 12.4 12.0 12.0 13.4 - 13.9 13.0 10.9 8.25 7.05 6.75 6.55 6.35 - 6.75 8.75 10.2 11.0 11.5 11.5 11.1 10.2 - 9.55 9.85 12.2 13.4 12.0 9.85 8.45 8.25 - 8.65 9.20 9.00 8.30 8.50 11.3 12.9 12.7 - 11.3 10.3 10.4 11.3 12.4 13.0 12.3 11.5 - 11.4 11.7 12.4 14.3 15.7 14.5 12.5 11.3 - 12.5 15.7 18.2 17.6 14.3 10.5 10.5 11.4 - 13.0 14.5 14.7 11.9 9.44 8.25 8.94 11.8 - 12.8 10.9 9.30 9.20 10.0 10.6 11.6 12.4 - 12.5 12.7 12.4 10.9 9.10 7.85 8.25 8.85 - 8.40 8.10 8.60 10.0 12.0 13.4 13.8 13.3 - 12.7 13.4 13.9 13.3 11.6 10.0 10.0 10.0 - 10.2 10.8 12.0 12.2 11.8 12.2 13.5 14.7 - 13.0 11.2 10.7 11.8 13.0 11.0 11.3 10.5 - 8.75 8.25 9.00 11.5 10.8 12.5 7.50 10.7 - 12.7 15.7 18.5 17.2 15.0 15.7 16.5 15.0 - 10.7 12.7 14.0 8.50 7.50 8.00 3.00 0.00 - 0.00 -### Errors - 1.00 1.00 1.00 6.48 5.76 3.70 3.60 3.58 - 3.50 3.34 3.24 3.15 3.04 2.92 2.98 2.93 - 2.73 2.49 2.55 2.87 3.00 2.96 2.76 2.43 - 2.37 2.57 2.71 2.66 2.38 2.34 2.52 2.59 - 2.46 2.10 2.15 2.38 2.49 2.44 2.44 2.59 - 2.63 2.54 2.31 2.02 1.87 1.84 1.80 1.77 - 1.83 2.07 2.24 2.35 2.40 2.40 2.36 2.25 - 2.17 2.21 2.45 2.58 2.42 2.20 2.06 2.03 - 2.08 2.14 2.12 2.02 2.04 2.35 2.53 2.51 - 2.36 2.26 2.29 2.37 2.48 2.55 2.47 2.39 - 2.39 2.42 2.49 2.66 2.79 2.67 2.48 2.37 - 2.49 2.78 3.00 2.94 2.62 2.29 2.29 2.38 - 2.54 2.69 2.71 2.42 2.15 2.03 2.10 2.39 - 2.50 2.31 2.13 2.13 2.23 2.30 2.41 2.48 - 2.50 2.52 2.48 2.32 2.12 1.97 2.02 2.10 - 2.05 2.01 2.07 2.23 2.44 2.58 2.63 2.58 - 2.52 2.58 2.64 2.57 2.39 2.24 2.24 2.24 - 2.26 2.32 2.45 2.47 2.43 2.46 2.59 2.72 - 2.53 2.35 2.31 2.42 2.55 2.35 2.37 2.29 - 2.09 2.03 2.12 2.39 2.32 2.50 1.94 2.31 - 2.52 2.78 3.04 2.94 2.74 2.81 2.87 2.74 - 2.30 2.52 2.64 2.05 1.93 2.00 1.73 1.00 - 1.00 -### Corrected Counts - -1.00e+20 -1.00e+20 -1.00e+20 19.0 20.6 22.2 21.2 17.6 - 14.0 15.6 17.2 17.8 17.4 17.0 21.0 25.0 - 23.8 17.4 11.0 10.6 10.2 10.8 12.4 14.0 - 14.4 14.8 14.8 14.4 14.0 14.0 14.0 13.4 - 12.2 11.0 11.8 12.6 13.6 14.8 16.0 11.2 - 6.41 5.99 9.99 14.0 11.6 9.19 7.80 7.40 - 7.00 10.2 13.4 14.4 13.2 12.0 12.8 13.6 - 12.8 10.4 8.00 10.4 12.8 13.4 12.2 11.0 - 10.6 10.2 10.4 11.2 12.0 11.6 11.2 10.4 - 9.20 8.00 8.80 9.60 10.4 11.2 12.0 10.4 - 8.80 9.40 12.2 15.0 13.0 11.0 10.6 11.8 - 13.0 12.6 12.2 12.0 12.0 12.0 12.4 12.8 - 12.2 10.6 9.00 10.2 11.4 12.8 14.4 16.0 - 14.0 12.0 10.4 9.20 8.00 8.40 8.80 9.20 - 9.60 10.0 10.4 10.8 10.6 9.80 9.00 8.20 - 7.40 7.80 9.39 11.0 11.8 12.6 12.6 11.8 - 11.0 11.0 11.0 11.2 11.6 12.0 9.61 7.20 - 7.59 10.8 14.0 12.8 11.6 11.4 12.2 13.0 - 11.4 9.80 10.4 13.2 16.0 7.00 12.0 13.0 - 9.00 6.00 4.00 9.99 4.05 11.0 4.00 17.0 - 11.0 6.00 14.0 6.03 10.0 16.0 24.9 16.0 - 16.0 15.0 19.9 11.0 10.0 7.00 5.01 6.00 - 16.0 -### Errors - 0.00 0.00 0.00 4.36 4.53 4.71 4.58 4.16 - 3.74 3.94 4.14 4.22 4.17 4.12 4.55 4.98 - 4.82 4.07 3.32 3.25 3.19 3.28 3.51 3.74 - 3.79 3.85 3.85 3.79 3.74 3.74 3.74 3.66 - 3.49 3.32 3.43 3.55 3.68 3.84 4.00 3.20 - 2.40 2.35 3.04 3.74 3.38 3.01 2.79 2.72 - 2.65 3.14 3.63 3.79 3.63 3.46 3.58 3.69 - 3.56 3.19 2.83 3.19 3.56 3.66 3.49 3.32 - 3.25 3.19 3.22 3.34 3.46 3.41 3.35 3.22 - 3.02 2.83 2.96 3.10 3.22 3.34 3.46 3.21 - 2.96 3.04 3.45 3.87 3.59 3.30 3.25 3.43 - 3.61 3.55 3.49 3.46 3.46 3.46 3.52 3.58 - 3.48 3.24 3.00 3.19 3.37 3.57 3.78 4.00 - 3.73 3.45 3.22 3.02 2.83 2.90 2.97 3.03 - 3.10 3.16 3.22 3.29 3.25 3.13 3.00 2.86 - 2.72 2.78 3.05 3.32 3.43 3.55 3.55 3.43 - 3.32 3.32 3.32 3.35 3.40 3.46 3.06 2.65 - 2.71 3.22 3.74 3.57 3.40 3.37 3.49 3.61 - 3.36 3.12 3.20 3.60 4.00 2.65 3.46 3.61 - 3.00 2.45 2.00 3.16 2.01 3.32 2.00 4.12 - 3.31 2.45 3.74 2.45 3.16 4.00 4.99 4.00 - 4.00 3.87 4.46 3.32 3.16 2.65 2.24 2.45 - 4.00 -### Corrected Counts - 0.00 0.00 0.00 19.0 18.2 19.3 17.2 14.7 - 12.3 12.2 13.7 15.1 15.6 15.0 16.0 17.4 - 18.2 17.5 15.8 12.7 11.8 12.6 13.5 12.5 - 10.5 9.10 8.60 9.30 11.5 13.5 13.4 12.3 - 11.4 11.7 13.6 14.3 13.0 10.2 8.25 9.65 - 11.5 12.5 12.5 12.3 13.1 11.6 8.70 6.00 - 6.00 8.80 10.4 10.4 9.20 8.00 8.60 10.3 - 11.5 11.8 12.0 13.2 13.4 13.0 12.4 12.0 - 10.8 9.30 8.40 8.40 9.00 8.40 9.00 10.7 - 12.6 13.0 12.2 10.5 9.10 8.75 9.75 9.15 - 10.0 11.9 13.1 11.5 9.69 8.95 8.55 8.35 - 8.75 8.95 9.30 10.4 11.7 11.5 12.3 14.0 - 14.2 12.6 11.0 12.0 12.7 11.6 9.21 8.01 - 10.0 10.4 10.4 10.6 11.0 9.80 9.80 10.7 - 12.0 13.2 12.5 10.3 8.40 7.65 8.25 9.85 - 12.1 13.7 14.0 12.7 10.8 8.31 7.50 9.06 - 12.3 11.2 9.05 7.25 6.50 6.50 7.30 8.10 - 8.90 9.55 9.75 10.1 10.1 9.20 7.65 6.25 - 5.65 6.69 8.50 10.0 10.0 12.0 8.00 7.00 - 4.26 6.25 9.75 9.00 9.78 12.7 11.0 8.50 - 9.78 14.7 7.75 11.0 10.5 11.0 13.5 19.0 - 35.5 21.3 20.2 16.3 17.7 10.7 6.48 7.99 - 15.0 -### Errors - 1.00 1.00 1.00 4.36 4.26 3.10 2.92 2.69 - 2.47 2.47 2.61 2.75 2.79 2.74 2.82 2.95 - 3.01 2.96 2.80 2.51 2.41 2.49 2.59 2.49 - 2.28 2.13 2.07 2.15 2.38 2.59 2.58 2.47 - 2.38 2.42 2.60 2.67 2.53 2.23 2.03 2.18 - 2.39 2.49 2.49 2.47 2.55 2.37 2.03 1.70 - 1.71 2.06 2.26 2.28 2.14 2.00 2.06 2.25 - 2.38 2.43 2.45 2.57 2.58 2.54 2.49 2.45 - 2.32 2.15 2.04 2.05 2.12 2.05 2.10 2.29 - 2.50 2.55 2.47 2.27 2.11 2.09 2.21 2.13 - 2.21 2.40 2.55 2.38 2.19 2.11 2.07 2.04 - 2.09 2.11 2.15 2.27 2.41 2.39 2.46 2.63 - 2.66 2.50 2.35 2.44 2.52 2.38 2.10 1.99 - 2.22 2.26 2.26 2.29 2.34 2.21 2.20 2.30 - 2.45 2.57 2.48 2.25 2.03 1.95 2.03 2.21 - 2.44 2.60 2.64 2.52 2.30 2.01 1.89 2.08 - 2.47 2.34 2.10 1.90 1.80 1.80 1.91 2.01 - 2.11 2.18 2.21 2.25 2.24 2.13 1.95 1.76 - 1.68 1.80 2.03 2.23 2.24 2.45 2.00 1.87 - 1.41 1.71 2.20 2.12 2.20 2.52 2.34 2.06 - 2.20 2.71 1.85 2.31 2.28 2.34 2.58 3.03 - 4.21 3.25 3.18 2.84 2.98 2.32 1.78 1.98 - 3.87 -### Corrected Counts - 96.0 87.9 71.7 43.3 33.7 24.2 18.8 17.5 - 16.2 15.4 14.7 13.8 12.9 12.0 13.1 14.2 - 14.9 15.2 15.5 14.3 13.1 12.8 13.6 14.3 - 15.0 15.9 15.6 14.3 13.0 12.8 12.6 13.1 - 14.1 15.2 14.4 13.7 12.7 11.8 10.8 11.2 - 11.5 11.7 11.5 11.3 11.6 11.8 12.3 13.0 - 13.7 12.6 11.3 10.5 9.85 9.25 10.5 11.7 - 12.5 12.8 13.2 12.4 11.6 11.1 10.7 10.3 - 10.4 10.6 10.4 9.70 9.00 8.10 7.20 7.10 - 7.80 8.50 9.10 9.70 9.90 9.70 9.50 9.60 - 9.70 10.0 10.5 11.0 11.3 11.6 11.6 11.1 - 10.8 11.0 11.4 11.7 12.1 12.5 12.0 11.5 - 11.0 10.6 10.3 10.1 10.0 10.3 10.7 11.3 - 11.8 12.3 12.4 12.3 12.3 11.6 10.9 10.2 - 9.60 9.00 9.40 9.80 10.0 10.1 10.2 9.75 - 9.25 9.00 9.00 9.00 9.40 9.80 10.1 10.2 - 10.3 10.3 10.3 10.3 10.4 10.5 10.1 9.70 - 9.40 9.20 9.00 9.60 10.2 10.5 10.5 10.5 - 9.90 9.30 8.85 8.55 8.25 8.75 9.67 7.75 - 8.50 9.25 7.00 6.00 7.76 9.25 10.2 11.2 - 9.99 8.00 8.25 9.50 10.0 15.8 13.7 12.0 - 14.0 25.0 37.2 32.5 21.7 17.0 15.8 16.5 - 10.0 -### Errors - 9.80 9.20 8.02 4.41 3.87 3.35 3.05 2.95 - 2.85 2.77 2.69 2.61 2.53 2.45 2.54 2.64 - 2.70 2.73 2.77 2.66 2.55 2.52 2.58 2.64 - 2.72 2.80 2.78 2.65 2.53 2.50 2.49 2.54 - 2.65 2.76 2.67 2.59 2.50 2.41 2.32 2.36 - 2.40 2.41 2.38 2.36 2.39 2.42 2.47 2.55 - 2.62 2.49 2.35 2.25 2.19 2.14 2.27 2.40 - 2.48 2.52 2.57 2.49 2.41 2.34 2.30 2.26 - 2.29 2.31 2.27 2.17 2.08 1.97 1.88 1.87 - 1.93 2.01 2.09 2.19 2.22 2.20 2.18 2.19 - 2.20 2.23 2.29 2.34 2.37 2.41 2.40 2.36 - 2.32 2.34 2.37 2.41 2.45 2.50 2.45 2.40 - 2.35 2.31 2.26 2.25 2.24 2.26 2.31 2.36 - 2.42 2.47 2.49 2.48 2.47 2.39 2.32 2.24 - 2.18 2.12 2.16 2.21 2.23 2.25 2.26 2.20 - 2.15 2.12 2.12 2.12 2.17 2.21 2.24 2.25 - 2.26 2.26 2.26 2.27 2.28 2.29 2.24 2.20 - 2.16 2.14 2.12 2.19 2.25 2.28 2.28 2.29 - 2.22 2.15 2.10 2.07 2.03 2.15 2.55 1.97 - 2.04 2.15 1.85 1.73 1.96 2.15 2.26 2.37 - 2.22 2.00 2.03 2.18 2.24 2.80 2.61 2.45 - 2.63 3.43 4.30 3.97 3.29 2.90 2.81 2.87 - 3.16 -### Corrected Counts - 0.00 0.00 0.00 30.0 25.2 17.5 15.7 14.2 - 13.3 16.2 17.6 17.3 16.3 16.5 16.5 16.4 - 16.7 17.3 16.8 13.3 11.0 9.90 9.80 10.0 - 11.2 11.7 11.5 11.4 13.0 14.4 13.2 11.1 - 9.30 8.50 8.10 8.75 10.1 11.5 12.5 13.3 - 13.8 13.5 12.8 13.0 15.2 15.8 15.8 15.9 - 16.5 14.3 12.4 11.8 12.1 11.5 9.10 7.90 - 8.20 9.50 10.5 10.5 10.4 10.2 10.4 11.8 - 13.6 12.8 10.4 7.90 7.50 7.70 7.30 7.10 - 7.40 8.00 8.60 7.70 6.50 6.35 8.75 10.8 - 10.7 9.15 7.55 7.75 9.96 10.3 10.1 10.2 - 10.7 10.8 12.1 14.5 16.9 17.7 14.4 11.3 - 9.35 8.50 7.50 7.70 8.05 9.16 11.1 13.7 - 14.2 13.2 11.2 9.10 8.50 9.90 9.80 8.69 - 7.70 8.50 9.70 10.3 10.2 9.40 8.00 8.00 - 8.45 9.15 9.65 9.25 8.26 8.45 8.45 7.80 - 7.00 8.40 9.50 10.0 10.0 10.3 10.4 9.75 - 9.15 9.15 9.75 9.15 9.30 10.3 11.6 12.0 - 10.8 10.1 10.0 10.5 10.5 7.00 6.75 5.50 - 6.25 9.25 9.25 7.50 8.77 11.5 7.01 8.74 - 7.22 5.25 7.25 7.99 8.26 8.25 9.97 8.74 - 16.7 11.3 13.0 16.8 90.9 99.7 16.5 15.2 - 10.0 -### Errors - 1.00 1.00 1.00 5.48 4.98 2.95 2.79 2.65 - 2.57 2.83 2.95 2.94 2.85 2.87 2.87 2.86 - 2.89 2.94 2.88 2.56 2.33 2.22 2.21 2.23 - 2.36 2.41 2.39 2.38 2.54 2.68 2.56 2.34 - 2.15 2.06 2.01 2.08 2.23 2.39 2.50 2.58 - 2.63 2.59 2.52 2.54 2.75 2.80 2.80 2.82 - 2.87 2.66 2.48 2.43 2.45 2.39 2.12 1.98 - 2.02 2.17 2.29 2.29 2.27 2.25 2.27 2.41 - 2.60 2.50 2.24 1.97 1.93 1.95 1.91 1.88 - 1.92 2.00 2.07 1.93 1.76 1.76 2.05 2.30 - 2.30 2.12 1.92 1.95 2.22 2.26 2.24 2.25 - 2.32 2.32 2.45 2.67 2.90 2.98 2.64 2.34 - 2.16 2.06 1.94 1.96 2.00 2.12 2.33 2.62 - 2.66 2.56 2.35 2.12 2.05 2.22 2.20 2.07 - 1.95 2.04 2.20 2.27 2.25 2.16 2.00 2.00 - 2.05 2.14 2.19 2.14 2.03 2.05 2.04 1.95 - 1.87 2.03 2.17 2.24 2.24 2.26 2.29 2.20 - 2.13 2.13 2.21 2.14 2.15 2.26 2.40 2.45 - 2.32 2.24 2.24 2.29 2.29 1.87 1.84 1.66 - 1.76 2.15 2.15 1.93 2.09 2.40 1.85 2.08 - 1.87 1.59 1.90 2.00 2.01 2.03 2.22 2.07 - 2.89 2.37 2.54 2.89 6.24 6.47 2.87 2.76 - 3.16 diff --git a/test_data/IPTS-34735/exp823/Shared/GY_scripts/Untitled.ipynb b/test_data/IPTS-34735/exp823/Shared/GY_scripts/Untitled.ipynb deleted file mode 100644 index 7156581a..00000000 --- a/test_data/IPTS-34735/exp823/Shared/GY_scripts/Untitled.ipynb +++ /dev/null @@ -1,432 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 110, - "id": "1e2bd9bc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in links: https://repoman.ornl.gov/pypi/web/simple\r\n", - "Requirement already satisfied: tabulate in /SNS/users/gy7/.local/lib/python3.10/site-packages (0.9.0)\r\n" - ] - } - ], - "source": [ - "!pip install tabulate --user" - ] - }, - { - "cell_type": "markdown", - "id": "7277ef5a", - "metadata": {}, - "source": [ - "# Import Stuff here" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "id": "1deb7218", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import re\n", - "import numpy as np\n", - "import pandas as pd\n", - "from scipy.interpolate import griddata\n", - "from tabulate import tabulate\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "d80cee34", - "metadata": {}, - "source": [ - "### Define paths here" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "id": "9c7f547c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/SNS/snfs1/instruments-hfir/HB3/IPTS-34735/exp823/Shared/GY_scripts\n" - ] - } - ], - "source": [ - "MAIN_DIR = os.getcwd()\n", - "print(MAIN_DIR)" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "id": "80ba831f", - "metadata": {}, - "outputs": [], - "source": [ - "DATA_DIR = '/SNS/snfs1/instruments-hfir/HB3/IPTS-34735/exp823/Datafiles'" - ] - }, - { - "cell_type": "markdown", - "id": "40c32714", - "metadata": {}, - "source": [ - "### Define functions here" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "29f7c801", - "metadata": {}, - "outputs": [], - "source": [ - "def data_reader(scannumber):\n", - " comments = []\n", - " header = []\n", - " footer = []\n", - " with open('{}/HB3_exp0823_scan0{}.dat'.format(DATA_DIR, scannumber)) as f:\n", - " data = f.readlines()\n", - " #for x in data[29:]:\n", - " # print(x)\n", - " for i in range(29):\n", - " comments.append(data.pop(0))\n", - " header.append(data.pop(0))\n", - " for i in range(4):\n", - " footer.append(data.pop(-1))\n", - "\n", - " datarray = []\n", - " for i in data:\n", - " datarray.append(np.array(i.split(), dtype='float'))\n", - " return(comments, header, footer, datarray)" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "id": "bb410fa5", - "metadata": {}, - "outputs": [], - "source": [ - "def which_command(comment):\n", - " for line in comment:\n", - " if '# command =' in line:\n", - " #print(line)\n", - " return(line)" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "id": "23632257", - "metadata": {}, - "outputs": [], - "source": [ - "def what_scan(command_line):\n", - " # Use regular expression to find values for h, k, l, e, ef, and mcu\n", - " match = re.search(r'h (\\S+) k (\\S+) l (\\S+) e (\\S+).* ef (\\S+).* mcu (\\S+)', command_line)\n", - " if match:\n", - " h = match.group(1)\n", - " k = match.group(2)\n", - " l = match.group(3)\n", - " e = match.group(4)\n", - " ef = match.group(5)\n", - " mcu = match.group(6)\n", - "\n", - " #print(\"h:\", h)\n", - " #print(\"k:\", k)\n", - " #print(\"l:\", l)\n", - " #print(\"e:\", e)\n", - " #print(\"ef:\", ef)\n", - " #print(\"mcu:\", mcu)\n", - " return([float(h), float(k), float(l)], float(e), float(ef), float(mcu))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "id": "0ec5cd46", - "metadata": {}, - "outputs": [], - "source": [ - "def all_scans_table(scans):\n", - " table_data = []\n", - " for i in scans:\n", - " comments, header, footer, datarray = data_reader(i)\n", - " command = which_command(comments)\n", - " result = what_scan(command)\n", - " if result:\n", - " table_data.append([i] + result[0] + [result[1], result[2], result[3]])\n", - " \n", - " df = pd.DataFrame(table_data, columns=[\"Scan#\", \"h\", \"k\", \"l\", \"e\", \"ef\", \"mcu\"])\n", - " pd.set_option(\"display.max_rows\", None) # Show all rows\n", - " pd.set_option(\"display.max_columns\", None) # Show all columns\n", - " pd.set_option(\"display.width\", None) # No limit on column width\n", - " pd.set_option(\"display.max_colwidth\", None) # Show full column content\n", - " print(df)\n", - " #print(tabulate(df, headers=\"keys\", tablefmt=\"fancy_grid\", showindex=False))" - ] - }, - { - "cell_type": "markdown", - "id": "008eb817", - "metadata": {}, - "source": [ - "### Use the command below to print a table of the scans " - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "id": "078d48aa", - "metadata": {}, - "outputs": [], - "source": [ - "#all_scans_table(scans)" - ] - }, - { - "cell_type": "markdown", - "id": "521aa0b6", - "metadata": {}, - "source": [ - "### Actual data processing done here -- Define data processing functions" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "id": "48309d2d", - "metadata": {}, - "outputs": [], - "source": [ - "scans = [str(x).zfill(3) for x in range(134, 146, 1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "id": "71de1d92", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "comments, header, footer, datarray = data_reader(scans[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "id": "7a8aca8e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['# Pt. h k l e ef time detector monitor mcu focal_length m1 m2 mcrystal marc mtrans mfocus s1 s2 sgl sgu bbb bbl bbr bbt bab bal bar bat stl stu a1 a2 q ei vti sample temp temp_2\\n']" - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "header" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "id": "54c27474", - "metadata": {}, - "outputs": [], - "source": [ - "def data_process(dataarray):\n", - " h_array = []\n", - " k_array = []\n", - " l_array = []\n", - " e_array = []\n", - " ef_array = []\n", - " t_array = []\n", - " d_array = []\n", - " m_array = []\n", - " q_array = []\n", - " ei_array = []\n", - " st_array = []\n", - " for i in dataarray:\n", - " temp = i\n", - " h_array.append(temp[1])\n", - " k_array.append(temp[2])\n", - " l_array.append(temp[3])\n", - " e_array.append(temp[4])\n", - " ef_array.append(temp[5])\n", - " t_array.append(temp[6])\n", - " d_array.append(temp[7]) \n", - " m_array.append(temp[8]) \n", - " q_array.append(temp[-6]) \n", - " ei_array.append(temp[-5])\n", - " st_array.append(temp[-3])\n", - " final_array = [h_array, k_array, l_array, e_array, ef_array, \n", - " t_array, d_array, m_array, q_array, ei_array, st_array]\n", - " #return(np.transpose(final_array))\n", - " return (final_array)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "id": "9fb4d5a5", - "metadata": {}, - "outputs": [], - "source": [ - "def data_finallizer():\n", - " all_arrays = []\n", - " for scan in scans:\n", - " comments, header, footer, datarray = data_reader(scan)\n", - " array_now = data_process(datarray)\n", - " all_arrays.append(array_now)\n", - " return all_arrays\n", - "\n", - "all_arrays = data_finallizer()" - ] - }, - { - "cell_type": "code", - "execution_count": 244, - "id": "e5b43307", - "metadata": {}, - "outputs": [], - "source": [ - "#all_arrays" - ] - }, - { - "cell_type": "code", - "execution_count": 284, - "id": "96443aec", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1425392/3494278924.py:13: RuntimeWarning: invalid value encountered in divide\n", - " dm_ratio = np.divide(d_array, m_array, out=np.zeros_like(d_array, dtype=float), where=(m_array != 0))\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Extract H, E, D, and M arrays from all_arrays\n", - "h_all = []\n", - "e_all = []\n", - "dm_all = []\n", - "\n", - "for array_now in all_arrays:\n", - " h_array = array_now[0] # H array\n", - " e_array = array_now[3] # E array\n", - " d_array = array_now[6] # D array\n", - " m_array = array_now[7] # M array\n", - "\n", - " # Compute D/M ratio\n", - " dm_ratio = np.divide(d_array, m_array, out=np.zeros_like(d_array, dtype=float), where=(m_array != 0))\n", - " \n", - " h_all.extend(h_array)\n", - " e_all.extend(e_array)\n", - " dm_all.extend(dm_ratio)\n", - "\n", - "# Convert lists to numpy arrays for plotting\n", - "h_all = np.array(h_all)\n", - "e_all = np.array(e_all)\n", - "dm_all = np.array(dm_all)\n", - "\n", - "# Filter out non-finite values\n", - "mask = np.isfinite(dm_all) & np.isfinite(h_all) & np.isfinite(e_all)\n", - "h_all = h_all[mask]\n", - "e_all = e_all[mask]\n", - "dm_all = dm_all[mask]\n", - "\n", - "# Create a grid for H and E\n", - "h_grid = np.linspace(np.min(h_all), np.max(h_all), 100) # 100 points in H\n", - "e_grid = np.linspace(np.min(e_all), np.max(e_all), 100) # 100 points in E\n", - "H, E = np.meshgrid(h_grid, e_grid)\n", - "\n", - "# Interpolate D/M ratio onto the grid using nearest neighbor\n", - "DM_grid = griddata((h_all, e_all), dm_all, (H, E), method='nearest')\n", - "\n", - "# Set color limits (adjust as needed)\n", - "vmin = 0 #np.nanmin(dm_grid) # Minimum value for color scale\n", - "vmax = 0.00015 # Maximum value for color scale\n", - "\n", - "# Create the image plot using imshow\n", - "plt.figure(figsize=(10, 6))\n", - "plt.imshow(DM_grid, extent=(np.min(h_grid), np.max(h_grid), np.min(e_grid), np.max(e_grid)),\n", - " origin='lower', aspect='auto', cmap='viridis', vmin=vmin, vmax=vmax)\n", - "plt.colorbar(label=\"D/M Ratio\")\n", - "plt.xlabel(\"H\")\n", - "plt.ylabel(\"E\")\n", - "plt.xlim(1, 1.5)\n", - "plt.ylim(15, 45)\n", - "plt.title(\"Image Plot of D/M Ratio with H vs E (Nearest Neighbor Interpolation)\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1405fda", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:mantid]", - "language": "python", - "name": "conda-env-mantid-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}