def ttest(x):
"""
returns the t-test for each row of the data x
"""
import nipy.neurospin.group.onesample as fos
t = fos.stat(x.T,id='student',axis=0)
return np.squeeze(t)
def compare_with_nipy():
"""
Check whether we obtain the same results as nipy
"""
import nipy.neurospin.group.onesample as fos
nsamples = 15
ndata = 1000
vardata = np.random.rand(ndata,nsamples)
data = np.zeros((ndata, nsamples))
for i in range(ndata):
data[i] = generate_data(0, 1, vardata[i])
# my computation
t1 = t_stat(data)
t2 = mfx_t_stat(data,vardata)
#nipy model
t3 = fos.stat(data, id='student', axis=1)
t4 = np.squeeze(fos.stat_mfx(data, vardata, id='student_mfx', axis=1))
diff = t4-t2
maxdiff = (diff**2).max()
if maxdiff>1:
i = np.abs(diff).argmax()
print t1[i], t2[i], t4[i]
print mfx_t_stat(np.array([data[i]]),np.array([vardata[i]]), niter=20)
print ((t1-t2)**2).sum(), ((t1-t4)**2).sum()
def test_onesample_stat():
dx, dy, dz = 3, 4, 2
nvox = dx*dy*dz
nsub = 12
# Make surrogate data
aux = np.arange(nvox)
x = np.reshape(aux.repeat(nsub), [dx, dy, dz, nsub])
# Gold standard
y_target = np.inf * np.ones(nvox)
y_target[0] = 0.0
# Test: input C-contiguous, data owner, axis=3
y = onesample.stat(x, axis=3).reshape(nvox)
assert_equal(y, y_target)
# Test: input F-contiguous, not owner, axis=0
y = onesample.stat(x.T, axis=0).reshape(nvox)
assert_equal(y, y_target)
# Test: input C-contiguous, data owner, axis=0
xT = x.T.copy()
y = onesample.stat(xT, axis=0).reshape(nvox)
assert_equal(y, y_target)
