def testrun(self):
funcim = load_image(funcfile)
fmriims = FmriImageList.from_image(funcim, volume_start_times=2.)
f1 = ExperimentalQuantitative("f1", lambda t:t)
f2 = ExperimentalQuantitative("f1", lambda t:t**2)
f3 = ExperimentalQuantitative("f1", lambda t:t**3)
f = f1 + f2 + f3
c = Contrast(f1, f)
c.compute_matrix(fmriims.volume_start_times)
c2 = Contrast(f1 + f2, f)
c2.compute_matrix(fmriims.volume_start_times)
outputs = []
outputs.append(model.output_AR1(self.ar1, fmriims, clobber=True))
outputs.append(model.output_resid(self.resid_OLS, fmriims,
clobber=True))
ols = model.OLS(fmriims, f, outputs)
ols.execute()
outputs = []
out_fn = os.path.join(self.out_dir, 'out.nii')
outputs.append(model.output_T(out_fn, c, fmriims, clobber=True))
outputs.append(model.output_F(self.F, c2, fmriims, clobber=True))
outputs.append(model.output_resid(self.resid, fmriims, clobber=True))
rho = load_image(self.ar1)
ar = model.AR1(fmriims, f, rho, outputs)
ar.execute()
def setUp(self):
self.fd = np.asarray(load_image(funcfile))
self.fi = FmriImageList.from_image(load_image(funcfile))
# I think it makes more sense to use fd instead of fi for GLM
# purposes -- reduces some noticeable overhead in creating the
# array from FmriImageList
# create a design matrix, model and contrast matrix
self.design = noise((self.fd.shape[0],3))
self.model = ols_model(self.design)
self.cmatrix = np.array([[1,0,0],[0,1,0]])
def test_write():
fp, fname = mkstemp('.nii')
img = load_image(funcfile)
save_image(img, fname)
test = FmriImageList.from_image(load_image(fname))
yield nose.tools.assert_equal, test[0].affine.shape, (4,4)
yield nose.tools.assert_equal, img[0].affine.shape, (5,4)
yield nose.tools.assert_true, np.allclose(test[0].affine, img[0].affine[1:])
# Under windows, if you don't close before delete, you get a
# locking error.
os.close(fp)
os.remove(fname)
def test_write():
fname = 'myfile.nii'
img = load_image(funcfile)
with InTemporaryDirectory():
save_image(img, fname)
test = FmriImageList.from_image(load_image(fname))
assert_equal(test[0].affine.shape, (4,4))
assert_equal(img[0].affine.shape, (5,4))
# Check the affine...
A = np.identity(4)
A[:3,:3] = img[:,:,:,0].affine[:3,:3]
A[:3,-1] = img[:,:,:,0].affine[:3,-1]
assert_true(np.allclose(test[0].affine, A))
del test
def test_write():
fname = "myfile.nii"
img = load_image(funcfile)
with InTemporaryDirectory():
save_image(img, fname)
test = FmriImageList.from_image(load_image(fname))
assert_equal(test[0].affine.shape, (4, 4))
assert_equal(img[0].affine.shape, (5, 4))
# Check the affine...
A = np.identity(4)
A[:3, :3] = img[:, :, :, 0].affine[:3, :3]
A[:3, -1] = img[:, :, :, 0].affine[:3, -1]
assert_true(np.allclose(test[0].affine, A))
del test
def test_write():
fp, fname = mkstemp('.nii')
img = load_image(funcfile)
save_image(img, fname)
test = FmriImageList.from_image(load_image(fname))
yield assert_equal, test[0].affine.shape, (4,4)
yield assert_equal, img[0].affine.shape, (5,4)
# Check the affine...
A = np.identity(4)
A[:3,:3] = img[:,:,:,0].affine[:3,:3]
A[:3,-1] = img[:,:,:,0].affine[:3,-1]
yield assert_true, np.allclose(test[0].affine, A)
# Under windows, if you don't close before delete, you get a
# locking error.
os.close(fp)
os.remove(fname)
def setup():
# Suppress warnings during tests to reduce noise
warnings.simplefilter("ignore")
def teardown():
# Clear list of warning filters
warnings.resetwarnings()
# Module globals
FIMG = load_image(funcfile)
# Put time on first axis
FIMG = img_rollaxis(FIMG, "t")
FDATA = FIMG.get_data()
FIL = FmriImageList.from_image(FIMG)
# I think it makes more sense to use FDATA instead of FIL for GLM
# purposes -- reduces some noticeable overhead in creating the
# array from FmriImageList
# create a design matrix, model and contrast matrix
DESIGN = noise((FDATA.shape[0], 3))
MODEL = OLSModel(DESIGN)
CMATRIX = np.array([[1, 0, 0], [0, 1, 0]])
# two prototypical functions in a GLM analysis
def fit(input):
return MODEL.fit(input).resid
def setup():
# Suppress warnings during tests to reduce noise
warnings.simplefilter("ignore")
def teardown():
# Clear list of warning filters
warnings.resetwarnings()
# Module globals
FIMG = load_image(funcfile)
# Put time on first axis
FIMG = rollimg(FIMG, 't')
FDATA = FIMG.get_data()
FIL = FmriImageList.from_image(FIMG)
# I think it makes more sense to use FDATA instead of FIL for GLM
# purposes -- reduces some noticeable overhead in creating the
# array from FmriImageList
# create a design matrix, model and contrast matrix
DESIGN = noise((FDATA.shape[0], 3))
MODEL = OLSModel(DESIGN)
CMATRIX = np.array([[1, 0, 0], [0, 1, 0]])
# two prototypical functions in a GLM analysis
def fit(input):
return MODEL.fit(input).resid
