def test_loadflat():
alist = [dict(a='one', c='three', b='two'),
dict(a='one', c='three', b='two')]
fd, name = mkstemp(suffix='.npz')
np.savez(name,a=alist)
aloaded = loadflat(name)['a']
os.unlink(name)
yield assert_equal, len(aloaded), 2
yield assert_equal, sorted(aloaded[0].items()), sorted(alist[0].items())
adict = dict(a='one', c='three', b='two')
fd, name = mkstemp(suffix='.npz')
np.savez(name,a=adict)
aloaded = loadflat(name)['a']
os.unlink(name)
yield assert_true, isinstance(aloaded, dict)
yield assert_equal, sorted(aloaded.items()), sorted(adict.items())
def _get_session_info(self, session_info_file):
key = "session_info"
data = loadflat(session_info_file)
session_info = data[key]
if isinstance(session_info, dict):
session_info = [session_info]
return session_info
def test_loadflat():
alist = [
dict(a='one', c='three', b='two'),
dict(a='one', c='three', b='two')
]
fd, name = mkstemp(suffix='.npz')
np.savez(name, a=alist)
aloaded = loadflat(name)['a']
os.unlink(name)
yield assert_equal, len(aloaded), 2
yield assert_equal, sorted(aloaded[0].items()), sorted(alist[0].items())
adict = dict(a='one', c='three', b='two')
fd, name = mkstemp(suffix='.npz')
np.savez(name, a=adict)
aloaded = loadflat(name)['a']
os.unlink(name)
yield assert_true, isinstance(aloaded, dict)
yield assert_equal, sorted(aloaded.items()), sorted(adict.items())
def _list_outputs(self):
outputs = self._outputs().get()
if not hasattr(self, "sessinfo"): # backwards compatibility
data = loadflat(os.path.join(os.getcwd(), "%s_modelspec.npz" % self.inputs.subject_id))
if isinstance(data["session_info"], dict):
self.sessinfo = [data["session_info"]]
else:
self.sessinfo = data["session_info"]
outputs["session_info"] = self.sessinfo
return outputs
'''
Created on 8 Nov 2010
@author: filo
'''
from nipype.utils.filemanip import loadflat
from neuroutils import ThresholdGGMM
c = loadflat("/media/data/2010reliability/workdir/pipeline/line_bisection/model/threshold_topo_ggmm/_subject_id_20100929_16849/ggmm/crash-20101116-135736-filo-_ggmm2.npz")
n = c['node']
print n.inputs
ggmm = ThresholdGGMM(mask_file= n.inputs.mask_file, stat_image=n.inputs.stat_image)
ggmm.run()
