def test_run():
ar1_fname = 'ar1_out.nii'
funcim = load_image(funcfile)
fmriims = FmriImageList.from_image(funcim, volume_start_times=2.)
one_vol = fmriims[0]
# Formula - with an intercept
t = Term('t')
f = Formula([t, t**2, t**3, 1])
# Design matrix and contrasts
time_vector = make_recarray(fmriims.volume_start_times, 't')
con_defs = dict(c=t, c2=t+t**2)
desmtx, cmatrices = f.design(time_vector, contrasts=con_defs)
# Run with Image and ImageList
for inp_img in (img_rollaxis(funcim, 't'), fmriims):
with InTemporaryDirectory():
# Run OLS model
outputs = []
outputs.append(model.output_AR1(ar1_fname, fmriims))
outputs.append(model.output_resid('resid_OLS_out.nii', fmriims))
ols = model.OLS(fmriims, f, outputs)
ols.execute()
# Run AR1 model
outputs = []
outputs.append(
model.output_T('T_out.nii', cmatrices['c'], fmriims))
outputs.append(
model.output_F('F_out.nii', cmatrices['c2'], fmriims))
outputs.append(
model.output_resid('resid_AR_out.nii', fmriims))
rho = load_image(ar1_fname)
ar = model.AR1(fmriims, f, rho, outputs)
ar.execute()
f_img = load_image('F_out.nii')
assert_equal(f_img.shape, one_vol.shape)
f_data = f_img.get_data()
assert_true(np.all((f_data>=0) & (f_data<30)))
resid_img = load_image('resid_AR_out.nii')
assert_equal(resid_img.shape, funcim.shape[3:] + one_vol.shape)
assert_array_almost_equal(np.mean(resid_img.get_data()), 0, 3)
e_img = load_image('T_out_effect.nii')
sd_img = load_image('T_out_sd.nii')
t_img = load_image('T_out_t.nii')
t_data = t_img.get_data()
assert_array_almost_equal(t_data,
e_img.get_data() / sd_img.get_data())
assert_true(np.all(np.abs(t_data) < 6))
# Need to delete to help windows delete temporary files
del rho, resid_img, f_img, e_img, sd_img, t_img, f_data, t_data
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
