def test_labels1():
img = load_image(funcfile)
parcelmap = fromarray(np.asarray(img[0]), 'kji', 'zyx')
parcelmap = (np.asarray(parcelmap) * 100).astype(np.int32)
v = 0
for i, d in fmri_generator(img, parcels(parcelmap)):
v += d.shape[1]
assert_equal(v, parcelmap.size)
def test_parcels1():
rho = gimg
parcelmap = np.asarray(rho).astype(np.int32)
test = np.zeros(parcelmap.shape)
v = 0
for i, d in data_generator(test, parcels(parcelmap)):
v += d.shape[0]
yield assert_equal, v, np.product(test.shape)
def test_parcels3():
rho = gimg[0]
parcelmap = np.asarray(rho).astype(np.int32)
labels = np.unique(parcelmap)
test = np.zeros(rho.shape)
v = 0
for i, d in data_generator(test, parcels(parcelmap, labels=labels)):
v += d.shape[0]
yield assert_equal, v, np.product(test.shape)
def test_labels1():
img = load_image(funcfile)
data = img.get_data()
parcelmap = Image(img[0].get_data(), AfT('kji', 'zyx', np.eye(4)))
parcelmap = (parcelmap.get_data() * 100).astype(np.int32)
v = 0
for i, d in axis0_generator(data, parcels(parcelmap)):
v += d.shape[1]
assert_equal(v, parcelmap.size)
def test_labels1():
img = load_image(funcfile)
data = img.get_data()
parcelmap = Image(img[0].get_data(), AfT("kji", "zyx", np.eye(4)))
parcelmap = (parcelmap.get_data() * 100).astype(np.int32)
v = 0
for i, d in axis0_generator(data, parcels(parcelmap)):
v += d.shape[1]
assert_equal(v, parcelmap.size)
def execute(self):
iterable = parcels(self.rho, exclude=[np.inf])
def model_params(i):
return (self.rho.get_data()[i].mean(), )
# Generates indexer, data, model
m = model_generator(self.formula,
self.data,
self.volume_start_times,
iterable=iterable,
model_type=ARModel,
model_params=model_params)
# Generates indexer, data, 2D results
r = results_generator(m)
def reshape(i, x):
"""
To write output, arrays have to be reshaped --
this function does the appropriate reshaping for the two
passes of fMRIstat.
These passes are:
i) 'slices through the z-axis'
ii) 'parcels of approximately constant AR1 coefficient'
"""
if len(x.shape) == 2: # 2D imput matrix
if type(i) is type(1): # integer indexing
# reshape to ND (where N is probably 4)
x.shape = (x.shape[0], ) + self.fmri_image[0].shape[1:]
# Convert lists to tuples, put anything else into a tuple
if type(i) not in [type([]), type(())]:
i = (i, )
else:
i = tuple(i)
# Add : to indexing
i = (slice(None, None, None), ) + tuple(i)
else: # not 2D
if type(i) is type(1): # integer indexing
x.shape = self.fmri_image[0].shape[1:]
return i, x
# Put results pulled from results generator r, into outputs
o = generate_output(self.outputs, r, reshape=reshape)
def execute(self):
iterable = parcels(self.rho, exclude=[np.inf])
def model_params(i):
return (np.asarray(self.rho)[i].mean(),)
# Generates indexer, data, model
m = model_generator(self.formula, self.data,
self.volume_start_times,
iterable=iterable,
model_type=ARModel,
model_params=model_params)
# Generates indexer, data, 2D results
r = results_generator(m)
def reshape(i, x):
"""
To write output, arrays have to be reshaped --
this function does the appropriate reshaping for the two
passes of fMRIstat.
These passes are:
i) 'slices through the z-axis'
ii) 'parcels of approximately constant AR1 coefficient'
"""
if len(x.shape) == 2: # 2D imput matrix
if type(i) is type(1): # integer indexing
# reshape to ND (where N is probably 4)
x.shape = (x.shape[0],) + self.fmri_image[0].shape[1:]
# Convert lists to tuples, put anything else into a tuple
if type(i) not in [type([]), type(())]:
i = (i,)
else:
i = tuple(i)
# Add : to indexing
i = (slice(None,None,None),) + tuple(i)
else: # not 2D
if type(i) is type(1): # integer indexing
x.shape = self.fmri_image[0].shape[1:]
return i, x
# Put results pulled from results generator r, into outputs
o = generate_output(self.outputs, r, reshape=reshape)
def execute(self):
iterable = parcels(self.rho, exclude=[np.inf])
def model_params(i):
return (np.asarray(self.rho)[i].mean(),)
m = model_generator(self.formula, self.data,
self.volume_start_times,
iterable=iterable,
model_type=ARModel,
model_params=model_params)
r = results_generator(m)
def reshape(i, x):
"""
To write output, arrays have to be reshaped --
this function does the appropriate reshaping for the two
passes of fMRIstat.
These passes are:
i) 'slices through the z-axis'
ii) 'parcels of approximately constant AR1 coefficient'
"""
if len(x.shape) == 2:
if type(i) is type(1):
x.shape = (x.shape[0],) + self.fmri_image[0].shape[1:]
if type(i) not in [type([]), type(())]:
i = (i,)
else:
i = tuple(i)
i = (slice(None,None,None),) + tuple(i)
else:
if type(i) is type(1):
x.shape = self.fmri_image[0].shape[1:]
return i, x
o = generate_output(self.outputs, r, reshape=reshape)
