def getPatternsData(subjectNum,runNum):
bids_id = 'sub-{0:03d}'.format(subjectNum)
ses_id = 'ses-{0:02d}'.format(2)
filename = '/jukebox/norman/amennen/RT_prettymouth/data/intelData/{0}/{1}/patternsData_r{2}_*.mat'.format(bids_id,ses_id,runNum)
fn = glob.glob(filename)[-1]
data = loadMatFile(fn)
return data
def getBehavData(subjectNum,runNum):
bids_id = 'sub-{0:03d}'.format(subjectNum)
ses_id = 'ses-{0:02d}'.format(2)
run_id = 'run-{0:03d}'.format(runNum)
filename = '/jukebox/norman/amennen/RT_prettymouth/data/intelData/{0}/{1}/{2}/behavior_run{3}_*.mat'.format(bids_id,ses_id,run_id,runNum)
fn = glob.glob(filename)[-1]
data = loadMatFile(fn)
return data
def getCorrectProbability(cfg):
nRuns = int(cfg.totalNumberRuns)
all_correct_prob = np.zeros((nRuns, 7))
for r in np.arange(nRuns):
fileStr = '{0}/patternsData_r{1}*'.format(cfg.subject_full_day_path,
r + 1)
run_pat = glob.glob(fileStr)[-1]
run_data = loadMatFile(run_pat)
all_correct_prob[r, :] = run_data.correct_prob[0, :]
return all_correct_prob
def getPatternsData(subject_num, run_num):
bids_id = 'sub-{0:03d}'.format(subject_num)
ses_id = 'ses-{0:02d}'.format(2)
filename = '/jukebox/norman/amennen/RT_prettymouth/data/intelData/{0}/{1}/patternsData_r{2}_*.mat'.format(
bids_id, ses_id, run_num)
fn = glob.glob(filename)[-1]
data = loadMatFile(fn)
cheating_prob = data['cheating_probability']
cheating_prob_z = data['zTransferred']
correct_score = data['correct_prob']
return data, cheating_prob, cheating_prob_z, correct_score
def test_loadMatlabFile(self, testStruct, matTestFilename):
print("Test LoadMatlabFile")
struct2 = utils.loadMatFile(matTestFilename)
assert testStruct.__name__ == struct2.__name__
res = vutils.compareMatStructs(testStruct, struct2)
assert vutils.isMeanWithinThreshold(res, 0)
with open(matTestFilename, 'rb') as fp:
data = fp.read()
struct3 = utils.loadMatFileFromBuffer(data)
res = vutils.compareMatStructs(testStruct, struct3)
assert vutils.isMeanWithinThreshold(res, 0)
# now load the same information
cmap=plt.get_cmap('cool')
colors=cmap(np.linspace(0,1,nStations))
brainiak_path='/jukebox/norman/amennen/github/brainiak/rt-cloud/projects/greenEyes/data/'
# now load newestfile
sys.path.append('/jukebox/norman/amennen/github/brainiak/rt-cloud')
from rtCommon.utils import findNewestFile, loadMatFile
s = 0
subject_path = brainiak_path + 'sub-' + str(allSubjects[s]) + '/' + 'ses-02' + '/'
run = 1
filePattern = 'patternsData_r{}*'.format(run)
fn = findNewestFile(subject_path,filePattern)
test_data = loadMatFile(fn)
test_prob = test_data.correct_prob
x = correct_prob[:,run-1,s]
y = test_prob[0,:]
corr = sstats.pearsonr(x,y)[0]
plt.figure(figsize=(10,10))
for st in np.arange(nStations):
plt.plot(x[st],y[st], '.', ms=20, color=colors[st],label=st)
plt.plot([0,1],[0,1], '--', color='r', lw=3)
plt.title('Subj %i, Run %i, Total corr = %3.3f' % (allSubjects[s],run,corr))
plt.xlim([0,1])
plt.ylim([0,1])
plt.xlabel('Offline prediction')
plt.legend()
sys.path.append('/home/amennen/code/rt-cloud')
# OR FOR INTELRT
sys.path.append('/Data1/code/rt-cloud/')
from rtCommon.utils import loadConfigFile, dateStr30, DebugLevels, writeFile, loadMatFile
from rtCommon.readDicom import readDicomFromBuffer
from rtCommon.fileClient import FileInterface
import rtCommon.webClientUtils as wcutils
from rtCommon.structDict import StructDict
import rtCommon.dicomNiftiHandler as dnh
import greenEyes
subject=102
#conf='/home/amennen/code/rt-cloud/projects/greenEyes/conf/greenEyes_organized.local.toml'
conf = '/Data1/code/rt-cloud/projects/greenEyes/conf/greenEyes_organized.toml'
args = StructDict()
args.config=conf
args.runs = '1'
args.scans = '5'
args.webpipe = None
args.filesremote = False
cfg = greenEyes.initializeGreenEyes(args.config,args)
r = 0
fileStr = '{0}/patternsData_r{1}*'.format(cfg.subject_full_day_path,r+1)
run_pat = glob.glob(fileStr)[-1]
run_data = loadMatFile(run_pat)
# check classifier
modelfn = '/home/amennen/utils/greenEyes_clf/UPPERRIGHT_stationInd_0_ROI_1_AVGREMOVE_1_filter_0_k1_0_k2_25.sav'
modelfn = '/Data1/code/utils_greenEyes/greenEyes_clf/UPPERRIGHT_stationInd_0_ROI_1_AVGREMOVE_1_filter_0_k1_0_k2_25.sav''
loaded_model = pickle.load(open(modelfn, 'rb'))
def getRegressorMatrix(cfg, runNum):
run_id = 'run-{0:02d}'.format(runNum)
matrix_file = cfg.subject_full_day_path + '/' + run_id + '/' + 'Regressors_unshifted_Rm2TR.mat'
regressor_matrix = loadMatFile(matrix_file)
reg_mat = regressor_matrix['REGRESSOR_MATRIX']
return reg_mat
