"""

global accuracy

You might want to save the log on the shell to a text file.

Lines to be modified if necessary.

sessionPath = '/home/brain/host/20120808jys-lang/'#Sesson path to be changed

chunksTargets_boldDelay="chunksTargets_boldDelay4-4.txt" #This log file is for boldDelay=4 and stimulusWidth=4

#Also check

boldDelay=4 #added

stimulusWidth=4 #added

#Change if you need the targets of classification:this script is for Japanese(j) vs English(e).

dataset = dataset[N.array([l in ['m', 't'] for l in dataset.sa.targets], dtype='bool')]

"""

import mvpa2.suite as M

import numpy as N

import pylab as P # plotting (matlab-like)

import os # file path processing, directory contents

import datetime # time stamps

import pickle # cPickle

import gzip

numFolds=6

# sessionPath = '/home/brain/host/20130908ln'

sessionPath = os.getcwd()

preprocessedCache = os.path.join(sessionPath, 'detrendedZscoredMaskedPyMVPAdataset.pkl')

trimmedCache = os.path.join(sessionPath, 'averagedDetrendedZscoredMaskedPyMVPAdataset.pkl')

# LOAD DATASET (Following the instruction of Brian, "and False" is added at lines 29 and 33.)

if os.path.isfile(trimmedCache) and False:

print 'loading cached averaged, trimmed, preprocessed dataset',trimmedCache,datetime.datetime.now()

dataset = pickle.load(gzip.open(trimmedCache, 'rb'))

else:

if os.path.isfile(preprocessedCache) and False:

print 'loading cached preprocessed dataset',preprocessedCache,datetime.datetime.now()

dataset = pickle.load(gzip.open(preprocessedCache, 'rb', 5))

else:

# if not, generate directly, and then cache

print 'loading and creating dataset',datetime.datetime.now()

# chunksTargets_boldDelay="chunksTargets_boldDelay4-4.txt" #Modified

chunksTargets_boldDelay="chunksTargets_boldDelay3-4-LanguageSwitch-Japanese_English.txt"

boldDelay=6 #added

stimulusWidth=4 #added

volAttribrutes = M.SampleAttributes(os.path.join(sessionPath,'behavioural',chunksTargets_boldDelay)) # default is 3.txt.

# print volAttribrutes.targets

# print len(volAttribrutes.targets)

# print volAttribrutes.chunks

# print len(volAttribrutes.chunks)

dataset = M.fmri_dataset(samples=os.path.join(sessionPath,'analyze/functional/functional4D.nii'),

targets=volAttribrutes.targets, # I think this was "labels" in versions 0.4.*

chunks=volAttribrutes.chunks,

mask=os.path.join(sessionPath,'analyze/structural/lc2ms_deskulled.hdr'))

# DATASET ATTRIBUTES (see AttrDataset)

print 'functional input has',dataset.a.voxel_dim,'voxels of dimesions',dataset.a.voxel_eldim,'mm'

print '... or',N.product(dataset.a.voxel_dim),'voxels per volume'

print 'masked data has',dataset.shape[1],'voxels in each of',dataset.shape[0],'volumes'

print '... which means that',round(100-100*dataset.shape[1]/N.product(dataset.a.voxel_dim)),'% of the voxels were masked out'

print 'of',dataset.shape[1],'remaining features ...'

print 'summary of conditions/volumes\n',datetime.datetime.now()

print dataset.summary_targets()

# DETREND

print 'detrending (remove slow drifts in signal, and jumps between runs) ...',datetime.datetime.now() # can be very memory intensive!

M.poly_detrend(dataset, polyord=1, chunks_attr='chunks') # linear detrend

print '... done',datetime.datetime.now()

# ZSCORE

print 'zscore normalising (give all voxels similar variance) ...',datetime.datetime.now()

M.zscore(dataset, chunks_attr='chunks', param_est=('targets', ['base'])) # zscoring, on basis of rest periods

print '... done',datetime.datetime.now()

#P.savefig(os.path.join(sessionPath,'pyMVPAimportDetrendZscore.png'))

pickleFile = gzip.open(preprocessedCache, 'wb', 5);

pickle.dump(dataset, pickleFile);

# AVERAGE OVER MULTIPLE VOLUMES IN A SINGLE TRIAL

print 'averaging over trials ...',datetime.datetime.now()

dataset = dataset.get_mapped(M.mean_group_sample(attrs=['chunks','targets']))

print '... only',dataset.shape[0],'cases left now'

dataset.chunks = N.mod(N.arange(0,dataset.shape[0]),5)

# print '\n\n\n'

# print dataset.targets

# print len(dataset.targets)

# print dataset.chunks

# print len(dataset.chunks)

# REDUCE TO CLASS LABELS, AND ONLY KEEP CONDITIONS OF INTEREST (JAPANESE VS ENGLISH)

dataset.targets = [t[0:2] for t in dataset.targets]

dataset = dataset[N.array([l in ['jj', 'je', 'ej', 'ee'] for l in dataset.sa.targets], dtype='bool')]

print '... and only',dataset.shape[0],'cases of interest (Language Switch between Japanese vs English)'

dataset=M.datasets.miscfx.remove_invariant_features(dataset)

print 'saving as compressed file',trimmedCache

pickleFile = gzip.open(trimmedCache, 'wb', 5);

pickle.dump(dataset, pickleFile);

anovaSelectedSMLR = M.FeatureSelectionClassifier(

)

foldwiseCvedAnovaSelectedSMLR = M.CrossValidation(

)

# run classifier

print 'learning on detrended, normalised, averaged, Language Switch ...',datetime.datetime.now()

results = foldwiseCvedAnovaSelectedSMLR(dataset)

print '... done',datetime.datetime.now()

print 'accuracy',N.round(100-N.mean(results)*100,1),'%',datetime.datetime.now()

#New lines for out putting the result into a csv file.

precision=N.round(100-N.mean(results)*100,1)

st=str(boldDelay) + ',' + str(stimulusWidth) + ',' + str(precision) +'\n'

f = open( "withinPredictionResult.csv", "a" )

f.write(st)

f.close

# display results

P.figure()

P.title(str(N.round(foldwiseCvedAnovaSelectedSMLR.ca.stats.stats['ACC%'], 1))+'%, n-fold SMLR with anova FS x 500')

foldwiseCvedAnovaSelectedSMLR.ca.stats.plot()

P.savefig(os.path.join(sessionPath,'confMatrixAvTrial-LanguageSwitch-Japanese_English-GNB.png'))

print foldwiseCvedAnovaSelectedSMLR.ca.stats.matrix

print 'accuracy',N.round(foldwiseCvedAnovaSelectedSMLR.ca.stats.stats['ACC%'], 1),'%',datetime.datetime.now()

# this should give average anova measure over the folds - but in fact would be much the same as taking over single fold

sensana = anovaSelectedSMLR.get_sensitivity_analyzer(postproc=M.maxofabs_sample())

cv_sensana = M.RepeatedMeasure(sensana, M.NFoldPartitioner())

sens = cv_sensana(dataset)

print sens.shape

M.map2nifti(dataset, N.mean(sens,0)).to_filename("anovaSensitivity_"+sessionID+'.nii')

# this looks good, but don't know way to get back from this feature selected space (of 500) to the whole space of 28k or so, for output

weights = anovaSelectedSMLR.clf.weights