[Camino-users] modelfit output .Bfloat has no 16 value per voxel
Alexander, Daniel
d.alexander at ucl.ac.uk
Mon Jun 30 06:15:29 PDT 2014
Hi,
Without looking through this in detail, I would expect the output to contain 16x10 values - 16 for each of the 10 voxels. The fact that you get 16x20 values with extreme values suggests a datatype problem: maybe somewhere you have a file containing doubles rather than floats.
Have a look through the chunkstats man page and you'll probably find where the error lies.
Danny
On 17 Jun 2014, at 12:18, stefania oliviero <stefania.oliviero at hotmail.it<mailto:stefania.oliviero at hotmail.it>> wrote:
Hi all,
first thanks to all the camino moderators for their precious suggestions.
I am trying to make a substrate of one voxel and fit the relative signal with mmwmd model.
I am using the follow script:
SCHEMEFILE=SF90.scheme
WALKERS=160000;
TIMESTEPS=5000;
SNR=20;
DIFF=6.0E-10;
OUTPUTDIR=AbSim
mkdir ${OUTPUTDIR}
FLOAT_rnd=$RANDOM
INT_rnd=${FLOAT_rnd%.*}
LATSIZE=2.92E-5
NUMCYL=20
GAMA=5.3316
GAMB=2.0484E-7
FNAME=AbD1a20.Bfloat
FNAMEINFO=AbD1a20
let "SIGMA=$WALKERS/$SNR"
datasynth -walkers ${WALKERS} -tmax ${TIMESTEPS} -geometry inflammation -numcylinders ${NUMCYL} -p 0.0 -initial uniform -seed ${INT_rnd} -voxels 1 \
-increments 1 -separateruns -latticesize ${LATSIZE} -schemefile ${SCHEMEFILE} -gamma ${GAMA} ${GAMB} -diffusivity ${DIFF} \
-substrateinfo -drawcrosssection ${FNAMEINFO}.gray > ${OUTPUTDIR}/${FNAME} 2> ${FNAMEINFO}.txt
for ((i=1; i<=10; i=i+1)); do cat ${OUTPUTDIR}/${FNAME}; done | addnoise -sigma ${SIGMA} > ${FNAMEINFO}_NOISE.Bfloat
cat ${FNAMEINFO}_NOISE.Bfloat | modelfit -fitmodel mmwmdfixed -fitalgorithm mcmc -mmwmddiff 6E-10 -burnin 1000 -samples 100 \
-interval 500 -noisemodel rician -sigma ${SIGMA} -schemefile ${SCHEMEFILE} | chunkstats -chunksize 16 -samples 100 \
-mean > ${FNAMEINFO}_NOISE_MMWMD.Bfloat
The problem is that file ${FNAMEINFO}_NOISE_MMWMD.Bfloat do not contain only 16 values as I expect (the substrate is formed of one voxel), but 16 X 20 with also negative value:
I see them using fopen and fread.
fid = fopen('AbD1a20_NOISE_MMWMD.Bfloat', 'r', 'b');
res = fread(fid, 'float')
How can I solve the problem? How can I interpret all the values?
Thanks again
Cheers
Stefania Oliviero
ps. the list of the 16X20 values
0
0
S0 8.224530220031738
-1.863699110444668e-21
f1 1.858711004257202
9.190513773973481e+25
f2 0.926609992980957
7.015029707773839e+21
f3 1.369219303131104
1.693542986709899e-30
f4 0.7559823989868164
-3.17694181388827e-30
d 0.1295076459646225
-4.165380421254879e+21
2.172163724899292
-9.884467027226646e-22
2.810829639434814
8.930397508547379e+16
0.4786647856235504
1.310057078073177e+36
0.1295076459646225
-4.165380421254879e+21
2.172163724899292
-9.884467027226646e-22
2.810829639434814
8.930397508547379e+16
0.06447996199131012
-1.633653217959846e+28
0.1574022024869919
-3.151482974496329e+24
-5.199854850769043
-1.163995524844674e+29
0
0
8.17563533782959
-4.75105565851089e-15
1.841294884681702
5.775239769918727e-13
1.488044142723083
-3.395917218218452e-24
1.047731041908264
35.55131530761719
0.9550412893295288
1.061308366193202e+21
0.1295076459646225
-4.165380421254879e+21
2.174391984939575
3.561243531291051e-25
-2.534331798553467
-9.564227348742295e-31
0.4786647856235504
1.319587750654668e+36
0.1295076459646225
-4.165380421254879e+21
2.174391984939575
3.561243531291051e-25
-2.534331798553467
-9.564227348742295e-31
0.1034887805581093
5.771853661684645e-09
0.1574022024869919
-3.151482974496329e+24
-5.175981044769287
-1.017516352809881e+31
0
0
8.219924926757812
9.26491752822447e+28
1.855491638183594
-4.515597338480398e-29
1.364690542221069
-5.770785558985449e+27
1.033074736595154
-1.394390768037511e+26
1.006693363189697
4309.93798828125
0.1295076459646225
-4.165380421254879e+21
-4.958737850189209
6758401536
3.998507738113403
6.259943643533397e-18
0.4765672087669373
3.773402528519296e-34
0.1295076459646225
-4.165380421254879e+21
-4.958737850189209
6758401536
3.998507738113403
6.259943643533397e-18
0.09576673060655594
5965048832
0.1574022024869919
-3.151482974496329e+24
-5.17671012878418
2.23596379118343e+27
0
0
8.234395980834961
1.016925203195662e+30
1.843050599098206
0.0001048958147293888
0.967486560344696
-6.782044408492824e-14
1.389882206916809
-3.594784016218256e+27
1.333303689956665
4.160535260636049e-18
0.1295076459646225
-4.165380421254879e+21
2.040200233459473
-2.493551332435465e-28
-2.421376705169678
-5.195420690853957e+26
0.4786647856235504
1.159980398881132e+36
0.1295076459646225
-4.165380421254879e+21
2.040200233459473
-2.493551332435465e-28
-2.421376705169678
-5.195420690853957e+26
0.07098308205604553
-1.573314289671638e-29
0.1574022024869919
-3.151482974496329e+24
-5.185783863067627
0.7966716885566711
0
0
8.235532760620117
-8.098136313376767e-16
1.847925424575806
3.091666234071697e-15
1.068425297737122
NaN
1.369619965553284
-1.664226908913367e-21
1.265216469764709
3.220426972578015e+27
0.1295076459646225
-4.165380421254879e+21
2.054977416992188
-3.666441705597253e+24
-2.232414484024048
-2.637206364558206e+36
0.4786647856235504
1.319557960865562e+36
0.1295076459646225
-4.165380421254879e+21
2.054977416992188
-3.666441705597253e+24
-2.232414484024048
-2.637206364558206e+36
0.07892990857362747
1.344784331364716e-18
0.1574022024869919
-3.151482974496329e+24
-5.193636894226074
4.350179035559406e-34
0
0
8.215572357177734
1.485588127265567e+25
1.846333742141724
8.714338832760404e-07
1.075241208076477
-2.976498574343404e+22
1.401218175888062
1.285109448242627e+17
1.212325811386108
-5.062357224591691e+29
0.1295076459646225
-4.165380421254879e+21
2.243474721908569
1.949174898671407e+36
-2.302396297454834
1.900524481719367e-24
0.474882572889328
1.963849098186959e+31
0.1295076459646225
-4.165380421254879e+21
2.243474721908569
1.949174898671407e+36
-2.302396297454834
1.900524481719367e-24
0.07929259538650513
4.45744695420087e-32
0.1574022024869919
-3.151482974496329e+24
-5.188226222991943
7.813559375285837e-21
0
0
8.190117835998535
1.20175033323514e-25
1.845718026161194
9.202333741453702e-37
1.145087122917175
4.004461796559765e-23
1.34853994846344
0.006131891626864672
1.297427892684937
-1.704187254181844e+37
0.1295076459646225
-4.165380421254879e+21
2.21069073677063
3.700585306736271e-19
-1.788000345230103
-1.796271638376812e-11
0.4717114567756653
-226895.890625
0.1295076459646225
-4.165380421254879e+21
2.21069073677063
3.700585306736271e-19
-1.788000345230103
-1.796271638376812e-11
0.08328014612197876
-5.104643430855692e+32
0.1574022024869919
-3.151482974496329e+24
-5.156137466430664
1.73671554887888e+36
0
0
8.220879554748535
7616.06298828125
1.706142067909241
-1.051235178980411e+22
1.717968940734863
4.473759607288441e-18
1.404045701026917
377.2929992675781
1.39950966835022
-8.309964484175231e-19
0.1295076459646225
-4.165380421254879e+21
2.019524574279785
-1.776001764192488e+34
1.441986560821533
-1.319797957257027e+24
0.4786647856235504
1.319516445308405e+36
0.1295076459646225
-4.165380421254879e+21
2.019524574279785
-1.776001764192488e+34
1.441986560821533
-1.319797957257027e+24
0.1197220385074615
1.642972189503863e-13
0.1574022024869919
-3.151482974496329e+24
-5.174685478210449
-2.278755491750942e+38
0
0
8.245189666748047
278710.40625
1.782524943351746
5.795323332336011e-09
1.595520973205566
-1.038947912619138e-14
1.322386980056763
-1.005076333284924e+19
1.450064539909363
-5.906108935249677e-09
0.1295076459646225
-4.165380421254879e+21
2.062609434127808
-3.539777639983856e-25
-2.417265176773071
0.01927732862532139
0.4786647856235504
1.306203499476646e+36
0.1295076459646225
-4.165380421254879e+21
2.062609434127808
-3.539777639983856e-25
-2.417265176773071
0.01927732862532139
0.1120011657476425
5.331455710003461e+32
0.1574022024869919
-3.151482974496329e+24
-5.179274559020996
-6.79911182910331e-10
0
0
8.243127822875977
-5.033884947667555e-29
1.78268301486969
-1.226406241071041e+36
1.662764310836792
-2.75854094571034e-38
1.27248477935791
-5219.408203125
0.9848801493644714
-213086279565312 #10^10
0.1295076459646225
-4.165380421254879e+21
2.034368515014648
-1.700320171723325e+17
1.544929027557373
9.088711549266166e+18
0.4786647856235504
1.310674265459163e+36
0.1295076459646225
-4.165380421254879e+21
2.034368515014648
-1.700320171723325e+17
1.544929027557373
9.088711549266166e+18
0.1152695417404175
-0.2617772817611694
0.1574022024869919
-3.151482974496329e+24
-5.211963176727295
-1.513784627604764e-05
fid = fopen('AbD1a80_NOISE_MMWMD.Bfloat', 'r', 'b'); #apre file bfloat
res = fread(fid, 'float'); # lo legge nella var res come float
fid = fopen('AbD1a80_NOISE_MMWMD.Bfloat', 'r', 'b'); #apre file bfloat
res = fread(fid, 'float'); # lo legge nella var res come float
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