def test_iterate_over_image(self):
# Fit a model, iterating over the slices of an array
# associated to an FmriImage.
c = np.zeros(self.fd.shape[1:]) + 0.5
fd = self.fd
res_gen = result_generator(flatten_generator(fmri_generator(fd)))
write_data(c, unflatten_generator(contrast_generator(res_gen)))
# Fit a model, iterating over the array associated to an
# FmriImage, iterating over a list of ROIs defined by binary
# regions of the same shape as a frame of FmriImage
# this might really be an anatomical image or AR(1) coefficients
a = np.asarray(fd[0])
p = np.greater(a, a.mean())
d = np.ones(fd.shape[1:]) * 2.0
flat_gen = flatten_generator(fmri_generator(fd, parcels(p)))
write_data(d, contrast_generator(result_generator(flat_gen)))
yield assert_array_almost_equal, d, c
e = np.zeros(fd.shape[1:]) + 3.0
flat_gen2 = flatten_generator(fmri_generator(fd, parcels(p)))
write_data(e, f_generator(contrast, result_generator(flat_gen2)))
yield assert_array_almost_equal, d, e
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 model_generator(formula, data, volume_start_times, iterable=None,
slicetimes=None, model_type=OLSModel,
model_params = lambda x: ()):
"""
Generator for the models for a pass of fmristat analysis.
"""
for i, d in matrix_generator(fmri_generator(data, iterable=iterable)):
model_args = model_params(i) # model may depend on i
rmodel = model_type(formula.design(volume_start_times), *model_args)
yield i, d, rmodel
def test_iter():
img = load_image(funcfile)
img_shape = img.shape
exp_shape = (img_shape[0],) + img_shape[2:]
j = 0
for i, d in fmri_generator(img):
j += 1
yield assert_equal(d.shape, exp_shape)
del(i); gc.collect()
yield assert_equal(j, img_shape[1])
def test_iter():
img = load_image(funcfile)
# flip to time first version so this makes sense
from nipy.core.reference.coordinate_map import reorder_input
arr = np.asarray(img).T
coordmap = reorder_input(img.coordmap)
img_t1 = Image(arr, coordmap)
slice_shape = (arr.shape[0],) + arr.shape[2:]
j = 0
for i, d in fmri_generator(img_t1):
j += 1
yield nose.tools.assert_equal, d.shape, slice_shape
del(i); gc.collect()
yield nose.tools.assert_equal, j, 3
