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
> 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: 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
>