[Eeglablist] AMICA Error on Mac
Kelly Michaelis
kcmichaelis at gmail.com
Wed Sep 12 08:54:24 PDT 2018
Sure thing! Here you go:
pop_loadset(): loading file
/Users/kcm73/Documents/WordsInNoiseProject/EEGData/CohenPipeline/NewPreproc/S13newpreproc/preICAfiles/S1302postASRpreICA.set
...
Reading float file
'/Users/kcm73/Documents/WordsInNoiseProject/EEGData/CohenPipeline/NewPreproc/S13newpreproc/preICAfiles/S1302postASRpreICA.fdt'...
Writing data file:
/Users/kcm73/Documents/WordsInNoiseProject/EEGData/tmpdata15761.fdt
1 processor name = NEUR-AD-UFDNMP.local
1 host_num = 258801812
This is MPI process 1 of 1 ; I am process 1
of
1 on node: NEUR-AD-UFDNMP.local
1 : node root process 1 of 1
Processing arguments ...
num_files = 1
FILES:
/Users/kcm73/Documents/WordsInNoiseProject/EEGData/tmpdata15761.fdt
num_dir_files = 1
initial matrix block_size = 128
do_opt_block = 0
blk_min = 256
blk_step = 256
blk_max = 1024
number of models = 1
max_thrds = 2
use_min_dll = 1
min dll = 1.000000000000000E-009
use_grad_norm = 1
min grad norm = 1.000000000000000E-007
number of density mixture components = 3
pdf type = 0
max_iter = 2000
num_samples = 1
data_dim = 128
field_dim = 471339
do_history = 0
histstep = 10
share_comps = 0
share_start = 100
comp_thresh = 0.990000000000000
share_int = 100
initial lrate = 5.000000000000000E-002
minimum lrate = 1.000000000000000E-008
minimum data covariance eigenvalue = 1.000000000000000E-012
lrate factor = 0.500000000000000
initial rholrate = 5.000000000000000E-002
rho0 = 1.50000000000000
min rho = 1.00000000000000
max rho = 2.00000000000000
rho lrate factor = 0.500000000000000
kurt_start = 3
num kurt = 5
kurt interval = 1
do_newton = 1
newt_start = 50
newt_ramp = 10
initial newton lrate = 1.00000000000000
do_reject = 1
num reject = 15
reject sigma = 3.00000000000000
reject start = 2
reject interval = 1
write step = 20
write_nd = 0
write_LLt = 1
dec window = 1
max_decs = 3
fix_init = 0
update_A = 1
update_c = 1
update_gm = 1
update_alpha = 1
update_mu = 1
update_beta = 1
invsigmax = 100.000000000000
invsigmin = 0.000000000000000E+000
do_rho = 1
load_rej = 0
load_c = 0
load_gm = 0
load_alpha = 0
load_mu = 0
load_beta = 0
load_rho = 0
load_comp_list = 0
do_mean = 1
do_sphere = 1
pcakeep = 128
pcadb = 30.0000000000000
byte_size = 4
doscaling = 1
scalestep = 1
mkdir:
/Users/kcm73/Documents/WordsInNoiseProject/EEGData/CohenPipeline/NewPreproc/S13newpreproc/postICAfiles/S1302amicaout/:
File exists
output directory =
/Users/kcm73/Documents/WordsInNoiseProject/EEGData/CohenPipeline/NewPreproc/S13
newpreproc/postICAfiles/S1302amicaout/
1 : setting num_thrds to 2 ...
1 : using 2 threads.
1 : node_thrds = 2
bytes in real = 1
1 : REAL nbyte = 1
getting segment list ...
blocks in sample = 471339
total blocks = 471339
node blocks = 471339
node 1 start: file 1 sample 1 index
1
node 1 stop : file 1 sample 1 index
471339
1 : data = 25.3650321960449 10.1630601882935
getting the mean ...
mean = -9.756913085498017E-002 -0.956047629660906
-1.47526619473147
subtracting the mean ...
getting the covariance matrix ...
cnt = 471339
doing eig nx = 128 lwork = 163840
minimum eigenvalues = 1.149162121975516E-012 1.895431946218596E-012
4.236845509809919E-012
maximum eigenvalues = 9419.65839076655 2570.08584450239
1758.66520615951
num eigs kept = 128
getting the sphering matrix ...
minimum eigenvalues = 1.149162121975516E-012 1.895431946218596E-012
4.236845509809919E-012
maximum eigenvalues = 9419.65839076655 2570.08584450239
1758.66520615951
num eigs kept = 128
sphering the data ...
numeigs = 128
1 : Allocating variables ...
1 : Initializing variables ...
1 : block size = 128
1 : entering the main loop ...
iter 1 lrate = 0.0500000000 LL = -1.6698055110 nd = 0.0340860203, D
= 0.67253E-01 0.67253E-01 ( 4.88 s, 2.7 h)
Doing rejection ....
maximum likelihood value = -1.36397350038783
minimum likelihood value = -14.1307674390157
average likelihood value = -1.66980551096439
standard deviation = 0.420485964745811
rejecting data with likelihood less than -2.93126340520182
rejected 9503 data points so far. Will perform rejection
14
more times at intervals of 1 iterations.
iter 2 lrate = 0.0500000000 LL = -1.4654950056 nd = 0.0288690764, D
= 0.59194E-01 0.59194E-01 ( 4.79 s, 2.7 h)
Doing rejection ....
maximum likelihood value = -0.977157440409710
minimum likelihood value = -2.96524772054921
average likelihood value = -1.46549500564456
standard deviation = 0.303163728040875
rejecting data with likelihood less than -2.37498618976719
rejected 18918 data points so far. Will perform rejection
13
more times at intervals of 1 iterations.
iter 3 lrate = 0.0500000000 LL = -1.3926954878 nd = 0.0275011071, D
= 0.58424E-01 0.58424E-01 ( 4.70 s, 2.6 h)
Doing rejection ....
maximum likelihood value = -0.777762136563562
minimum likelihood value = -2.55766743708507
average likelihood value = -1.39269548775611
standard deviation = 0.319846000043196
rejecting data with likelihood less than -2.35223348788570
rejected 24174 data points so far. Will perform rejection
12
more times at intervals of 1 iterations.
iter 4 lrate = 0.0500000000 LL = -1.3632471401 nd = 0.0290695735, D
= 0.94839E-01 0.94839E-01 ( 4.66 s, 2.6 h)
Doing rejection ....
maximum likelihood value = -0.686256773219760
minimum likelihood value = -2.43842460366318
average likelihood value = -1.36324714013545
standard deviation = 0.330772484995092
rejecting data with likelihood less than -2.35556459512072
rejected 26716 data points so far. Will perform rejection
11
more times at intervals of 1 iterations.
iter 5 lrate = 0.0500000000 LL = -1.3482726930 nd = 0.0288899726, D
= 0.18201E+00 0.18201E+00 ( 4.61 s, 2.6 h)
Doing rejection ....
maximum likelihood value = -0.646418570096526
minimum likelihood value = -2.39934446681889
average likelihood value = -1.34827269296910
standard deviation = 0.336117397856749
rejecting data with likelihood less than -2.35662488653935
rejected 27789 data points so far. Will perform rejection
10
more times at intervals of 1 iterations.
iter 6 lrate = 0.0500000000 LL = -1.3390648637 nd = 0.0275633419, D
= 0.31228E+00 0.31228E+00 ( 4.61 s, 2.6 h)
Doing rejection ....
maximum likelihood value = -0.627721013095301
minimum likelihood value = -2.38360832281495
average likelihood value = -1.33906486365062
standard deviation = 0.339889548496796
rejecting data with likelihood less than -2.35873350914101
rejected 28210 data points so far. Will perform rejection
9
more times at intervals of 1 iterations.
/Users/kcm73/Documents/WordsInNoiseProject/EEGData/eeglab14_1_2b/plugins/AMICA1.5/amica15mac
/Users/kcm73/Documents/WordsInNoiseProject/EEGData/CohenPipeline/NewPreproc/S13newpreproc/postICAfiles/S1302amicaout/input.param:
Illegal instruction
No gm present, setting num_models to 1
No W present, exiting
Reference to non-existent field 'W'.
Error in runamica15 (line 873)
weights = mods.W(:,:,1);
Error in runAMICAonfiles (line 62)
runamica15(EEG.data, 'num_chans', EEG.nbchan,...
On Wed, Sep 12, 2018 at 11:50 AM, <japalmer29 at gmail.com> wrote:
> Hi Kelly,
>
> Thanks, but could you past the output from the command line (including the
> MPI, etc. output, and up to the point where it fails)?
>
> … It would be helpful to see the output of all the parameter values used
> and relative the minimum covariance eigenvalues, and where it fails exactly
> ….
>
> Thanks,
>
> Jason
>
>
>
> *From:* Kelly Michaelis <kcmichaelis at gmail.com>
> *Sent:* Thursday, September 13, 2018 12:07 AM
> *To:* japalmer29 at gmail.com
> *Cc:* EEGLAB List <eeglablist at sccn.ucsd.edu>
> *Subject:* Re: [Eeglablist] AMICA Error on Mac
>
>
>
> Hi Jason,
>
>
>
> The text output from a failed run is attached. With this particular file,
> I ran the same script again and now it appears to be working. The issue is
> I can't predict when Amica will fail, and I need to run lots of files
> through it. Let me know if you have any suggestions.
>
>
>
> Thanks,
>
> Kelly
>
>
>
>
>
>
>
> On Wed, Sep 12, 2018 at 10:56 AM, <japalmer29 at gmail.com> wrote:
>
> Hi Kelly,
>
> Could you provide the text output of Amica on the run(s) where it fails?
>
> Thanks,
>
> Jason
>
>
>
> *From:* eeglablist <eeglablist-bounces at sccn.ucsd.edu> *On Behalf Of *Kelly
> Michaelis
> *Sent:* Tuesday, September 11, 2018 6:49 AM
> *To:* EEGLAB List <eeglablist at sccn.ucsd.edu>
> *Subject:* [Eeglablist] AMICA Error on Mac
>
>
>
> Hi everyone,
>
>
>
> I have been running into this error with AMICA on mac Sierra 10.12.6 using
> EEGLAB version 14_1_2b. Most of time it works just fine, but then on some
> files it fails and I can't figure out why. The files on which it fails
> don't seem to be different in any observable way. I installed AMICA through
> the GUI, and I have tried uninstalling and reinstalling to no avail.
>
>
>
> I'm hoping one of you can help.
>
>
>
> My script was adapted from Makato's script
> <https://sccn.ucsd.edu/wiki/Makoto's_useful_EEGLAB_code>and is as follows
> (no spaces in path):
>
>
>
>
>
> for iFile = iFiles
>
>
>
> disp(['starting subject' currentSub 'file' num2str(iFile)])
>
>
>
> curoutfname = ['S' currentSub '0' num2str(iFile)
> 'postASRpostICA.set'];
>
>
>
> cutfulloutputfname = fullfile(postICAdir,curoutfname); % so we
> can check if an output file exists already (so we can potentially bail on
> this iteration of a loop so we don't redo work)
>
>
>
> if exist(cutfulloutputfname,'file')
>
> disp(['File ' cutfulloutputfname ' already exists,
> bailing...'])
>
> continue % break?
>
> end
>
>
>
> %reload to clear
>
> eeglab
>
>
>
> EEG = pop_loadset('filename',['S' currentSub '0'
> num2str(iFile) 'postASRpreICA.set'],'filepath',preICAdir);
>
>
>
> % Run AMICA using calculated data rank with 'pcakeep' option
>
> if isfield(EEG.etc, 'clean_channel_mask')
>
> dataRank = min([rank(double(EEG.data'))
> sum(EEG.etc.clean_channel_mask)]);
>
> else
>
> dataRank = rank(double(EEG.data'));
>
> end
>
> amicaoutdir = fullfile(postICAdir,['S' currentSub '0'
> num2str(iFile) 'amicaout']);
>
> runamica15(EEG.data, 'num_chans', EEG.nbchan,...
>
> 'outdir', amicaoutdir, ... % [ postICAdir '/S' currentSub
> '0' num2str(iFile) 'amicaout'],...
>
> 'pcakeep', dataRank, 'num_models', 1,...
>
> 'do_reject', 1, 'numrej', 15, 'rejsig', 3, 'rejint', 1);
>
> EEG.etc.amica = loadmodout15(amicaoutdir); %
> fullfile(postICAdir,['S' currentSub '0' num2str(iFile) 'amicaout']));
>
> EEG.etc.amica.S = EEG.etc.amica.S(1:EEG.etc.amica.num_pcs,
> :); % Weirdly, I saw size(S,1) be larger than rank. This process does not
> hurt anyway.
>
> EEG.icaweights = EEG.etc.amica.W;
>
> EEG.icasphere = EEG.etc.amica.S;
>
> EEG = eeg_checkset(EEG, 'ica');
>
>
>
>
>
> EEG = pop_saveset( EEG, 'filename',curoutfname,'filepath',
> postICAdir);
>
> %EEG = pop_saveset( EEG, 'filename',['S' currentSub '0'
> num2str(iFile) 'postASRpostICA.set'],'filepath', postICAdir);
>
>
>
> disp(['done with' currentSub 'file' num2str(iFile)])
>
> end
>
>
>
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