op = args.operator
if op == "vb":
flow = flow.visualizeBrain()
elif op == "v":
nsamples = args.nsamples
flow = flow.visualize(nsamples=int(nsamples))
elif op == "vc":
k = args.k
flow = flow.clustering(int(k)).visualize()
elif op == "ts":
k = args.k
flow = flow.clustering(int(k))
flow.execute()
with open("model", "a+") as output:
pickle.dump(flow.last.result, output, pickle.HIGHEST_PROTOCOL)
exit("Model Saved")
elif op == "pr":
utils.predict(args.model, args.vector)
exit()
else:
exit("Operator not found")
flow.execute()
import os.path as pth
import thunder
import utils
from workflow import Workflow
from pyspark import SparkContext
sc = SparkContext()
#data = pth.join(pth.dirname(pth.realpath(thunder.__file__)), 'utils/data/fish/images')
data = utils.readNifti("/home/vic/Dev/fMRI/bold_dico.nii")[:,:,:,:100]
flow1 = Workflow(data, sc)\
.extract()\
.visualize()\
.clustering(12)\
.visualize()\
.visualizeBrain()
print "\n=====PLAN====\n" \
"%s" \
"=====PLAN=====\n"%flow1.explain()
flow1.execute()
