def main():
argParser = argparse.ArgumentParser()
argParser.add_argument('--config',
'-c',
default=defaultConfig,
type=str,
help='experiment config file (.json or .toml)')
argParser.add_argument('--runs',
'-r',
default='',
type=str,
help='Comma separated list of run numbers')
argParser.add_argument('--scans',
'-s',
default='',
type=str,
help='Comma separated list of scan number')
# creates web pipe communication link to send/request responses through web pipe
argParser.add_argument('--webpipe',
'-w',
default=None,
type=str,
help='Named pipe to communicate with webServer')
argParser.add_argument('--filesremote',
'-x',
default=False,
action='store_true',
help='dicom files retrieved from remote server')
argParser.add_argument('--getReward',
'-g',
default=False,
action='store_true',
help='if you want to calculate reward earned')
argParser.add_argument(
'--makePlots',
'-p',
default=False,
action='store_true',
help='if you want to plot correct probability over time')
args = argParser.parse_args()
print(args)
cfg = greenEyes.initializeGreenEyes(args.config, args)
correct_prob = getCorrectProbability(cfg)
print('Prob for each run (col) over all stations (row)')
print(correct_prob.T)
if args.getReward:
total_reward = getReward(cfg, correct_prob)
if args.makePlots:
plotCorrectProbability(cfg, correct_prob)
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
font = {'weight': 'bold', 'size': 22}
plt.rc('font', **font)
defaultConfig = os.path.join(os.getcwd(), 'conf/greenEyes_cluster.toml')
cfg = loadConfigFile(defaultConfig)
params = StructDict({
'config': defaultConfig,
'runs': '1',
'scans': '9',
'webpipe': 'None',
'webfilesremote': False
})
cfg = greenEyes.initializeGreenEyes(defaultConfig, params)
# date doesn't have to be right, but just make sure subject number, session number, computers are correct
def getClassificationAndScore(data):
correct_prob = data['correct_prob'][0, :]
max_ratio = data['max_ratio'][0, :]
return correct_prob, max_ratio
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]
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'))