Happy holidaysHappy holidays|
Dear Edward & Michael, I wish you some nice holidays and a good start into the new year. The last time I had sent you examples of the PDC ouput. I would assume that you can easily read it via Python and that your Python programs are flexible. I will make sure that the format stays in its essential components, i.e. values are tab separated and the sections start with the keywords used so far. May I ask if you in what detail you read other keywords ? Example: The section with the integrals looks like: SECTION: used integrals Mixing time [s]: 0.01 0.03 ......... Would you suffer if I changed Mixing time into Mixing times ? Or do you just read a token up to : ? The reason why I'm asking that someone looked through export and report and found such minor differences. Other examples are that in between the tab separated title lines of the tables, e.g. the title line Peak name F1[ppm] F2[ppm] .... sometimes additional spaces are contained, sometimes 2, sometimes 3. Do you evaluate these title lines at all or just the relevant table that follows ? Early next year we should write down which parts of the output must no be changed by myself to avoid conflicts. Best regards, Peter On 8:59 PM, Edward d'Auvergne wrote: Arrrhhh, this is not good. Something is wrong with the Gna! site! http://gna.org/ says the following: "We're investigating a potential security issue. Apologizes for the inconvenience." This is not good at all! We will need to wait until the website is back up. The mailing lists and other infrastructure seems to be still functional though. For the attachments of large files, this should not be done on a public mailing list as it causes big problems with internet traffic and the Gna! infrastructure. It is broadcast out to many people and the message (and attached files) are archived across the internet (for example http://www.mail-archive.com/relax-devel@xxxxxxx/). So it puts a lot of strain on the system. It is best to attach it to the task https://gna.org/task/?7180, but this link will not work until the Gna! website is back up. Big attachments should be caught though and the message blocked. The new files look quite good and seem to have the complete information needed. And the higher precision should avoid truncation errors sneeking into the subsequent model-free analysis. Just to be sure with the errors, if we have: Peak name F1 [ppm] F2 [ppm] T2[s] error scale Gln [2] 122.508 8.898 0.1752 0.0026246 222814 Then the unscaled error for this T2 is 0.005847976244? I will take a look at the manual and see what we can do. We have to now implement the code to read the PDC files, though this should be trivial with the current file format. For the text, it can possibly be expanded by following from the previous chapter and explaining the models M6-M9 and TM6-TM9, the ellipsoidal diffusion, etc. Maybe that the user has full flexibility with relax to do as they wish, the can do reduce spectral density mapping or model-free analysis, perform model-free analysis in any way they wish, but if they have data at 2 or more field strengths they can take advantage of the automatic protocols. relax now has a GUI, thanks to Michael Bieri, but it might be better to wait until that is published. I might try to come up with some text, but that will probably have to wait a number of days. Cheers, Edward On 30 November 2010 14:40, Dr. Klaus-Peter Neidig <peter.neidig@xxxxxxxxxxxxxxxxx> wrote:Hi, accessing your various links causes problems, either I get something like "The requested URL /task/index.php was not found on this server." or there is some security issue. In any case, I have implemented the average variance method as discussed and also changed the export functionality. Doing more changes in the export area is no problem if needed. The changes so far include that besides integrals or decay rates also the corresponding errors are shown in a further table/section. Concerning the fit parameter output I have added a column called scale. This value (always the same for a given confidence value and number of degrees of freedom was multiplied to the error obtained by fit or MC. You can just do the reverse. Finally, I have added more digits and more infos. e.g. which method was used to calculate systematic errors. May I just attach examples to this mail. One of the next things I should certainly enhance is our manual. I will also attach a copy of that. The last chapter contains a very short part referring to relax. There, I should certainly do more. We might also think to describe more details of relax itself just as an advertisement, what do you think ? If you want to provide some (unformatted) text or other pictures I can add those. The current PDC version is now 1.1.3 a next version is roughly planned for early January since I have to switch between projects in between. Best regards, Peter On 11/29/2010 11:08 PM, Edward d'Auvergne wrote: Hi, I've converted the scripts to Java for a reference (and to see how quick I can learn Java ;) which surprisingly wasn't very hard). These can be accessed at https://gna.org/task/index.php?7180#comment4. I'll take the data you included in the post, and calculate what the error estimates should be using the 3 different methods and send another message with the results. Cheers, Edward P.S. As you mention, the error is the sqrt of the mean variance. On 29 November 2010 21:09, Neidig Klaus-Peter <Klaus-Peter.Neidig@xxxxxxxxxxxxxxxxx> wrote: Hi, sorry, I didn't find enough time to look at your scripts so far. I'm not a python programmer but the PDC is in Java which is not so far away from python. I ran my calculation again with a t1 dataset. 2 Mixing times (0.1s and 0.5s) were replicated. Below there is a listing of the the peaks, each with pairwise measured intensities and the calculated variance. As you see the average variance is much higher than the differences. One should finally take the sqrt of the mean variance, right ? suppose we have mixing times 10, 10, 10 (3 replicates) 20 30, 30 (2 replicates) 40 50 and spectra with 3 peaks. A: I would take peak1, then check for the largest difference in intensity in the first 3 replicates and in the 2 replicates. The largest found difference is then assigned as a systematic error to peak1 at all mixing times. Is an absolute value is used for finding the maximum difference? So say in the mixing time of 10, the intensities are 1, 4, 2 so the max difference is 3. And in the 30 has intensities of 0.9 and 3. So the error of peak 1 would be 3. Is this correct? yes I think A: and C: should be offered. After implementing this I see my data that the C: produces much larger errors than A and the errors of the fitted parameters get much larger, sometimes larger than the values itself. This seems not acceptable. >From my tests, the errors should be bigger with the A procedure. This is strange! It might be worth seeing if the C procedure implementation matches that in https://gna.org/support/download.php?file_id=11440. I may have also have made a mistake within the logic of these testing scripts! If one takes the sqrt of the average variance then indeed method A produces somewhat larger errors. I think, I offer A (our current method) together with C (your method). Best regards, Peter peak = 0 tm = 0.1 I = 4.6579229E7 I = 4.7092814E7 variance = 6.594238805625E10 diff = 513585.0 peak = 1 tm = 0.1 I = 6.5538757E7 I = 6.6204377E7 variance = 1.107624961E11 diff = 665620.0 peak = 2 tm = 0.1 I = 1.21932973E8 I = 1.23353789E8 variance = 5.04679526464E11 diff = 1420816.0 peak = 3 tm = 0.1 I = 9.6274144E7 I = 9.7964494E7 variance = 7.14320780625E11 diff = 1690350.0 peak = 4 tm = 0.1 I = 1.06222149E8 I = 1.06097448E8 variance = 3.88758485025E9 diff = 124701.0 peak = 5 tm = 0.1 I = 1.10164756E8 I = 1.12050649E8 variance = 8.8914810186225E11 diff = 1885893.0 peak = 6 tm = 0.1 I = 1.15388575E8 I = 1.14537365E8 variance = 1.81139616025E11 diff = 851210.0 peak = 7 tm = 0.1 I = 1.15486414E8 I = 1.14394665E8 variance = 2.9797896975025E11 diff = 1091749.0 peak = 8 tm = 0.1 I = 1.14525583E8 I = 1.14547343E8 variance = 1.183744E8 diff = 21760.0 peak = 9 tm = 0.1 I = 1.03498437E8 I = 1.03430099E8 variance = 1.167520561E9 diff = 68338.0 peak = 10 tm = 0.1 I = 1.07762024E8 I = 1.08701708E8 variance = 2.20751504964E11 diff = 939684.0 peak = 11 tm = 0.1 I = 1.17546981E8 I = 1.19479003E8 variance = 9.33177252121E11 diff = 1932022.0 peak = 12 tm = 0.1 I = 1.20946012E8 I = 1.21216828E8 variance = 1.8335326464E10 diff = 270816.0 peak = 13 tm = 0.1 I = 1.08270684E8 I = 1.07100014E8 variance = 3.42617062225E11 diff = 1170670.0 peak = 14 tm = 0.1 I = 1.17589408E8 I = 1.19154699E8 variance = 6.1253397867025E11 diff = 1565291.0 peak = 15 tm = 0.1 I = 1.06143297E8 I = 1.11806654E8 variance = 8.01840312736225E12 diff = 5663357.0 peak = 16 tm = 0.1 I = 1.26524839E8 I = 1.27556879E8 variance = 2.662766404E11 diff = 1032040.0 peak = 17 tm = 0.1 I = 1.17952859E8 I = 1.19287488E8 variance = 4.4530864191025E11 diff = 1334629.0 peak = 18 tm = 0.1 I = 1.47666938E8 I = 1.4852416E8 variance = 1.83707389321E11 diff = 857222.0 peak = 19 tm = 0.1 I = 1.23776848E8 I = 1.22882896E8 variance = 1.99787544576E11 diff = 893952.0 peak = 20 tm = 0.1 I = 1.30533396E8 I = 1.27649681E8 variance = 2.07895305030625E12 diff = 2883715.0 peak = 21 tm = 0.1 I = 1.6891493E8 I = 1.67411869E8 variance = 5.6479809243025E11 diff = 1503061.0 peak = 22 tm = 0.1 I = 1.35523059E8 I = 1.36722043E8 variance = 3.59390658064E11 diff = 1198984.0 peak = 23 tm = 0.1 I = 1.27802315E8 I = 1.29052997E8 variance = 3.91051366281E11 diff = 1250682.0 peak = 24 tm = 0.1 I = 1.17016675E8 I = 1.18095171E8 variance = 2.90788405504E11 diff = 1078496.0 peak = 25 tm = 0.1 I = 1.29967983E8 I = 1.3044962E8 variance = 5.799354994225E10 diff = 481637.0 peak = 26 tm = 0.1 I = 1.36634823E8 I = 1.35990949E8 variance = 1.03643431969E11 diff = 643874.0 peak = 27 tm = 0.1 I = 1.58302631E8 I = 1.59486024E8 variance = 3.5010474811225E11 diff = 1183393.0 peak = 28 tm = 0.1 I = 1.38726372E8 I = 1.39593108E8 variance = 1.87807823424E11 diff = 866736.0 peak = 29 tm = 0.1 I = 1.42372202E8 I = 1.39627085E8 variance = 1.88391683592225E12 diff = 2745117.0 peak = 30 tm = 0.1 I = 1.5055807E8 I = 1.50011046E8 variance = 7.4808814144E10 diff = 547024.0 peak = 31 tm = 0.1 I = 1.32578855E8 I = 1.31810056E8 variance = 1.4776297560025E11 diff = 768799.0 peak = 32 tm = 0.1 I = 1.25931036E8 I = 1.27087384E8 variance = 3.34285174276E11 diff = 1156348.0 peak = 33 tm = 0.1 I = 1.29676769E8 I = 1.26021573E8 variance = 3.340114449604E12 diff = 3655196.0 peak = 34 tm = 0.1 I = 1.88839431E8 I = 1.94279364E8 variance = 7.39821776112225E12 diff = 5439933.0 peak = 35 tm = 0.1 I = 1.47419506E8 I = 1.47923225E8 variance = 6.343320774025E10 diff = 503719.0 peak = 36 tm = 0.1 I = 1.46650015E8 I = 1.50163517E8 variance = 3.086174076001E12 diff = 3513502.0 peak = 37 tm = 0.1 I = 1.58540567E8 I = 1.60266729E8 variance = 7.44908812561E11 diff = 1726162.0 peak = 38 tm = 0.1 I = 1.65899251E8 I = 1.65095264E8 variance = 1.6159877404225E11 diff = 803987.0 peak = 39 tm = 0.1 I = 1.95564053E8 I = 1.95696906E8 variance = 4.41247990225E9 diff = 132853.0 peak = 40 tm = 0.1 I = 1.63186285E8 I = 1.6658136E8 variance = 2.88163356390625E12 diff = 3395075.0 peak = 41 tm = 0.1 I = 1.55029756E8 I = 1.53590916E8 variance = 5.175651364E11 diff = 1438840.0 peak = 42 tm = 0.1 I = 1.4958378E8 I = 1.47708405E8 variance = 8.7925784765625E11 diff = 1875375.0 peak = 43 tm = 0.1 I = 1.6560712E8 I = 1.64228419E8 variance = 4.7520411185025E11 diff = 1378701.0 peak = 44 tm = 0.1 I = 1.50697528E8 I = 1.51946572E8 variance = 3.90027728484E11 diff = 1249044.0 peak = 45 tm = 0.1 I = 1.49555131E8 I = 1.49561168E8 variance = 9111342.25 diff = 6037.0 peak = 46 tm = 0.1 I = 1.58015972E8 I = 1.57904736E8 variance = 3.093361924E9 diff = 111236.0 peak = 47 tm = 0.1 I = 1.97131421E8 I = 1.96490618E8 variance = 1.0265712120225E11 diff = 640803.0 peak = 48 tm = 0.1 I = 1.82176188E8 I = 1.81782509E8 variance = 3.874578876025E10 diff = 393679.0 peak = 49 tm = 0.1 I = 1.5198114E8 I = 1.52700081E8 variance = 1.2921904037025E11 diff = 718941.0 peak = 50 tm = 0.1 I = 1.61473483E8 I = 1.63244378E8 variance = 7.8401727525625E11 diff = 1770895.0 peak = 51 tm = 0.1 I = 1.48631233E8 I = 1.46815888E8 variance = 8.2386936725625E11 diff = 1815345.0 peak = 52 tm = 0.1 I = 1.81950724E8 I = 1.83639051E8 variance = 7.1261201473225E11 diff = 1688327.0 peak = 53 tm = 0.1 I = 1.69073538E8 I = 1.67487239E8 variance = 6.2908612935025E11 diff = 1586299.0 peak = 54 tm = 0.1 I = 1.61282534E8 I = 1.59685797E8 variance = 6.3739226179225E11 diff = 1596737.0 peak = 55 tm = 0.1 I = 1.5709911E8 I = 1.5600867E8 variance = 2.972648484E11 diff = 1090440.0 peak = 56 tm = 0.1 I = 1.6611418E8 I = 1.67053174E8 variance = 2.20427433009E11 diff = 938994.0 peak = 57 tm = 0.1 I = 1.46584627E8 I = 1.50749529E8 variance = 4.336602167401E12 diff = 4164902.0 peak = 58 tm = 0.1 I = 1.70725817E8 I = 1.71664068E8 variance = 2.2007873475025E11 diff = 938251.0 peak = 59 tm = 0.1 I = 1.64853593E8 I = 1.63313105E8 variance = 5.93275819536E11 diff = 1540488.0 peak = 60 tm = 0.1 I = 1.51200708E8 I = 1.5158809E8 variance = 3.7516203481E10 diff = 387382.0 peak = 61 tm = 0.1 I = 1.9012644E8 I = 1.89307384E8 variance = 1.67713182784E11 diff = 819056.0 peak = 62 tm = 0.1 I = 1.75656082E8 I = 1.73752539E8 variance = 9.0586898821225E11 diff = 1903543.0 peak = 63 tm = 0.1 I = 1.63517301E8 I = 1.63740568E8 variance = 1.246203832225E10 diff = 223267.0 peak = 64 tm = 0.1 I = 1.85905154E8 I = 1.86077342E8 variance = 7.412176836E9 diff = 172188.0 peak = 65 tm = 0.1 I = 1.84562498E8 I = 1.90140009E8 variance = 7.77715723878025E12 diff = 5577511.0 peak = 66 tm = 0.1 I = 2.0236154E8 I = 2.04490091E8 variance = 1.13268233990025E12 diff = 2128551.0 peak = 67 tm = 0.1 I = 2.02161335E8 I = 2.0267724E8 variance = 6.653949225625E10 diff = 515905.0 peak = 68 tm = 0.1 I = 2.30185994E8 I = 2.31846663E8 variance = 6.8945538189025E11 diff = 1660669.0 peak = 69 tm = 0.1 I = 3.79864243E8 I = 3.79113005E8 variance = 1.41089633161E11 diff = 751238.0 peak = 0 tm = 0.5 I = 1.9563653E7 I = 1.9746012E7 variance = 8.31370122025E9 diff = 182359.0 peak = 1 tm = 0.5 I = 3.0312041E7 I = 3.0297906E7 variance = 4.994955625E7 diff = 14135.0 peak = 2 tm = 0.5 I = 4.8577923E7 I = 4.9077995E7 variance = 6.2518001296E10 diff = 500072.0 peak = 3 tm = 0.5 I = 4.1269809E7 I = 3.9238878E7 variance = 1.03117018169025E12 diff = 2030931.0 peak = 4 tm = 0.5 I = 4.3359729E7 I = 4.1922537E7 variance = 5.16380211216E11 diff = 1437192.0 peak = 5 tm = 0.5 I = 4.1514679E7 I = 4.0799801E7 variance = 1.27762638721E11 diff = 714878.0 peak = 6 tm = 0.5 I = 4.5743324E7 I = 4.7463749E7 variance = 7.3996554515625E11 diff = 1720425.0 peak = 7 tm = 0.5 I = 4.6234985E7 I = 4.586273E7 variance = 3.464344625625E10 diff = 372255.0 peak = 8 tm = 0.5 I = 4.6488924E7 I = 4.6747152E7 variance = 1.6670424996E10 diff = 258228.0 peak = 9 tm = 0.5 I = 4.0829824E7 I = 4.1911615E7 variance = 2.9256794192025E11 diff = 1081791.0 peak = 10 tm = 0.5 I = 6.7062951E7 I = 6.8482726E7 variance = 5.0394026265625E11 diff = 1419775.0 peak = 11 tm = 0.5 I = 4.8402696E7 I = 4.8185607E7 variance = 1.178190848025E10 diff = 217089.0 peak = 12 tm = 0.5 I = 5.1163711E7 I = 5.2797973E7 variance = 6.67703071161E11 diff = 1634262.0 peak = 13 tm = 0.5 I = 4.323635E7 I = 4.4260399E7 variance = 2.6216908860025E11 diff = 1024049.0 peak = 14 tm = 0.5 I = 4.656801E7 I = 4.7185417E7 variance = 9.529785091225E10 diff = 617407.0 peak = 15 tm = 0.5 I = 4.3503199E7 I = 4.3946518E7 variance = 4.913293394025E10 diff = 443319.0 peak = 16 tm = 0.5 I = 5.2497166E7 I = 5.2595045E7 variance = 2.39507466025E9 diff = 97879.0 peak = 17 tm = 0.5 I = 4.5800415E7 I = 4.5733428E7 variance = 1.12181454225E9 diff = 66987.0 peak = 18 tm = 0.5 I = 5.7608294E7 I = 5.8646397E7 variance = 2.6941445965225E11 diff = 1038103.0 peak = 19 tm = 0.5 I = 4.8516943E7 I = 4.9073731E7 variance = 7.7503219236E10 diff = 556788.0 peak = 20 tm = 0.5 I = 5.3655693E7 I = 5.3809611E7 variance = 5.922687681E9 diff = 153918.0 peak = 21 tm = 0.5 I = 6.8565235E7 I = 6.9031018E7 variance = 5.423845077225E10 diff = 465783.0 peak = 22 tm = 0.5 I = 5.1375755E7 I = 5.2188109E7 variance = 1.64979755329E11 diff = 812354.0 peak = 23 tm = 0.5 I = 5.1810263E7 I = 5.2344521E7 variance = 7.1357902641E10 diff = 534258.0 peak = 24 tm = 0.5 I = 4.6199502E7 I = 4.6016621E7 variance = 8.36136504025E9 diff = 182881.0 peak = 25 tm = 0.5 I = 5.39143E7 I = 5.3985173E7 variance = 1.25574553225E9 diff = 70873.0 peak = 26 tm = 0.5 I = 5.2627948E7 I = 5.2676455E7 variance = 5.8823226225E8 diff = 48507.0 peak = 27 tm = 0.5 I = 6.3678183E7 I = 6.4471977E7 variance = 1.57527228609E11 diff = 793794.0 peak = 28 tm = 0.5 I = 5.7015817E7 I = 5.6609499E7 variance = 4.1273579281E10 diff = 406318.0 peak = 29 tm = 0.5 I = 6.051052E7 I = 6.1354983E7 variance = 1.7827943959225E11 diff = 844463.0 peak = 30 tm = 0.5 I = 6.0634749E7 I = 6.1111522E7 variance = 5.682812338225E10 diff = 476773.0 peak = 31 tm = 0.5 I = 5.1288385E7 I = 5.2392778E7 variance = 3.0492097461225E11 diff = 1104393.0 peak = 32 tm = 0.5 I = 4.9933573E7 I = 4.9808151E7 variance = 3.932669521E9 diff = 125422.0 peak = 33 tm = 0.5 I = 5.13737E7 I = 5.2088949E7 variance = 1.2789528300025E11 diff = 715249.0 peak = 34 tm = 0.5 I = 8.3226525E7 I = 8.1710722E7 variance = 5.7441468370225E11 diff = 1515803.0 peak = 35 tm = 0.5 I = 6.2873606E7 I = 6.2786605E7 variance = 1.89229350025E9 diff = 87001.0 peak = 36 tm = 0.5 I = 5.7373548E7 I = 5.567159E7 variance = 7.24165258441E11 diff = 1701958.0 peak = 37 tm = 0.5 I = 6.3317661E7 I = 6.405389E7 variance = 1.3550828511025E11 diff = 736229.0 peak = 38 tm = 0.5 I = 6.7781756E7 I = 6.7018349E7 variance = 1.4569756191225E11 diff = 763407.0 peak = 39 tm = 0.5 I = 8.7420968E7 I = 8.8893101E7 variance = 5.4179389242225E11 diff = 1472133.0 peak = 40 tm = 0.5 I = 6.3010012E7 I = 6.4053853E7 variance = 2.7240100832025E11 diff = 1043841.0 peak = 41 tm = 0.5 I = 6.4045596E7 I = 6.3158976E7 variance = 1.965237561E11 diff = 886620.0 peak = 42 tm = 0.5 I = 5.9510986E7 I = 6.117577E7 variance = 6.92876441664E11 diff = 1664784.0 peak = 43 tm = 0.5 I = 7.4016447E7 I = 7.463353E7 variance = 9.519785722225E10 diff = 617083.0 peak = 44 tm = 0.5 I = 5.7766643E7 I = 5.8410938E7 variance = 1.0377901175625E11 diff = 644295.0 peak = 45 tm = 0.5 I = 6.0520977E7 I = 6.0785249E7 variance = 1.7459922496E10 diff = 264272.0 peak = 46 tm = 0.5 I = 6.4723785E7 I = 6.516751E7 variance = 4.922296890625E10 diff = 443725.0 peak = 47 tm = 0.5 I = 7.9676985E7 I = 8.0747732E7 variance = 2.8662478450225E11 diff = 1070747.0 peak = 48 tm = 0.5 I = 7.2206981E7 I = 7.1441266E7 variance = 1.4657986530625E11 diff = 765715.0 peak = 49 tm = 0.5 I = 6.3133692E7 I = 6.4204513E7 variance = 2.8666440351025E11 diff = 1070821.0 peak = 50 tm = 0.5 I = 8.4545481E7 I = 8.489639E7 variance = 3.078428157025E10 diff = 350909.0 peak = 51 tm = 0.5 I = 5.723598E7 I = 5.7096426E7 variance = 4.868829729E9 diff = 139554.0 peak = 52 tm = 0.5 I = 7.668595E7 I = 7.668635E7 variance = 40000.0 diff = 400.0 peak = 53 tm = 0.5 I = 6.5137953E7 I = 6.4910564E7 variance = 1.292643933025E10 diff = 227389.0 peak = 54 tm = 0.5 I = 6.454268E7 I = 6.4903073E7 variance = 3.247077861225E10 diff = 360393.0 peak = 55 tm = 0.5 I = 6.6980952E7 I = 6.6344623E7 variance = 1.0122864906025E11 diff = 636329.0 peak = 56 tm = 0.5 I = 6.8266768E7 I = 6.8204704E7 variance = 9.62985024E8 diff = 62064.0 peak = 57 tm = 0.5 I = 6.0170695E7 I = 6.1619711E7 variance = 5.24911842064E11 diff = 1449016.0 peak = 58 tm = 0.5 I = 7.5589849E7 I = 7.6549623E7 variance = 2.30291532769E11 diff = 959774.0 peak = 59 tm = 0.5 I = 7.0466565E7 I = 7.2501426E7 variance = 1.03516482233025E12 diff = 2034861.0 peak = 60 tm = 0.5 I = 6.2742003E7 I = 6.298303E70 variance = 1.452350368225E10 diff = 241027.0 peak = 61 tm = 0.5 I = 7.5699056E7 I = 7.5756423E7 variance = 8.2274317225E8 diff = 57367.0 peak = 62 tm = 0.5 I = 7.2902312E7 I = 7.2526609E7 variance = 3.528818605225E10 diff = 375703.0 peak = 63 tm = 0.5 I = 7.0063736E7 I = 7.0482549E7 variance = 4.385108224225E10 diff = 418813.0 peak = 64 tm = 0.5 I = 7.5061455E7 I = 7.4535024E7 variance = 6.928239944025E10 diff = 526431.0 peak = 65 tm = 0.5 I = 8.1217506E7 I = 8.1240784E7 variance = 1.35466321E8 diff = 23278.0 peak = 66 tm = 0.5 I = 9.581976E7 I = 9.6013079E7 variance = 9.34305894025E9 diff = 193319.0 peak = 67 tm = 0.5 I = 7.9184293E7 I = 7.9308447E7 variance = 3.853553929E9 diff = 124154.0 peak = 68 tm = 0.5 I = 9.6049008E7 I = 9.7593757E7 variance = 5.9656236825025E11 diff = 1544749.0 peak = 69 tm = 0.5 I = 2.78284758E8 I = 2.78155671E8 variance = 4.16586339225E9 diff = 129087.0 totalvariance = 7.421424743656775E13 number of calculated Variances = 140 mean variance = 5.3010176740405536E11 sqrt(mean var) = 728080.8797132743 . -- Bruker BioSpin ________________________________ Dr. Klaus-Peter Neidig Head of Analysis Group NMR Software Development Bruker BioSpin GmbH Silberstreifen 4 76287 Rheinstetten Germany Phone: +49 721 5161-6447 Fax: +49 721 5161-6480 peter.neidig@xxxxxxxxxxxxxxxxx www.bruker-biospin.com ________________________________ Bruker BioSpin GmbH: Sitz der Gesellschaft/Registered Office: Rheinstetten, HRB 102368 Amtsgericht Mannheim Geschäftsführer/Managing Directors: Joerg Laukien, Dr. Bernd Gewiese, Dr. Dieter Schmalbein, Dr. Gerhard Roth Diese E-Mail und alle Anlagen können Betriebs- oder Geschäftsgeheimnisse, oder sonstige vertrauliche Informationen enthalten. 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