PFS-JIRA https://pfspipe.ipmu.jp/jira This file is an XML representation of an issue en-us 8.3.4 803005 13-09-2019 [PIPE2D-1346] Generate detectorMaps for Dec. 2023 data https://pfspipe.ipmu.jp/jira/browse/PIPE2D-1346 DRP 2-D Pipeline <p>Generate detectorMaps based on the calibration data taken during the telescope test in Dec. 2023.</p> PIPE2D-1346 Generate detectorMaps for Dec. 2023 data Task Normal In Progress Unresolved Kiyoto Yabe Kiyoto Yabe Fri, 26 Jan 2024 00:47:44 +0000 Wed, 7 Feb 2024 23:58:07 +0000 0 2 <p>After m4 bootstrap detectorMap, I just ran reduceArc.py for all camera using calib set on Dec. 28 with and without doSlitOffsets option. </p> <p>Without doSlitOffsets:</p> <div class="code panel" style="border-width: 1px;"><div class="codeContent panelContent"> <pre class="code-java"> (b1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=45654117.632993 dof=11091684 xRMS=0.026695 yRMS=0.083563 xSoften=0.024577 ySoften=0.098187 from 11033952 lines (KrI: 6792, NeI: 49790, Trace: 10976142, XeI: 1228) (b2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=43612508.240033 dof=10598788 xRMS=0.028245 yRMS=0.040853 xSoften=0.026353 ySoften=0.049451 from 10542814 lines (KrI: 7325, NeI: 46933, Trace: 10486762, XeI: 1794) (b3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=29671618.184812 dof=10929293 xRMS=0.024581 yRMS=0.040523 xSoften=0.020406 ySoften=0.043460 from 10872801 lines (KrI: 7536, NeI: 47154, Trace: 10816231, XeI: 1880) (b4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=48864539.510907 dof=11140335 xRMS=0.030246 yRMS=0.047646 xSoften=0.027864 ySoften=0.050703 from 11080571 lines (KrI: 8639, NeI: 48748, Trace: 11020729, XeI: 2455) (r1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=72012855.325722 dof=14317177 xRMS=0.022759 yRMS=0.025278 xSoften=0.023066 ySoften=0.025133 from 13996892 lines (ArI: 76722, KrI: 32487, NeI: 135019, Trace: 13676529, XeI: 76135) (r2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=53442423.869833 dof=13336196 xRMS=0.022727 yRMS=0.031812 xSoften=0.021971 ySoften=0.031884 from 13041169 lines (ArI: 74087, KrI: 30759, NeI: 123990, Trace: 12746064, XeI: 66269) (r3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=67323468.655685 dof=13619054 xRMS=0.025899 yRMS=0.028046 xSoften=0.025126 ySoften=0.027540 from 13324369 lines (ArI: 76542, KrI: 31910, NeI: 127131, Trace: 13029606, XeI: 59180) (r4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=113267922.604893 dof=13907089 xRMS=0.031139 yRMS=0.031577 xSoften=0.031652 ySoften=0.030808 from 13592573 lines (ArI: 77098, KrI: 33121, NeI: 133876, Trace: 13277979, XeI: 70499) (n1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=32358843.968862 dof=10738544 xRMS=0.026294 yRMS=0.051225 xSoften=0.023504 ySoften=0.045070 from 10626768 lines (ArI: 53078, KrI: 9598, NeI: 27738, Trace: 10514920, XeI: 21434) (n2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=163776230.150713 dof=10195383 xRMS=0.042713 yRMS=0.063358 xSoften=0.046465 ySoften=0.056897 from 10075067 lines (ArI: 55640, KrI: 10470, NeI: 27821, Trace: 9954679, XeI: 26457) (n3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=115193395.159363 dof=11399862 xRMS=0.033644 yRMS=0.064554 xSoften=0.034658 ySoften=0.059119 from 11269761 lines (ArI: 60106, KrI: 10440, NeI: 31325, Trace: 11139588, XeI: 28302) (m1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=173954239.899078 dof=13539282 xRMS=0.049939 yRMS=0.060598 xSoften=0.048119 ySoften=0.062298 from 13333456 lines (ArI: 45788, KrI: 37450, NeI: 77198, Trace: 13127552, XeI: 45468) (m2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=58837302.710912 dof=12462895 xRMS=0.029090 yRMS=0.106042 xSoften=0.024967 ySoften=0.107446 from 12285333 lines (ArI: 41088, KrI: 33998, NeI: 68965, Trace: 12107693, XeI: 33589) (m3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=150178349.046475 dof=13126397 xRMS=0.046668 yRMS=0.172309 xSoften=0.042884 ySoften=0.175301 from 12933802 lines (ArI: 43618, KrI: 35265, NeI: 74917, Trace: 12741129, XeI: 38873) (m4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=225006469.381380 dof=13544467 xRMS=0.060372 yRMS=0.047453 xSoften=0.059073 ySoften=0.047816 from 13339174 lines (ArI: 46204, KrI: 36962, NeI: 77463, Trace: 13133803, XeI: 44742) </pre> </div></div> <p>With doSlitOffsets:</p> <div class="code panel" style="border-width: 1px;"><div class="codeContent panelContent"> <pre class="code-java"> (b1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=31463086.579820 dof=10682450 xRMS=0.022398 yRMS=0.140902 xSoften=0.019733 ySoften=0.193647 from 10653234 lines (KrI: 4064, NeI: 25898, Trace: 10622750, XeI: 522) (b2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=26702337.438333 dof=10206638 xRMS=0.023049 yRMS=0.027032 xSoften=0.021102 ySoften=0.038639 from 10163701 lines (KrI: 6960, NeI: 35404, Trace: 10119524, XeI: 1813) (b3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=22509320.593434 dof=10763935 xRMS=0.021811 yRMS=0.028973 xSoften=0.017355 ySoften=0.031570 from 10718023 lines (KrI: 6517, NeI: 38915, Trace: 10670833, XeI: 1758) (b4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=24630535.023649 dof=10919160 xRMS=0.021947 yRMS=0.029005 xSoften=0.018053 ySoften=0.037566 from 10871340 lines (KrI: 7715, NeI: 38968, Trace: 10822248, XeI: 2409) (r1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=35946055.807622 dof=13511162 xRMS=0.016787 yRMS=0.018428 xSoften=0.015874 ySoften=0.016581 from 13224003 lines (ArI: 66477, KrI: 26740, NeI: 121777, Trace: 12935576, XeI: 73433) (r2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=32071219.337058 dof=12800928 xRMS=0.018357 yRMS=0.019742 xSoften=0.016766 ySoften=0.017297 from 12535687 lines (ArI: 64411, KrI: 26406, NeI: 111256, Trace: 12269206, XeI: 64408) (r3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=31030301.416250 dof=13023263 xRMS=0.018259 yRMS=0.018566 xSoften=0.016251 ySoften=0.014434 from 12758697 lines (ArI: 68154, KrI: 27658, NeI: 113596, Trace: 12492853, XeI: 56436) (r4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=29934782.534349 dof=13184414 xRMS=0.016616 yRMS=0.018485 xSoften=0.014679 ySoften=0.014756 from 12907391 lines (ArI: 63809, KrI: 25777, NeI: 121589, Trace: 12629096, XeI: 67120) (n1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=24881715.186988 dof=10531070 xRMS=0.023337 yRMS=0.041044 xSoften=0.019363 ySoften=0.033661 from 10424378 lines (ArI: 51573, KrI: 9336, NeI: 27182, Trace: 10316424, XeI: 19863) (n2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=30555501.108077 dof=7792104 xRMS=0.021727 yRMS=0.044413 xSoften=0.023384 ySoften=0.041866 from 7687007 lines (ArI: 48512, KrI: 9057, NeI: 26076, Trace: 7580676, XeI: 22686) (n3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=34396254.833888 dof=9368745 xRMS=0.020619 yRMS=0.047238 xSoften=0.020070 ySoften=0.051553 from 9254990 lines (ArI: 52328, KrI: 9207, NeI: 29943, Trace: 9139963, XeI: 23549) (m1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=29341520.983447 dof=12832557 xRMS=0.022041 yRMS=0.018298 xSoften=0.018546 ySoften=0.017795 from 12659409 lines (ArI: 31228, KrI: 30013, NeI: 70219, Trace: 12484993, XeI: 42956) (m2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=25225599.834047 dof=12028787 xRMS=0.023027 yRMS=0.019033 xSoften=0.018720 ySoften=0.017868 from 11877184 lines (ArI: 29968, KrI: 28561, NeI: 63768, Trace: 11724341, XeI: 30546) (m3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=27701383.752870 dof=12709725 xRMS=0.024453 yRMS=0.023058 xSoften=0.019908 ySoften=0.021540 from 12552180 lines (ArI: 23842, KrI: 28960, NeI: 69417, Trace: 12393357, XeI: 36604) (m4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=26377082.509995 dof=12807863 xRMS=0.021994 yRMS=0.020176 xSoften=0.017484 ySoften=0.018413 from 12644289 lines (ArI: 27379, KrI: 26404, NeI: 70672, Trace: 12479443, XeI: 40391) </pre> </div></div> <p>The results are basically good for doSlitOffsets=True except for b1. <br/> The script is in /work/kiyoyabe/drp/calibs/generateCALIBs/2024jan/update_detectorMaps/process.sh and the reruns are kiyoyabe/calibs/2024jan/20240201 and kiyoyabe/calibs/2024jan/20240201-2.</p> <p>detectorMaps before 12/28 are also regenerated.</p> <p>With doSlitOffsets:</p> <div class="code panel" style="border-width: 1px;"><div class="codeContent panelContent"> <pre class="code-python"> (b1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=32523615.517450 dof=10762267 xRMS=0.022305 yRMS=0.153569 xSoften=0.019694 ySoften=0.190640 <span class="code-keyword">from</span> 10728639 lines (KrI: 8278, NeI: 26274, Trace: 10693739, XeI: 348) (b2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=27030738.188432 dof=10398006 xRMS=0.023096 yRMS=0.029890 xSoften=0.021252 ySoften=0.042375 <span class="code-keyword">from</span> 10347914 lines (KrI: 13875, NeI: 36229, Trace: 10296550, XeI: 1260) (b3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=22748128.768425 dof=10813851 xRMS=0.021693 yRMS=0.030402 xSoften=0.017388 ySoften=0.031811 <span class="code-keyword">from</span> 10755149 lines (ArI: 8, KrI: 17264, NeI: 40542, Trace: 10695167, XeI: 2168) (b4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=22402765.524722 dof=10881889 xRMS=0.023161 yRMS=0.034262 xSoften=0.017946 ySoften=0.043064 <span class="code-keyword">from</span> 10831477 lines (KrI: 14232, NeI: 35754, Trace: 10779787, XeI: 1704) (r1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=37396790.988725 dof=14062377 xRMS=0.016862 yRMS=0.017485 xSoften=0.016119 ySoften=0.016375 <span class="code-keyword">from</span> 13786992 lines (ArI: 46077, KrI: 47332, NeI: 116812, Trace: 13510333, XeI: 66438) (r2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=34545617.146477 dof=13515894 xRMS=0.018637 yRMS=0.021583 xSoften=0.017415 ySoften=0.020777 <span class="code-keyword">from</span> 13237993 lines (ArI: 54226, KrI: 54252, NeI: 111674, Trace: 12958816, XeI: 59025) (r3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=28147413.885080 dof=13198939 xRMS=0.018625 yRMS=0.017084 xSoften=0.016367 ySoften=0.013627 <span class="code-keyword">from</span> 12913846 lines (ArI: 61198, KrI: 55648, NeI: 114642, Trace: 12627473, XeI: 54885) (r4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=28141029.559018 dof=12605574 xRMS=0.021522 yRMS=0.018036 xSoften=0.020884 ySoften=0.013445 <span class="code-keyword">from</span> 12362943 lines (ArI: 37717, KrI: 46454, NeI: 106703, Trace: 12119032, XeI: 53037) (n1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=25573026.116767 dof=10664263 xRMS=0.023767 yRMS=0.039903 xSoften=0.019844 ySoften=0.033097 <span class="code-keyword">from</span> 10533114 lines (ArI: 68309, KrI: 19044, NeI: 27449, Trace: 10400697, XeI: 17615) (n2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=32718985.073962 dof=9062994 xRMS=0.022129 yRMS=0.042836 xSoften=0.024605 ySoften=0.039836 <span class="code-keyword">from</span> 8940265 lines (ArI: 58912, KrI: 18041, NeI: 26083, Trace: 8816268, XeI: 20961) (n3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=35207348.435563 dof=9548186 xRMS=0.020590 yRMS=0.054249 xSoften=0.020164 ySoften=0.058282 <span class="code-keyword">from</span> 9402655 lines (ArI: 69100, KrI: 21401, NeI: 31453, Trace: 9255848, XeI: 24853) (m1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=35981124.599212 dof=12425009 xRMS=0.016091 yRMS=0.038190 xSoften=0.014710 ySoften=0.044385 <span class="code-keyword">from</span> 12268153 lines (ArI: 14738, KrI: 28032, NeI: 70311, Trace: 12110025, XeI: 45047) (m2)(fitDistortedDetectorMap.py:1337)- Final result: chi2=32239518.853053 dof=12092425 xRMS=0.017638 yRMS=0.020846 xSoften=0.016282 ySoften=0.019417 <span class="code-keyword">from</span> 11942480 lines (ArI: 16046, KrI: 29789, NeI: 67180, Trace: 11791263, XeI: 38202) (m3)(fitDistortedDetectorMap.py:1337)- Final result: chi2=34444851.020252 dof=12086124 xRMS=0.019486 yRMS=0.036357 xSoften=0.017721 ySoften=0.037605 <span class="code-keyword">from</span> 11946455 lines (ArI: 15260, KrI: 19269, NeI: 70062, Trace: 11805508, XeI: 36356) (m4)(fitDistortedDetectorMap.py:1337)- Final result: chi2=25689398.117607 dof=12171776 xRMS=0.018939 yRMS=0.039549 xSoften=0.015524 ySoften=0.040543 <span class="code-keyword">from</span> 12039506 lines (ArI: 10020, KrI: 23161, NeI: 67097, Trace: 11905958, XeI: 33270) </pre> </div></div> <p>b1 detectorMap is not good so turned off doSlitOffsets:</p> <div class="code panel" style="border-width: 1px;"><div class="codeContent panelContent"> <pre class="code-python"> (b1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=43351474.033928 dof=11171213 xRMS=0.025533 yRMS=0.089772 xSoften=0.023151 ySoften=0.104356 <span class="code-keyword">from</span> 11107816 lines (KrI: 12677, NeI: 50117, Trace: 11044341, XeI: 681) </pre> </div></div> <p> <br/> detectorMaps <b>after</b> 12/28/23:</p> <div class='table-wrap'> <table class='confluenceTable'><tbody> <tr> <th class='confluenceTh'>camera</th> <th class='confluenceTh'>xRMS (pix)</th> <th class='confluenceTh'>yRMS (pix)</th> <th class='confluenceTh'>doSlitOffsets</th> </tr> <tr> <td class='confluenceTd'>b1</td> <td class='confluenceTd'>0.026695</td> <td class='confluenceTd'>0.083563</td> <td class='confluenceTd'>False</td> </tr> <tr> <td class='confluenceTd'>b2</td> <td class='confluenceTd'>0.023049</td> <td class='confluenceTd'>0.027032</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>b3</td> <td class='confluenceTd'>0.021811</td> <td class='confluenceTd'>0.028973</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>b4</td> <td class='confluenceTd'>0.021947</td> <td class='confluenceTd'>0.029005</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>r1</td> <td class='confluenceTd'>0.016787</td> <td class='confluenceTd'>0.018428</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>r2</td> <td class='confluenceTd'>0.018357</td> <td class='confluenceTd'>0.019742</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>r3</td> <td class='confluenceTd'>0.018259</td> <td class='confluenceTd'>0.018566</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>r4</td> <td class='confluenceTd'>0.016616</td> <td class='confluenceTd'>0.018485</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>n1</td> <td class='confluenceTd'>0.023337</td> <td class='confluenceTd'>0.041044</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>n2</td> <td class='confluenceTd'>0.021727</td> <td class='confluenceTd'>0.044413</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>n3</td> <td class='confluenceTd'>0.020619</td> <td class='confluenceTd'>0.047238</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>m1</td> <td class='confluenceTd'>0.022041</td> <td class='confluenceTd'>0.018298</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>m2</td> <td class='confluenceTd'>0.023027</td> <td class='confluenceTd'>0.019033</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>m3</td> <td class='confluenceTd'>0.024453</td> <td class='confluenceTd'>0.023058</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>m4</td> <td class='confluenceTd'>0.021994</td> <td class='confluenceTd'>0.020176</td> <td class='confluenceTd'>True</td> </tr> </tbody></table> </div> <p> <br/> detectorMaps <b>before</b> 12/28/23:</p> <p> </p> <div class='table-wrap'> <table class='confluenceTable'><tbody> <tr> <th class='confluenceTh'>camera</th> <th class='confluenceTh'>xRMS (pix)</th> <th class='confluenceTh'>yRMS (pix)</th> <th class='confluenceTh'>doSlitOffsets</th> </tr> <tr> <td class='confluenceTd'>b1</td> <td class='confluenceTd'>0.025533</td> <td class='confluenceTd'>0.089772</td> <td class='confluenceTd'>False</td> </tr> <tr> <td class='confluenceTd'>b2</td> <td class='confluenceTd'>0.023096</td> <td class='confluenceTd'>0.029890</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>b3</td> <td class='confluenceTd'>0.021693</td> <td class='confluenceTd'>0.030402</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>b4</td> <td class='confluenceTd'>0.023161</td> <td class='confluenceTd'>0.034262</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>r1</td> <td class='confluenceTd'>0.016862</td> <td class='confluenceTd'>0.017485</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>r2</td> <td class='confluenceTd'>0.018637</td> <td class='confluenceTd'>0.021583</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>r3</td> <td class='confluenceTd'>0.018625</td> <td class='confluenceTd'>0.017084</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>r4</td> <td class='confluenceTd'>0.021522</td> <td class='confluenceTd'>0.018036</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>n1</td> <td class='confluenceTd'>0.023767</td> <td class='confluenceTd'>0.039903</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>n2</td> <td class='confluenceTd'>0.022129</td> <td class='confluenceTd'>0.042836</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>n3</td> <td class='confluenceTd'>0.020590</td> <td class='confluenceTd'>0.054249</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>m1</td> <td class='confluenceTd'>0.016091</td> <td class='confluenceTd'>0.038190</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>m2</td> <td class='confluenceTd'>0.017638</td> <td class='confluenceTd'>0.020846</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>m3</td> <td class='confluenceTd'>0.019486</td> <td class='confluenceTd'>0.036357</td> <td class='confluenceTd'>True</td> </tr> <tr> <td class='confluenceTd'>m4</td> <td class='confluenceTd'>0.018939</td> <td class='confluenceTd'>0.039549</td> <td class='confluenceTd'>True</td> </tr> </tbody></table> </div> <p> </p> <p> </p> <p>The visit that the DM QA identifies is 104151 in `b1` only.  The Argon visit set is 104149..104153 and all the DMs in that set look the same except for the `b1`.  Attached is visit 104150, which is representative of all the others, and 104151, which is the outlier.</p> <p> </p> <p><span class="image-wrap" style=""><img src="https://pfspipe.ipmu.jp/jira/secure/attachment/17142/17142_Screenshot+2024-02-07+at+10.22.04.png" style="border: 0px solid black" /></span></p> <p>Thank you. Excluding that visit, the result doesn't change much.</p> <div class="code panel" style="border-width: 1px;"><div class="codeContent panelContent"> <pre class="code-python"> (b1)(fitDistortedDetectorMap.py:1337)- Final result: chi2=45636858.630669 dof=11091617 xRMS=0.026689 yRMS=0.083871 xSoften=0.024572 ySoften=0.098460 <span class="code-keyword">from</span> 11033843 lines (KrI: 6815, NeI: 49758, Trace: 10975991, XeI: 1279) </pre> </div></div> <p> </p> Development Rank 0|zzt0xa:w Sprint PreEngRun15Mar