Dear Stefano,
from this it is clear that eddy has finished estimating the distortions and then crashes in the stages of "post-processing" those estimates. This is precisely the behaviour we would expect from the bug we had before but which has been fixed.
So my theory is that after all you are not running the latest version. What do you think?
Jesper
On 15 Feb 2013, at 20:51, Marenco, Stefano (NIH/NIMH) [E] wrote:
> Jesper, any further suggestions on how to proceed?
> I re-ran eddy with -topup option and the --very_verbose flag and the output is listed below. I also re-ran eddy without the -topup option and the --very_verbose flag. That one made it all the way through these same pieces successfully and that was the end. So, my guess is it's failing when trying to apply the topup results.
>
>
> eddy --imain=allRawData.nii.gz --mask=nodif_brain_mask.nii --acqp=my_acq_param_allData.txt --index=allData_index.txt --bvecs=bvecs_allData --bvals=bvals_allData --topup=my_topup_results --out=eddy_wTopup_out --very_verbose Reading images Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Evaluating prediction maker model Calculating parameter updates
> Iter: 0, scan: 0, mss = 41.302414
> Iter: 0, scan: 1, mss = 39.063176
> Iter: 0, scan: 2, mss = 40.564474
> Iter: 0, scan: 3, mss = 38.813325
> Iter: 0, scan: 4, mss = 38.103097
> Iter: 0, scan: 5, mss = 37.356510
> Iter: 0, scan: 6, mss = 40.120006
> Iter: 0, scan: 7, mss = 37.635694
> Iter: 0, scan: 8, mss = 38.920864
> Iter: 0, scan: 9, mss = 36.321454
> Iter: 0, scan: 10, mss = 35.842110
> Iter: 0, scan: 11, mss = 36.214662
> Iter: 0, scan: 12, mss = 37.338151
> Iter: 0, scan: 13, mss = 35.962403
> Iter: 0, scan: 14, mss = 36.369726
> Iter: 0, scan: 15, mss = 35.055187
> Iter: 0, scan: 16, mss = 36.873455
> Iter: 0, scan: 17, mss = 35.534278
> Iter: 0, scan: 18, mss = 34.641102
> Iter: 0, scan: 19, mss = 35.060710
> Iter: 0, scan: 20, mss = 39.986350
> Iter: 0, scan: 21, mss = 36.056859
> Iter: 0, scan: 22, mss = 38.503923
> Iter: 0, scan: 23, mss = 38.693200
> Iter: 0, scan: 24, mss = 38.736633
> Iter: 0, scan: 25, mss = 39.681401
> Iter: 0, scan: 26, mss = 41.684141
> Iter: 0, scan: 27, mss = 37.176572
> Iter: 0, scan: 28, mss = 36.467017
> Iter: 0, scan: 29, mss = 42.856322
> Iter: 0, scan: 30, mss = 42.645119
> Iter: 0, scan: 31, mss = 41.990345
> Iter: 0, scan: 32, mss = 39.651330
> Iter: 0, scan: 33, mss = 42.686622
> Iter: 0, scan: 34, mss = 38.547333
> Iter: 0, scan: 35, mss = 38.552440
> Iter: 0, scan: 36, mss = 40.747862
> Iter: 0, scan: 37, mss = 38.957232
> Iter: 0, scan: 38, mss = 41.113287
> Iter: 0, scan: 39, mss = 38.809636
> Iter: 0, scan: 40, mss = 36.308450
> Iter: 0, scan: 41, mss = 37.556948
> Iter: 0, scan: 42, mss = 37.058360
> Iter: 0, scan: 43, mss = 39.171197
> Iter: 0, scan: 44, mss = 40.676398
> Iter: 0, scan: 45, mss = 37.341045
> Iter: 0, scan: 46, mss = 39.193152
> Iter: 0, scan: 47, mss = 35.845493
> Iter: 0, scan: 48, mss = 36.099977
> Iter: 0, scan: 49, mss = 38.221167
> Iter: 0, scan: 50, mss = 37.806978
> Iter: 0, scan: 51, mss = 41.830090
> Iter: 0, scan: 52, mss = 39.852929
> Iter: 0, scan: 53, mss = 41.773500
> Iter: 0, scan: 54, mss = 36.539026
> Iter: 0, scan: 55, mss = 38.655944
> Iter: 0, scan: 56, mss = 46.534517
> Iter: 0, scan: 57, mss = 39.033364
> Iter: 0, scan: 58, mss = 37.385028
> Iter: 0, scan: 59, mss = 38.867102
> Iter: 0, Total mss = 38.6065
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Evaluating prediction maker model Calculating parameter updates
> Iter: 1, scan: 0, mss = 38.414671
> Iter: 1, scan: 1, mss = 35.513856
> Iter: 1, scan: 2, mss = 38.721699
> Iter: 1, scan: 3, mss = 36.877621
> Iter: 1, scan: 4, mss = 36.110165
> Iter: 1, scan: 5, mss = 35.899144
> Iter: 1, scan: 6, mss = 37.159387
> Iter: 1, scan: 7, mss = 35.038139
> Iter: 1, scan: 8, mss = 37.197174
> Iter: 1, scan: 9, mss = 34.737045
> Iter: 1, scan: 10, mss = 33.520791
> Iter: 1, scan: 11, mss = 35.194708
> Iter: 1, scan: 12, mss = 35.896989
> Iter: 1, scan: 13, mss = 33.911532
> Iter: 1, scan: 14, mss = 34.968624
> Iter: 1, scan: 15, mss = 33.677513
> Iter: 1, scan: 16, mss = 35.056669
> Iter: 1, scan: 17, mss = 34.216793
> Iter: 1, scan: 18, mss = 32.741345
> Iter: 1, scan: 19, mss = 34.196600
> Iter: 1, scan: 20, mss = 36.019846
> Iter: 1, scan: 21, mss = 36.475146
> Iter: 1, scan: 22, mss = 36.406632
> Iter: 1, scan: 23, mss = 35.325223
> Iter: 1, scan: 24, mss = 37.780682
> Iter: 1, scan: 25, mss = 34.471474
> Iter: 1, scan: 26, mss = 40.528812
> Iter: 1, scan: 27, mss = 35.304493
> Iter: 1, scan: 28, mss = 34.105399
> Iter: 1, scan: 29, mss = 38.099558
> Iter: 1, scan: 30, mss = 38.439993
> Iter: 1, scan: 31, mss = 35.990677
> Iter: 1, scan: 32, mss = 35.893783
> Iter: 1, scan: 33, mss = 36.979231
> Iter: 1, scan: 34, mss = 35.643321
> Iter: 1, scan: 35, mss = 35.989610
> Iter: 1, scan: 36, mss = 38.000433
> Iter: 1, scan: 37, mss = 35.423315
> Iter: 1, scan: 38, mss = 37.392252
> Iter: 1, scan: 39, mss = 35.552864
> Iter: 1, scan: 40, mss = 33.763459
> Iter: 1, scan: 41, mss = 35.175598
> Iter: 1, scan: 42, mss = 34.240772
> Iter: 1, scan: 43, mss = 35.653763
> Iter: 1, scan: 44, mss = 37.084266
> Iter: 1, scan: 45, mss = 34.271937
> Iter: 1, scan: 46, mss = 36.012595
> Iter: 1, scan: 47, mss = 33.599120
> Iter: 1, scan: 48, mss = 33.769245
> Iter: 1, scan: 49, mss = 34.957680
> Iter: 1, scan: 50, mss = 35.046167
> Iter: 1, scan: 51, mss = 40.350632
> Iter: 1, scan: 52, mss = 36.718081
> Iter: 1, scan: 53, mss = 39.338981
> Iter: 1, scan: 54, mss = 35.158474
> Iter: 1, scan: 55, mss = 34.165150
> Iter: 1, scan: 56, mss = 37.189257
> Iter: 1, scan: 57, mss = 35.017210
> Iter: 1, scan: 58, mss = 35.963114
> Iter: 1, scan: 59, mss = 35.765496
> Iter: 1, Total mss = 35.8686
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Evaluating prediction maker model Calculating parameter updates
> Iter: 2, scan: 0, mss = 35.989581
> Iter: 2, scan: 1, mss = 33.585587
> Iter: 2, scan: 2, mss = 37.403102
> Iter: 2, scan: 3, mss = 34.900559
> Iter: 2, scan: 4, mss = 34.488923
> Iter: 2, scan: 5, mss = 34.020458
> Iter: 2, scan: 6, mss = 34.926970
> Iter: 2, scan: 7, mss = 33.033694
> Iter: 2, scan: 8, mss = 34.910951
> Iter: 2, scan: 9, mss = 33.281233
> Iter: 2, scan: 10, mss = 32.003373
> Iter: 2, scan: 11, mss = 34.334603
> Iter: 2, scan: 12, mss = 34.486578
> Iter: 2, scan: 13, mss = 32.484611
> Iter: 2, scan: 14, mss = 33.608417
> Iter: 2, scan: 15, mss = 32.667964
> Iter: 2, scan: 16, mss = 33.656526
> Iter: 2, scan: 17, mss = 32.980965
> Iter: 2, scan: 18, mss = 31.119783
> Iter: 2, scan: 19, mss = 32.923777
> Iter: 2, scan: 20, mss = 33.991902
> Iter: 2, scan: 21, mss = 33.967556
> Iter: 2, scan: 22, mss = 34.832843
> Iter: 2, scan: 23, mss = 33.489906
> Iter: 2, scan: 24, mss = 36.572597
> Iter: 2, scan: 25, mss = 33.198586
> Iter: 2, scan: 26, mss = 39.459344
> Iter: 2, scan: 27, mss = 34.303393
> Iter: 2, scan: 28, mss = 32.926106
> Iter: 2, scan: 29, mss = 35.754834
> Iter: 2, scan: 30, mss = 35.814969
> Iter: 2, scan: 31, mss = 34.070859
> Iter: 2, scan: 32, mss = 34.196926
> Iter: 2, scan: 33, mss = 34.624596
> Iter: 2, scan: 34, mss = 34.001025
> Iter: 2, scan: 35, mss = 33.803683
> Iter: 2, scan: 36, mss = 35.739198
> Iter: 2, scan: 37, mss = 33.260152
> Iter: 2, scan: 38, mss = 35.068390
> Iter: 2, scan: 39, mss = 34.122499
> Iter: 2, scan: 40, mss = 32.753206
> Iter: 2, scan: 41, mss = 33.476936
> Iter: 2, scan: 42, mss = 32.708078
> Iter: 2, scan: 43, mss = 34.422985
> Iter: 2, scan: 44, mss = 35.343664
> Iter: 2, scan: 45, mss = 32.571967
> Iter: 2, scan: 46, mss = 34.328248
> Iter: 2, scan: 47, mss = 32.214563
> Iter: 2, scan: 48, mss = 32.157840
> Iter: 2, scan: 49, mss = 33.701375
> Iter: 2, scan: 50, mss = 33.158861
> Iter: 2, scan: 51, mss = 38.955235
> Iter: 2, scan: 52, mss = 34.440849
> Iter: 2, scan: 53, mss = 37.457993
> Iter: 2, scan: 54, mss = 34.477858
> Iter: 2, scan: 55, mss = 32.397791
> Iter: 2, scan: 56, mss = 35.007745
> Iter: 2, scan: 57, mss = 32.922232
> Iter: 2, scan: 58, mss = 35.172488
> Iter: 2, scan: 59, mss = 33.469477
> Iter: 2, Total mss = 34.1858
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Evaluating prediction maker model Calculating parameter updates
> Iter: 3, scan: 0, mss = 34.941313
> Iter: 3, scan: 1, mss = 32.828335
> Iter: 3, scan: 2, mss = 36.852567
> Iter: 3, scan: 3, mss = 34.078169
> Iter: 3, scan: 4, mss = 33.860952
> Iter: 3, scan: 5, mss = 33.558582
> Iter: 3, scan: 6, mss = 33.664327
> Iter: 3, scan: 7, mss = 32.005217
> Iter: 3, scan: 8, mss = 33.690184
> Iter: 3, scan: 9, mss = 32.376674
> Iter: 3, scan: 10, mss = 31.197114
> Iter: 3, scan: 11, mss = 33.161434
> Iter: 3, scan: 12, mss = 33.822331
> Iter: 3, scan: 13, mss = 31.847158
> Iter: 3, scan: 14, mss = 32.801947
> Iter: 3, scan: 15, mss = 31.615392
> Iter: 3, scan: 16, mss = 33.153071
> Iter: 3, scan: 17, mss = 32.474946
> Iter: 3, scan: 18, mss = 30.501585
> Iter: 3, scan: 19, mss = 32.391819
> Iter: 3, scan: 20, mss = 32.935940
> Iter: 3, scan: 21, mss = 33.331754
> Iter: 3, scan: 22, mss = 33.816370
> Iter: 3, scan: 23, mss = 32.573579
> Iter: 3, scan: 24, mss = 36.121424
> Iter: 3, scan: 25, mss = 32.358539
> Iter: 3, scan: 26, mss = 38.864719
> Iter: 3, scan: 27, mss = 33.328583
> Iter: 3, scan: 28, mss = 32.694794
> Iter: 3, scan: 29, mss = 34.686873
> Iter: 3, scan: 30, mss = 34.741905
> Iter: 3, scan: 31, mss = 33.111107
> Iter: 3, scan: 32, mss = 33.330318
> Iter: 3, scan: 33, mss = 33.778417
> Iter: 3, scan: 34, mss = 33.559733
> Iter: 3, scan: 35, mss = 33.117393
> Iter: 3, scan: 36, mss = 34.235660
> Iter: 3, scan: 37, mss = 32.456254
> Iter: 3, scan: 38, mss = 33.381679
> Iter: 3, scan: 39, mss = 33.623393
> Iter: 3, scan: 40, mss = 32.065556
> Iter: 3, scan: 41, mss = 32.911309
> Iter: 3, scan: 42, mss = 31.731361
> Iter: 3, scan: 43, mss = 33.530664
> Iter: 3, scan: 44, mss = 34.617938
> Iter: 3, scan: 45, mss = 31.759795
> Iter: 3, scan: 46, mss = 34.064684
> Iter: 3, scan: 47, mss = 31.347626
> Iter: 3, scan: 48, mss = 31.250905
> Iter: 3, scan: 49, mss = 33.068829
> Iter: 3, scan: 50, mss = 32.373147
> Iter: 3, scan: 51, mss = 38.569354
> Iter: 3, scan: 52, mss = 33.344743
> Iter: 3, scan: 53, mss = 36.770702
> Iter: 3, scan: 54, mss = 33.423660
> Iter: 3, scan: 55, mss = 31.720154
> Iter: 3, scan: 56, mss = 34.275210
> Iter: 3, scan: 57, mss = 32.000051
> Iter: 3, scan: 58, mss = 34.786428
> Iter: 3, scan: 59, mss = 32.524588
> Iter: 3, Total mss = 33.3835
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Evaluating prediction maker model Calculating parameter updates
> Iter: 4, scan: 0, mss = 34.487865
> Iter: 4, scan: 1, mss = 32.476642
> Iter: 4, scan: 2, mss = 36.530186
> Iter: 4, scan: 3, mss = 33.645072
> Iter: 4, scan: 4, mss = 33.620845
> Iter: 4, scan: 5, mss = 33.297989
> Iter: 4, scan: 6, mss = 33.152349
> Iter: 4, scan: 7, mss = 31.582378
> Iter: 4, scan: 8, mss = 33.243559
> Iter: 4, scan: 9, mss = 32.140430
> Iter: 4, scan: 10, mss = 31.051472
> Iter: 4, scan: 11, mss = 32.800078
> Iter: 4, scan: 12, mss = 33.625489
> Iter: 4, scan: 13, mss = 31.535548
> Iter: 4, scan: 14, mss = 32.624375
> Iter: 4, scan: 15, mss = 31.231040
> Iter: 4, scan: 16, mss = 32.978648
> Iter: 4, scan: 17, mss = 32.254608
> Iter: 4, scan: 18, mss = 30.270354
> Iter: 4, scan: 19, mss = 32.148885
> Iter: 4, scan: 20, mss = 32.474388
> Iter: 4, scan: 21, mss = 32.795921
> Iter: 4, scan: 22, mss = 33.367095
> Iter: 4, scan: 23, mss = 32.315517
> Iter: 4, scan: 24, mss = 35.913834
> Iter: 4, scan: 25, mss = 31.813271
> Iter: 4, scan: 26, mss = 38.566299
> Iter: 4, scan: 27, mss = 32.966188
> Iter: 4, scan: 28, mss = 32.434736
> Iter: 4, scan: 29, mss = 34.228967
> Iter: 4, scan: 30, mss = 34.325641
> Iter: 4, scan: 31, mss = 32.834110
> Iter: 4, scan: 32, mss = 33.073514
> Iter: 4, scan: 33, mss = 33.488984
> Iter: 4, scan: 34, mss = 33.391745
> Iter: 4, scan: 35, mss = 32.845922
> Iter: 4, scan: 36, mss = 33.900971
> Iter: 4, scan: 37, mss = 31.990703
> Iter: 4, scan: 38, mss = 32.938861
> Iter: 4, scan: 39, mss = 32.403519
> Iter: 4, scan: 40, mss = 31.649166
> Iter: 4, scan: 41, mss = 32.498660
> Iter: 4, scan: 42, mss = 31.520858
> Iter: 4, scan: 43, mss = 33.198957
> Iter: 4, scan: 44, mss = 34.150963
> Iter: 4, scan: 45, mss = 31.216084
> Iter: 4, scan: 46, mss = 33.951287
> Iter: 4, scan: 47, mss = 31.084199
> Iter: 4, scan: 48, mss = 31.007358
> Iter: 4, scan: 49, mss = 32.708674
> Iter: 4, scan: 50, mss = 31.717554
> Iter: 4, scan: 51, mss = 38.190089
> Iter: 4, scan: 52, mss = 32.911936
> Iter: 4, scan: 53, mss = 36.375839
> Iter: 4, scan: 54, mss = 33.065918
> Iter: 4, scan: 55, mss = 31.567538
> Iter: 4, scan: 56, mss = 34.069405
> Iter: 4, scan: 57, mss = 31.851997
> Iter: 4, scan: 58, mss = 34.625169
> Iter: 4, scan: 59, mss = 31.899565
> Iter: 4, Total mss = 33.0338
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9
> Evaluating prediction maker model
> Calculating parameter updates
> Iter: 0, scan: 0, mss = 2118.164487
> Iter: 0, scan: 1, mss = 2126.219427
> Iter: 0, scan: 2, mss = 2148.123637
> Iter: 0, scan: 3, mss = 2128.805733
> Iter: 0, scan: 4, mss = 2109.882012
> Iter: 0, scan: 5, mss = 2114.532849
> Iter: 0, scan: 6, mss = 2164.137190
> Iter: 0, scan: 7, mss = 2145.795010
> Iter: 0, scan: 8, mss = 2149.869007
> Iter: 0, scan: 9, mss = 2137.387812
> Iter: 0, Total mss = 2134.29
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9
> Evaluating prediction maker model
> Calculating parameter updates
> Iter: 1, scan: 0, mss = 2129.423610
> Iter: 1, scan: 1, mss = 2129.245860
> Iter: 1, scan: 2, mss = 2144.132916
> Iter: 1, scan: 3, mss = 2152.897936
> Iter: 1, scan: 4, mss = 2143.397761
> Iter: 1, scan: 5, mss = 2106.787265
> Iter: 1, scan: 6, mss = 2113.834974
> Iter: 1, scan: 7, mss = 2125.569954
> Iter: 1, scan: 8, mss = 2135.372776
> Iter: 1, scan: 9, mss = 2114.543093
> Iter: 1, Total mss = 2129.52
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9
> Evaluating prediction maker model
> Calculating parameter updates
> Iter: 2, scan: 0, mss = 2094.420939
> Iter: 2, scan: 1, mss = 2090.043242
> Iter: 2, scan: 2, mss = 2109.513830
> Iter: 2, scan: 3, mss = 2112.782209
> Iter: 2, scan: 4, mss = 2127.524519
> Iter: 2, scan: 5, mss = 2074.817170
> Iter: 2, scan: 6, mss = 2078.645175
> Iter: 2, scan: 7, mss = 2090.750206
> Iter: 2, scan: 8, mss = 2102.441216
> Iter: 2, scan: 9, mss = 2084.687680
> Iter: 2, Total mss = 2096.56
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9
> Evaluating prediction maker model
> Calculating parameter updates
> Iter: 3, scan: 0, mss = 2087.456671
> Iter: 3, scan: 1, mss = 2061.515050
> Iter: 3, scan: 2, mss = 2086.210116
> Iter: 3, scan: 3, mss = 2085.416759
> Iter: 3, scan: 4, mss = 2092.906151
> Iter: 3, scan: 5, mss = 2052.652639
> Iter: 3, scan: 6, mss = 2055.971578
> Iter: 3, scan: 7, mss = 2068.060079
> Iter: 3, scan: 8, mss = 2082.187276
> Iter: 3, scan: 9, mss = 2063.668842
> Iter: 3, Total mss = 2073.6
> Loading prediction maker
> Scan: 0 1 2 3 4 5 6 7 8 9
> Evaluating prediction maker model
> Calculating parameter updates
> Iter: 4, scan: 0, mss = 2052.968427
> Iter: 4, scan: 1, mss = 2043.845896
> Iter: 4, scan: 2, mss = 2074.318891
> Iter: 4, scan: 3, mss = 2067.121755
> Iter: 4, scan: 4, mss = 2063.313345
> Iter: 4, scan: 5, mss = 2039.728846
> Iter: 4, scan: 6, mss = 2042.870112
> Iter: 4, scan: 7, mss = 2055.784871
> Iter: 4, scan: 8, mss = 2070.493359
> Iter: 4, scan: 9, mss = 2051.012493
> Iter: 4, Total mss = 2056.15
> terminate called after throwing an instance of 'NEWMAT::IndexException'
> Abort
>
> ________________________________________
> From: Marenco, Stefano (NIH/NIMH) [E]
> Sent: Thursday, February 14, 2013 11:29 AM
> To: Sarlls, Joelle (NIH/NINDS) [E]
> Subject: FW: [FSL] eddy crash: NEWMAT::IndexException
>
> Please see below....
>
> Stefano Marenco, MD
> NIMH/CBDB
> 10 Center Drive, Bldg 10 room 3C103
> Bethesda MD 20892
> Tel 301 435-8964
> Fax 301 480-7795
> Email: [log in to unmask]
>
>
> -----Original Message-----
> From: Jesper Andersson [mailto:[log in to unmask]]
> Sent: Thursday, February 14, 2013 11:22 AM
> To: [log in to unmask]
> Subject: Re: [FSL] eddy crash: NEWMAT::IndexException
>
> Dear Stefano,
>
>> I believe so. This was the first question I asked myself when I got the error message. To verify that I was using the most recent version of FSL, I deleted my old FSL, downloaded the most recent version, and performed a full install of the software. I then went to the website and downloaded/installed the most recent patch - fsl-centos5_64-patch-5.0.2.1_from_5.0.1.tar.gz. I verified that when I call FSL, this is the directory that's used with the "which fsl" command. The license files, etc, say I have FSL 4.1. Do you know what else I can do to check that I'm using the correct software?
>
> it sounds like you have done what you can to ensure you have the latest version. Can I please ask you to re-run it with the --very_verbose switch so we can get a better idea when in the process the crash occurs?
>
> Jesper
>
>>
>>
>> Stefano Marenco, MD
>> NIMH/CBDB
>> 10 Center Drive, Bldg 10 room 3C103
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>>
>>
>> -----Original Message-----
>> From: Jesper Andersson [mailto:[log in to unmask]]
>> Sent: Wednesday, February 13, 2013 3:40 AM
>> To: [log in to unmask]
>> Subject: Re: [FSL] eddy crash: NEWMAT::IndexException
>>
>> Hi Stefano,
>>
>>> I tried running eddy in the latest FSL version with this command line:
>>>
>>> eddy --imain=allRawData.nii.gz --mask=nodif_brain_mask.nii
>>> --acqp=my_acq_param_allData.txt --index=allData_index.txt
>>> --bvecs=bvecs_allData --bvals=bvals_allData --topup=my_topup_results
>>> --out=eddy_wTopup_out
>>>
>>> and got this error message.
>>>
>>> terminate called after throwing an instance of 'NEWMAT::IndexException'
>>> Abort
>>>
>>> I was able to use all these same files to run eddy and then applytopup consecutively. So, I think the files are fine.
>>
>> can you please let me know which version you are running? This sounds like a problem that we had for the first release of eddy, but which has since been fixed.
>>
>> Jesper
>>
>
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