[Camino-users] modelfit output .Bfloat has no 16 value per voxel

Thu Jul 3 00:17:27 PDT 2014

Thank you very much Danny ! The problem is solved: my files were double and not float .. 

Cheers 
Stefania

From: d.alexander at ucl.ac.uk
To: stefania.oliviero at hotmail.it
CC: camino-users at www.nitrc.org
Subject: Re: [Camino-users] modelfit output .Bfloat has no 16 value per voxel
Date: Mon, 30 Jun 2014 13:15:29 +0000

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> 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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