def group_analysis(design, contrast):
""" Compute group analysis effect, t, sd for `design` and `contrast`
Saves to disk in 'group' analysis directory
Parameters
----------
design : {'block', 'event'}
contrast : str
contrast name
"""
array = np.array # shorthand
# Directory where output will be written
odir = futil.ensure_dir(futil.DATADIR, 'group', design, contrast)
# Which subjects have this (contrast, design) pair?
subj_con_dirs = futil.subj_des_con_dirs(design, contrast)
if len(subj_con_dirs) == 0:
raise ValueError('No subjects for %s, %s' % (design, contrast))
# Assemble effects and sds into 4D arrays
sds = []
Ys = []
for s in subj_con_dirs:
sd_img = load_image(pjoin(s, "sd.nii"))
effect_img = load_image(pjoin(s, "effect.nii"))
sds.append(sd_img.get_data())
Ys.append(effect_img.get_data())
sd = array(sds)
Y = array(Ys)
# This function estimates the ratio of the fixed effects variance
# (sum(1/sd**2, 0)) to the estimated random effects variance
# (sum(1/(sd+rvar)**2, 0)) where rvar is the random effects variance.
# The EM algorithm used is described in:
#
# Worsley, K.J., Liao, C., Aston, J., Petre, V., Duncan, G.H.,
# Morales, F., Evans, A.C. (2002). \'A general statistical
# analysis for fMRI data\'. NeuroImage, 15:1-15
varest = onesample.estimate_varatio(Y, sd)
random_var = varest['random']
# XXX - if we have a smoother, use
# random_var = varest['fixed'] * smooth(varest['ratio'])
# Having estimated the random effects variance (and possibly smoothed it),
# the corresponding estimate of the effect and its variance is computed and
# saved.
# This is the coordmap we will use
coordmap = futil.load_image_ds105("fiac_00", "wanatomical.nii").coordmap
adjusted_var = sd**2 + random_var
adjusted_sd = np.sqrt(adjusted_var)
results = onesample.estimate_mean(Y, adjusted_sd)
for n in ['effect', 'sd', 't']:
im = api.Image(results[n], copy(coordmap))
save_image(im, pjoin(odir, "%s.nii" % n))
def test_save4():
# Same as test_save3 except we have reordered the 'ijk' input axes.
shape = (13, 5, 7, 3)
step = np.array([3.45, 2.3, 4.5, 6.9])
# When the input coords are in the 'ljki' order, the affines get
# rearranged. Note that the 'start' below, must be 0 for
# non-spatial dimensions, because we have no way to store them in
# most cases. For example, a 'start' of [1,5,3,1] would be lost on
# reload
mni_xyz = mni_csm(3).coord_names
cmap = AT(CS('tkji'), CS((('t', ) + mni_xyz[::-1])),
from_matvec(np.diag([2., 3, 5, 1]), step))
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
tmp = load_image(TMP_FNAME)
data = tmp.get_data().copy()
# Detach image from file so we can delete it
img2 = api.Image(data, tmp.coordmap, tmp.metadata)
del tmp
P = np.array([[0, 0, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 0, 0],
[1, 0, 0, 0, 0], [0, 0, 0, 0, 1]])
res = np.dot(P, np.dot(img.affine, P.T))
# the step part of the affine should be set correctly
assert_array_almost_equal(res[:4, :4], img2.affine[:4, :4])
# start in the spatial dimensions should be set correctly
assert_array_almost_equal(res[:3, -1], img2.affine[:3, -1])
# start in the time dimension should be 3.45 as in img, because NIFTI stores
# the time offset in hdr[``toffset``]
assert_not_equal(res[3, -1], img2.affine[3, -1])
assert_equal(res[3, -1], 3.45)
# shapes should be reversed because img has coordinates reversed
assert_equal(img.shape[::-1], img2.shape)
# data should be transposed because coordinates are reversed
assert_array_almost_equal(np.transpose(img2.get_data(), [3, 2, 1, 0]),
img.get_data())
# coordinate names should be reversed as well
assert_equal(img2.coordmap.function_domain.coord_names,
img.coordmap.function_domain.coord_names[::-1])
assert_equal(img2.coordmap.function_domain.coord_names,
('i', 'j', 'k', 't'))
def fixed_effects(subj, design):
""" Fixed effects (within subject) for OpenfMRI ds105 model
Finds run by run estimated model results, creates fixed effects results
image per subject.
Parameters
----------
subj : int
subject number 1..6 inclusive
design : {'standard'}
design type
"""
# First, find all the effect and standard deviation images
# for the subject and this design type
path_dict = futil.path_info_design(subj, design)
rootdir = path_dict['rootdir']
# The output directory
fixdir = pjoin(rootdir, "fixed")
# Fetch results images from run estimations
results = futil.results_table(path_dict)
# Get our hands on the relevant coordmap to save our results
coordmap = futil.load_image_ds105("_%02d" % subj,
"wanatomical.nii").coordmap
# Compute the "fixed" effects for each type of contrast
for con in results:
fixed_effect = 0
fixed_var = 0
for effect, sd in results[con]:
effect = load_image(effect).get_data()
sd = load_image(sd).get_data()
var = sd**2
# The optimal, in terms of minimum variance, combination of the
# effects has weights 1 / var
#
# XXX regions with 0 variance are set to 0
# XXX do we want this or np.nan?
ivar = np.nan_to_num(1. / var)
fixed_effect += effect * ivar
fixed_var += ivar
# Now, compute the fixed effects variance and t statistic
fixed_sd = np.sqrt(fixed_var)
isd = np.nan_to_num(1. / fixed_sd)
fixed_t = fixed_effect * isd
# Save the results
odir = futil.ensure_dir(fixdir, con)
for a, n in zip([fixed_effect, fixed_sd, fixed_t],
['effect', 'sd', 't']):
im = api.Image(a, copy(coordmap))
save_image(im, pjoin(odir, '%s.nii' % n))
def test_save3():
# A test to ensure that when a file is saved, the affine
# and the data agree. In this case, things don't agree:
# i) the pixdim is off
# ii) makes the affine off
step = np.array([3.45, 2.3, 4.5, 6.9])
shape = (13, 5, 7, 3)
mni_xyz = mni_csm(3).coord_names
cmap = AT(CS('jkli'), CS(('t', ) + mni_xyz[::-1]),
from_matvec(np.diag([0, 3, 5, 1]), step))
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
# with InTemporaryDirectory():
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
tmp = load_image(TMP_FNAME)
# Detach image from file so we can delete it
data = tmp.get_data().copy()
img2 = api.Image(data, tmp.coordmap, tmp.metadata)
del tmp
assert_equal(tuple([img.shape[l] for l in [3, 2, 1, 0]]), img2.shape)
a = np.transpose(img.get_data(), [3, 2, 1, 0])
assert_false(np.allclose(img.affine, img2.affine))
assert_true(np.allclose(a, img2.get_data()))
def test_save2():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file
shape = (13, 5, 7, 3)
step = np.array([3.45, 2.3, 4.5, 6.93])
cmap = api.AffineTransform.from_start_step('ijkt', 'xyzt', [1, 3, 5, 0],
step)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
img2 = load_image(TMP_FNAME)
assert_array_almost_equal(img.affine, img2.affine)
assert_equal(img.shape, img2.shape)
assert_array_almost_equal(img2.get_data(), img.get_data())
del img2
def test_save2b():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file. This example has a
# non-diagonal affine matrix for the spatial part, but is 'diagonal' for the
# space part.
#
# make a 5x5 transformation (for 4d image)
step = np.array([3.45, 2.3, 4.5, 6.9])
A = np.random.standard_normal((3, 3))
B = np.diag(list(step) + [1])
B[:3, :3] = A
shape = (13, 5, 7, 3)
cmap = api.vox2mni(B)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
img2 = load_image(TMP_FNAME)
assert_array_almost_equal(img.affine, img2.affine)
assert_equal(img.shape, img2.shape)
assert_array_almost_equal(img2.get_data(), img.get_data())
del img2