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. this should raise a warnings
# about 'non-diagonal' affine matrix
# make a 5x5 transformatio
step = np.array([3.45,2.3,4.5,6.9])
A = np.random.standard_normal((4,4))
B = np.diag(list(step)+[1])
B[:4,:4] = A
shape = (13,5,7,3)
cmap = api.AffineTransform.from_params('ijkt', 'xyzt', B)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
save_image(img, tmpfile.name)
img2 = load_image(tmpfile.name)
yield assert_false, np.allclose(img.affine, img2.affine)
yield assert_true, np.allclose(img.affine[:3,:3], img2.affine[:3,:3])
yield assert_equal, img.shape, img2.shape
yield assert_true, np.allclose(np.asarray(img2), np.asarray(img))
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_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(np.asarray(img), [3,2,1,0])
assert_false(np.allclose(img.affine, img2.affine))
assert_true(np.allclose(a, img2.get_data()))
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_fiac("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 save(self):
"""
Save current Image data to disk
"""
if not self.clobber and path.exists(self.filename):
raise ValueError('trying to clobber existing file')
save_image(self._im, self.filename)
self._flushed = True
del (self._im)
def save(self):
"""
Save current Image data to disk
"""
if not self.clobber and path.exists(self.filename):
raise ValueError('trying to clobber existing file')
save_image(self._im, self.filename)
self._flushed = True
del(self._im)
def test_save1():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file
img = load_image(funcfile)
save_image(img, tmpfile.name)
img2 = load_image(tmpfile.name)
yield assert_true, np.allclose(img.affine, img2.affine)
yield assert_equal, img.shape, img2.shape
yield assert_true, np.allclose(np.asarray(img2), np.asarray(img))
def test_save1():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file
img = load_image(funcfile)
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_save1():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file
img = load_image(funcfile)
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_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 fixed_effects(subj, design):
""" Fixed effects (within subject) for FIAC 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_fiac("_%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 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_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
cmap = api.Affine.from_start_step('lkji', 'tzyx', [2,5,3,1], step)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
save_image(img, tmpfile.name)
img2 = load_image(tmpfile.name)
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
yield assert_array_almost_equal, res[:4,:4], img2.affine[:4,:4]
# start in the spatial dimensions should be set correctly
yield assert_array_almost_equal, res[:3,-1], img2.affine[:3,-1]
# start in the time dimension should not be 2 as in img, but 0
# because NIFTI dosen't have a time start
yield assert_false, (res[3,-1] == img2.affine[3,-1])
yield assert_true, (res[3,-1] == 2)
yield assert_true, (img2.affine[3,-1] == 0)
# shapes should be reversed because img has coordinates reversed
yield assert_equal, img.shape[::-1], img2.shape
# data should be transposed because coordinates are reversed
yield (assert_array_almost_equal,
np.transpose(np.asarray(img2),[3,2,1,0]),
np.asarray(img))
# coordinate names should be reversed as well
yield assert_equal, img2.coordmap.input_coords.coord_names, \
img.coordmap.input_coords.coord_names[::-1]
yield assert_equal, img2.coordmap.input_coords.coord_names, \
['i', 'j', 'k', 'l']
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_space_time_realign():
path, fname = psplit(funcfile)
original_affine = load_image(funcfile).affine
path, fname = psplit(funcfile)
froot, _ = fname.split('.', 1)
with InTemporaryDirectory():
# Make another image with .nii extension and extra dot in filename
save_image(load_image(funcfile), 'my.test.nii')
for in_fname, out_fname in ((funcfile, froot + '_mc.nii.gz'),
('my.test.nii', 'my.test_mc.nii.gz')):
xforms = reg.space_time_realign(in_fname, 2.0, out_name='.')
assert_true(np.allclose(xforms[0].as_affine(), np.eye(4), atol=1e-7))
assert_false(np.allclose(xforms[-1].as_affine(), np.eye(4), atol=1e-3))
img = load_image(out_fname)
npt.assert_almost_equal(original_affine, img.affine)
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 group_analysis(design, contrast):
"""
Compute group analysis effect, sd and t
for a given contrast and design type
"""
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?
subjects = futil.subject_dirs(design, contrast)
sd = array([array(load_image(pjoin(s, "sd.nii"))) for s in subjects])
Y = array([array(load_image(pjoin(s, "effect.nii"))) for s in subjects])
# 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_fiac("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], coordmap.copy())
save_image(im, pjoin(odir, "%s.nii" % n))
def fixed_effects(subj, design):
"""
Fixed effects (within subject) for FIAC model
"""
# First, find all the effect and standard deviation images
# for the subject and this design type
path_dict = futil.path_info2(subj, design)
rootdir = path_dict['rootdir']
# The output directory
fixdir = pjoin(rootdir, "fixed")
results = futil.results_table(path_dict)
# Get our hands on the relevant coordmap to
# save our results
coordmap = futil.load_image_fiac("fiac_%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); sd = load_image(sd)
var = np.array(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, coordmap.copy())
save_image(im, pjoin(odir, '%s.nii' % n))
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)
save_image(img, tmpfile.name)
img2 = load_image(tmpfile.name)
yield assert_true, np.allclose(img.affine, img2.affine)
yield assert_equal, img.shape, img2.shape
yield assert_true, np.allclose(np.asarray(img2), np.asarray(img))
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_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(np.asarray(img2),[3,2,1,0]),
np.asarray(img))
# 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 test_space_time_realign():
path, fname = psplit(funcfile)
original_affine = load_image(funcfile).affine
path, fname = psplit(funcfile)
froot, _ = fname.split('.', 1)
with InTemporaryDirectory():
# Make another image with .nii extension and extra dot in filename
save_image(load_image(funcfile), 'my.test.nii')
for in_fname, out_fname in ((funcfile, froot + '_mc.nii.gz'),
('my.test.nii', 'my.test_mc.nii.gz')):
xforms = reg.space_time_realign(in_fname, 2.0, out_name='.')
assert_true(
np.allclose(xforms[0].as_affine(), np.eye(4), atol=1e-7))
assert_false(
np.allclose(xforms[-1].as_affine(), np.eye(4), atol=1e-3))
img = load_image(out_fname)
npt.assert_almost_equal(original_affine, img.affine)
def seg_recovery(y_pred, filename):
label = load_image(PRE_LABEL_PATH + filename)
shape = label.get_data().shape
segment = np.zeros(shape)
h1 = y_pred[0].around()
h2 = y_pred[1].around()
area = crop_setting['3d' + str(shape[2])]
segment[area['x'][0]:area['x'][1], area['y'][0]:area['y'][1],
area['z1'][0]:area['z1'][1], 0] = h1[0]
segment[area['x'][0]:area['x'][1], area['y'][0]:area['y'][1],
area['z2'][0]:area['z2'][1], 1] = h2[0]
img = Image(segment, label.coordmap)
save_image(img, OUTPUT + filename)
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 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)
cmap = api.Affine.from_start_step('jkli', 'tzyx', [0,3,5,1], step)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
save_image(img, tmpfile.name)
img2 = load_image(tmpfile.name)
yield assert_equal, tuple([img.shape[l] for l in [3,0,1,2]]), img2.shape
a = np.transpose(np.asarray(img), [3,0,1,2])
yield assert_false, np.allclose(img.affine, img2.affine)
yield assert_true, np.allclose(a, np.asarray(img2))
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 test_roundtrip_fromarray():
data = np.random.rand(10,20,30)
img = fromarray(data, 'kji', 'xyz')
fd, name = mkstemp(suffix='.nii.gz')
tmpfile = open(name)
save_image(img, tmpfile.name)
img2 = load_image(tmpfile.name)
data2 = np.asarray(img2)
tmpfile.close()
os.unlink(name)
# verify data
yield assert_true, np.allclose(data2, data)
yield assert_true, np.allclose(data2.mean(), data.mean())
yield assert_true, np.allclose(data2.min(), data.min())
yield assert_true, np.allclose(data2.max(), data.max())
# verify shape and ndims
yield assert_equal, img2.shape, img.shape
yield assert_equal, img2.ndim, img.ndim
# verify affine
yield assert_true, np.allclose(img2.affine, img.affine)
def test_file_roundtrip():
img = load_template_img()
fd, name = mkstemp(suffix='.nii.gz')
tmpfile = open(name)
save_image(img, tmpfile.name)
img2 = load_image(tmpfile.name)
data = np.asarray(img)
data2 = np.asarray(img2)
tmpfile.close()
os.unlink(name)
# verify data
yield assert_true, np.allclose(data2, data)
yield assert_true, np.allclose(data2.mean(), data.mean())
yield assert_true, np.allclose(data2.min(), data.min())
yield assert_true, np.allclose(data2.max(), data.max())
# verify shape and ndims
yield assert_equal, img2.shape, img.shape
yield assert_equal, img2.ndim, img.ndim
# verify affine
yield assert_true, np.allclose(img2.affine, img.affine)
def uint8_to_dtype(dtype, name):
dtype = dtype
shape = (2,3,4)
dmax = np.iinfo(np.uint8).max
data = np.random.randint(0, dmax, size=shape)
data[0,0,0] = 0
data[1,0,0] = dmax
data = data.astype(np.uint8) # randint returns np.int32
img = fromarray(data, 'kji', 'zxy')
newimg = save_image(img, name, dtype=dtype)
newdata = np.asarray(newimg)
return newdata, data
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
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
def test_header_roundtrip():
img = load_template_img()
fd, name = mkstemp(suffix='.nii.gz')
tmpfile = open(name)
hdr = img.header
# Update some header values and make sure they're saved
hdr['slice_duration'] = 0.200
hdr['intent_p1'] = 2.0
hdr['descrip'] = 'descrip for TestImage:test_header_roundtrip'
hdr['slice_end'] = 12
img.header = hdr
save_image(img, tmpfile.name)
newimg = load_image(tmpfile.name)
newhdr = newimg.header
tmpfile.close()
os.unlink(name)
yield (assert_array_almost_equal,
newhdr['slice_duration'],
hdr['slice_duration'])
yield assert_equal, newhdr['intent_p1'], hdr['intent_p1']
yield assert_equal, newhdr['descrip'], hdr['descrip']
yield assert_equal, newhdr['slice_end'], hdr['slice_end']
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. this should raise a warnings
# about 'non-diagonal' affine matrix
# make a 5x5 transformation
step = np.array([3.45,2.3,4.5,6.9])
A = np.random.standard_normal((4,4))
B = np.diag(list(step)+[1])
B[:4,:4] = A
shape = (13,5,7,3)
cmap = api.AffineTransform.from_params('ijkt', 'xyzt', 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_false(np.allclose(img.affine, img2.affine))
assert_array_almost_equal(img.affine[:3,:3], img2.affine[:3,:3])
assert_equal(img.shape, img2.shape)
assert_array_almost_equal(img2.get_data(), img.get_data())
del img2
def float32_to_dtype(dtype):
# Utility function for the scaling_float32 function
dtype = dtype
shape = (2,3,4)
# set some value value for scaling our data
scale = np.iinfo(np.uint16).max * 2.0
data = np.random.normal(size=(2,3,4), scale=scale)
data[0,0,0] = np.finfo(np.float32).max
data[1,0,0] = np.finfo(np.float32).min
# random.normal will return data as native machine type
data = data.astype(np.float32)
img = fromarray(data, 'kji', 'zyx')
newimg = save_image(img, gtmpfile.name, dtype=dtype)
newdata = np.asarray(newimg)
return newdata, data
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 run_model(subj, run):
"""
Single subject fitting of OpenfMRI ds105 model
"""
#----------------------------------------------------------------------
# Set initial parameters of the OpenfMRI ds105 dataset
#----------------------------------------------------------------------
# Number of volumes in the fMRI data
nvol = 121
# The TR of the experiment
TR = 2.5
# The time of the first volume
Tstart = 0.0
# The array of times corresponding to each volume in the fMRI data
volume_times = np.arange(nvol) * TR + Tstart
# This recarray of times has one column named 't'. It is used in the
# function design.event_design to create the design matrices.
volume_times_rec = make_recarray(volume_times, 't')
# Get a path description dictionary that contains all the path data relevant
# to this subject/run
path_info = futil.path_info_run(subj, run)
#----------------------------------------------------------------------
# Experimental design
#----------------------------------------------------------------------
# Load the experimental description from disk. We have utilities in futil
# that reformat the original OpenfMRI ds105-supplied format into something
# where the factorial structure of the design is more explicit. This has
# already been run once, and get_experiment_initial() will simply load the
# newly-formatted design description files (.csv) into record arrays.
experiment = futil.get_experiment(path_info)
# Create design matrices for the "initial" and "experiment" factors, saving
# the default contrasts.
# The function event_design will create design matrices, which in the case
# of "experiment" will have num_columns = (# levels of speaker) * (# levels
# of sentence) * len(delay.spectral) = 2 * 2 * 2 = 8. For "initial", there
# will be (# levels of initial) * len([hrf.glover]) = 1 * 1 = 1.
# Here, delay.spectral is a sequence of 2 symbolic HRFs that are described
# in:
#
# Liao, C.H., Worsley, K.J., Poline, J-B., Aston, J.A.D., Duncan, G.H.,
# Evans, A.C. (2002). \'Estimating the delay of the response in fMRI
# data.\' NeuroImage, 16:593-606.
# The contrast definitions in ``cons_exper`` are a dictionary with keys
# ['constant_0', 'constant_1', 'speaker_0', 'speaker_1', 'sentence_0',
# 'sentence_1', 'sentence:speaker_0', 'sentence:speaker_1'] representing the
# four default contrasts: constant, main effects + interactions, each
# convolved with 2 HRFs in delay.spectral. For example, sentence:speaker_0
# is the interaction of sentence and speaker convolved with the first (=0)
# of the two HRF basis functions, and sentence:speaker_1 is the interaction
# convolved with the second (=1) of the basis functions.
# XXX use the hrf __repr__ for naming contrasts
X_exper, cons_exper = design.block_design(experiment,
volume_times_rec,
hrfs=delay.spectral,
level_contrasts=True)
# In addition to factors, there is typically a "drift" term. In this case,
# the drift is a natural cubic spline with a not at the midpoint
# (volume_times.mean())
vt = volume_times # shorthand
drift = np.array([vt**i for i in range(4)] + [(vt - vt.mean())**3 *
(np.greater(vt, vt.mean()))])
for i in range(drift.shape[0]):
drift[i] /= drift[i].max()
# We transpose the drift so that its shape is (nvol,5) so that it will have
# the same number of rows as X_exper.
drift = drift.T
# There are helper functions to create these drifts: design.fourier_basis,
# design.natural_spline. Therefore, the above is equivalent (except for
# the normalization by max for numerical stability) to
#
# >>> drift = design.natural_spline(t, [volume_times.mean()])
# Stack all the designs, keeping the new contrasts which has the same keys
# as cons_exper, but its values are arrays with 15 columns, with the
# non-zero entries matching the columns of X corresponding to X_exper
X, cons = design.stack_designs((X_exper, cons_exper), (drift, {}))
# Sanity check: delete any non-estimable contrasts
for k in cons.keys():
if not isestimable(cons[k], X):
del (cons[k])
warnings.warn("contrast %s not estimable for this run" % k)
# The default contrasts are all t-statistics. We may want to output
# F-statistics for 'speaker', 'sentence', 'speaker:sentence' based on the
# two coefficients, one for each HRF in delay.spectral
# We reproduce the same constrasts as in the data base
# outputting an F using both HRFs, as well as the
# t using only the first HRF
for obj1, obj2 in [('face', 'scrambled'), ('house', 'scrambled'),
('chair', 'scrambled'), ('face', 'house')]:
cons['%s_vs_%s_F' % (obj1, obj2)] = \
np.vstack([cons['object_%s_0' % obj1] -
cons['object_%s_0' % obj2],
cons['object_%s_1' % obj1] -
cons['object_%s_1' % obj2]])
cons['%s_vs_%s_t' % (obj1, obj2)] = (cons['object_%s_0' % obj1] -
cons['object_%s_0' % obj2])
#----------------------------------------------------------------------
# Data loading
#----------------------------------------------------------------------
# Load in the fMRI data, saving it as an array. It is transposed to have
# time as the first dimension, i.e. fmri[t] gives the t-th volume.
fmri_im = futil.get_fmri(path_info) # an Image
fmri_im = rollimg(fmri_im, 't')
fmri = fmri_im.get_data() # now, it's an ndarray
nvol, volshape = fmri.shape[0], fmri.shape[1:]
nx, sliceshape = volshape[0], volshape[1:]
#----------------------------------------------------------------------
# Model fit
#----------------------------------------------------------------------
# The model is a two-stage model, the first stage being an OLS (ordinary
# least squares) fit, whose residuals are used to estimate an AR(1)
# parameter for each voxel.
m = OLSModel(X)
ar1 = np.zeros(volshape)
# Fit the model, storing an estimate of an AR(1) parameter at each voxel
for s in range(nx):
d = np.array(fmri[:, s])
flatd = d.reshape((d.shape[0], -1))
result = m.fit(flatd)
ar1[s] = ((result.resid[1:] * result.resid[:-1]).sum(0) /
(result.resid**2).sum(0)).reshape(sliceshape)
# We round ar1 to nearest one-hundredth and group voxels by their rounded
# ar1 value, fitting an AR(1) model to each batch of voxels.
# XXX smooth here?
# ar1 = smooth(ar1, 8.0)
ar1 *= 100
ar1 = ar1.astype(np.int) / 100.
# We split the contrasts into F-tests and t-tests.
# XXX helper function should do this
fcons = {}
tcons = {}
for n, v in cons.items():
v = np.squeeze(v)
if v.ndim == 1:
tcons[n] = v
else:
fcons[n] = v
# Setup a dictionary to hold all the output
# XXX ideally these would be memmap'ed Image instances
output = {}
for n in tcons:
tempdict = {}
for v in ['sd', 't', 'effect']:
tempdict[v] = np.memmap(NamedTemporaryFile(prefix='%s%s.nii' %
(n, v)),
dtype=np.float,
shape=volshape,
mode='w+')
output[n] = tempdict
for n in fcons:
output[n] = np.memmap(NamedTemporaryFile(prefix='%s%s.nii' % (n, v)),
dtype=np.float,
shape=volshape,
mode='w+')
# Loop over the unique values of ar1
for val in np.unique(ar1):
armask = np.equal(ar1, val)
m = ARModel(X, val)
d = fmri[:, armask]
results = m.fit(d)
# Output the results for each contrast
for n in tcons:
resT = results.Tcontrast(tcons[n])
output[n]['sd'][armask] = resT.sd
output[n]['t'][armask] = resT.t
output[n]['effect'][armask] = resT.effect
for n in fcons:
output[n][armask] = results.Fcontrast(fcons[n]).F
# Dump output to disk
odir = futil.output_dir(path_info, tcons, fcons)
# The coordmap for a single volume in the time series
vol0_map = fmri_im[0].coordmap
for n in tcons:
for v in ['t', 'sd', 'effect']:
im = Image(output[n][v], vol0_map)
save_image(im, pjoin(odir, n, '%s.nii' % v))
for n in fcons:
im = Image(output[n], vol0_map)
save_image(im, pjoin(odir, n, "F.nii"))
mappedTrkFile = '/chb/tmp/kihotest_mapped.trk'
# volume
testArr = np.zeros( ( 10, 10, 10 ) )
r = 0
for i in range( testArr.shape[0] ):
for j in range( testArr.shape[1] ):
for k in range( testArr.shape[2] ):
r += 1
testArr[i, j, k] = r
img = image.fromarray( testArr, 'ijk', 'xyz' )
save_image( img, volFile )
# trk file
fibers = []
# 2,5,6
# 3,5,7
# 2,6,7
# 8,7,3
# 9,5,4
points = np.array( [[2, 5, 6], [3, 5, 7], [2, 6, 7], [8, 7, 3], [9, 5, 4]], dtype=np.float32 )
fibers.append( ( points, None, None ) )
io.saveTrk( trkFile, fibers, None, None, True )
img_roi = load_image(roi)
img_roi = Image(img_roi.get_data().astype(np.float64), img_roi.coordmap, img_roi.header)
ind_roi = img_roi.get_data().astype(bool) & img_mask.get_data().astype(bool)
ind_noroi = ~img_roi.get_data().astype(bool) & img_mask.get_data().astype(bool)
roi_data = np.random.randn(np.sum(ind_roi))
for i in range(1, n_subjects+1):
print('subject %02g' % i)
s_dir = os.path.join(root, '%02g' % i)
if not os.path.isdir(s_dir):
os.makedirs(s_dir)
roi_dir = os.path.join(s_dir, 'roi')
if not os.path.isdir(roi_dir):
os.makedirs(roi_dir)
if mode == 'SVR':
img_mask.get_data()[ind_roi] = (i - (i > n_subjects / 2) * n_subjects / 2) * roi_data + 0.4*np.random.randn(roi_data.shape[0])
y = (i - (i > n_subjects / 2) * n_subjects / 2)
else:
img_mask.get_data()[ind_roi] = (1 + (i > n_subjects / 2)) * roi_data + 0.4\
*np.random.randn(roi_data.shape[0])
y = 1 + (i > n_subjects / 2)
img_mask.get_data()[ind_noroi] = np.random.randn(np.sum(ind_noroi))
img_mask = smooth(img_mask, fwhm)
save_image(img_mask, os.path.join(roi_dir, roi_name))
json.dump({'y': y}, open(os.path.join(s_dir, 'y_%s.json' % mode), 'w+'))
def test_nondiag():
gimg.affine[0,1] = 3.0
save_image(gimg, gtmpfile.name)
img2 = load_image(gtmpfile.name)
yield assert_true, np.allclose(img2.affine, gimg.affine)
trkFile = '/chb/tmp/kihotest.trk'
mappedTrkFile = '/chb/tmp/kihotest_mapped.trk'
# volume
testArr = np.zeros((10, 10, 10))
r = 0
for i in range(testArr.shape[0]):
for j in range(testArr.shape[1]):
for k in range(testArr.shape[2]):
r += 1
testArr[i, j, k] = r
img = image.fromarray(testArr, 'ijk', 'xyz')
save_image(img, volFile)
# trk file
fibers = []
# 2,5,6
# 3,5,7
# 2,6,7
# 8,7,3
# 9,5,4
points = np.array([[2, 5, 6], [3, 5, 7], [2, 6, 7], [8, 7, 3], [9, 5, 4]],
dtype=np.float32)
fibers.append((points, None, None))
io.saveTrk(trkFile, fibers, None, None, True)
def space_time_realign(
input, tr, slice_order="descending", slice_dim=2, slice_dir=1, apply=True, make_figure=False, out_name=None
):
"""
This is a scripting interface to `nipy.algorithms.registration.SpaceTimeRealign`
Parameters
----------
input : str or list
A full path to a file-name (4D nifti time-series) , or to a directory
containing 4D nifti time-series, or a list of full-paths to files.
tr : float
The repetition time
slice_order : str (optional)
This is the order of slice-times in the acquisition. This is used as a
key into the ``SLICETIME_FUNCTIONS`` dictionary from
:mod:`nipy.algorithms.slicetiming.timefuncs`. Default: 'descending'.
slice_dim : int (optional)
Denotes the axis in `images` that is the slice axis. In a 4D image,
this will often be axis = 2 (default).
slice_dir : int (optional)
1 if the slices were acquired slice 0 first (default), slice -1 last,
or -1 if acquire slice -1 first, slice 0 last.
apply : bool (optional)
Whether to apply the transformation and produce an output. Default:
True.
make_figure : bool (optional)
Whether to generate a .png figure with the parameters across scans.
out_name : bool (optional)
Specify an output location (full path) for the files that are
generated. Default: generate files in the path of the inputs (with an
`_mc` suffix added to the file-names.
Returns
-------
transforms : ndarray
An (n_times_points,) shaped array containing
`nipy.algorithms.registration.affine.Rigid` class instances for each time
point in the time-series. These can be used as affine transforms by
referring to their `.as_affine` attribute.
"""
if make_figure:
if not HAVE_MPL:
e_s = "You need to have matplotlib installed to run this function"
e_s += " with `make_figure` set to `True`"
raise RuntimeError(e_s)
# If we got only a single file, we motion correct that one:
if op.isfile(input):
if not (input.endswith(".nii") or input.endswith(".nii.gz")):
e_s = "Input needs to be a nifti file ('.nii' or '.nii.gz'"
raise ValueError(e_s)
fnames = [input]
input = nib.load(input)
# If this is a full-path to a directory containing files, it's still a
# string:
elif isinstance(input, str):
list_of_files = os.listdir(input)
fnames = [op.join(input, f) for f in np.sort(list_of_files) if (f.endswith(".nii") or f.endswith(".nii.gz"))]
input = [nib.load(x) for x in fnames]
# Assume that it's a list of full-paths to files:
else:
input = [nib.load(x) for x in input]
slice_times = timefuncs[slice_order]
slice_info = [slice_dim, slice_dir]
reggy = SpaceTimeRealign(input, tr, slice_times, slice_info)
reggy.estimate(align_runs=True)
# We now have the transformation parameters in here:
transforms = np.squeeze(np.array(reggy._transforms))
rot = np.array([t.rotation for t in transforms])
trans = np.array([t.translation for t in transforms])
if apply:
new_reggy = reggy.resample(align_runs=True)
for run_idx, new_im in enumerate(new_reggy):
# Fix output TR - it was probably lost in the image realign step
assert new_im.affine.shape == (5, 5)
new_im.affine[:] = new_im.affine.dot(np.diag([1, 1, 1, tr, 1]))
# Save it out to a '.nii.gz' file:
froot, ext, trail_ext = splitext_addext(fnames[run_idx])
path, fname = op.split(froot)
# We retain the file-name adding '_mc' regardless of where it's
# saved
new_path = path if out_name is None else out_name
save_image(new_im, op.join(new_path, fname + "_mc.nii.gz"))
if make_figure:
# Delay MPL plotting import to latest moment to avoid errors trying
# import the default MPL backend (such as tkinter, which may not be
# installed). See: https://github.com/nipy/nipy/issues/414
import matplotlib.pyplot as plt
figure, ax = plt.subplots(2)
figure.set_size_inches([8, 6])
ax[0].plot(rot)
ax[0].set_xlabel("Time (TR)")
ax[0].set_ylabel("Translation (mm)")
ax[1].plot(trans)
ax[1].set_xlabel("Time (TR)")
ax[1].set_ylabel("Rotation (radians)")
figure.savefig(op.join(os.path.split(fnames[0])[0], "mc_params.png"))
return transforms
def space_time_realign(input,
tr,
slice_order='descending',
slice_dim=2,
slice_dir=1,
apply=True,
make_figure=False,
out_name=None):
"""
This is a scripting interface to `nipy.algorithms.registration.SpaceTimeRealign`
Parameters
----------
input : str or list
A full path to a file-name (4D nifti time-series) , or to a directory
containing 4D nifti time-series, or a list of full-paths to files.
tr : float
The repetition time
slice_order : str (optional)
This is the order of slice-times in the acquisition. This is used as a
key into the ``SLICETIME_FUNCTIONS`` dictionary from
:mod:`nipy.algorithms.slicetiming.timefuncs`. Default: 'descending'.
slice_dim : int (optional)
Denotes the axis in `images` that is the slice axis. In a 4D image,
this will often be axis = 2 (default).
slice_dir : int (optional)
1 if the slices were acquired slice 0 first (default), slice -1 last,
or -1 if acquire slice -1 first, slice 0 last.
apply : bool (optional)
Whether to apply the transformation and produce an output. Default:
True.
make_figure : bool (optional)
Whether to generate a .png figure with the parameters across scans.
out_name : bool (optional)
Specify an output location (full path) for the files that are
generated. Default: generate files in the path of the inputs (with an
`_mc` suffix added to the file-names.
Returns
-------
transforms : ndarray
An (n_times_points,) shaped array containing
`nipy.algorithms.registration.affine.Rigid` class instances for each time
point in the time-series. These can be used as affine transforms by
referring to their `.as_affine` attribute.
"""
if make_figure:
if not HAVE_MPL:
e_s = "You need to have matplotlib installed to run this function"
e_s += " with `make_figure` set to `True`"
raise RuntimeError(e_s)
# If we got only a single file, we motion correct that one:
if op.isfile(input):
if not (input.endswith('.nii') or input.endswith('.nii.gz')):
e_s = "Input needs to be a nifti file ('.nii' or '.nii.gz'"
raise ValueError(e_s)
fnames = [input]
input = nib.load(input)
# If this is a full-path to a directory containing files, it's still a
# string:
elif isinstance(input, str):
list_of_files = os.listdir(input)
fnames = [
op.join(input, f) for f in np.sort(list_of_files)
if (f.endswith('.nii') or f.endswith('.nii.gz'))
]
input = [nib.load(x) for x in fnames]
# Assume that it's a list of full-paths to files:
else:
input = [nib.load(x) for x in input]
slice_times = timefuncs[slice_order]
slice_info = [slice_dim, slice_dir]
reggy = SpaceTimeRealign(input, tr, slice_times, slice_info)
reggy.estimate(align_runs=True)
# We now have the transformation parameters in here:
transforms = np.squeeze(np.array(reggy._transforms))
rot = np.array([t.rotation for t in transforms])
trans = np.array([t.translation for t in transforms])
if apply:
new_reggy = reggy.resample(align_runs=True)
for run_idx, new_im in enumerate(new_reggy):
# Fix output TR - it was probably lost in the image realign step
assert new_im.affine.shape == (5, 5)
new_im.affine[:] = new_im.affine.dot(np.diag([1, 1, 1, tr, 1]))
# Save it out to a '.nii.gz' file:
froot, ext, trail_ext = splitext_addext(fnames[run_idx])
path, fname = op.split(froot)
# We retain the file-name adding '_mc' regardless of where it's
# saved
new_path = path if out_name is None else out_name
save_image(new_im, op.join(new_path, fname + '_mc.nii.gz'))
if make_figure:
figure, ax = plt.subplots(2)
figure.set_size_inches([8, 6])
ax[0].plot(rot)
ax[0].set_xlabel('Time (TR)')
ax[0].set_ylabel('Translation (mm)')
ax[1].plot(trans)
ax[1].set_xlabel('Time (TR)')
ax[1].set_ylabel('Rotation (radians)')
figure.savefig(op.join(os.path.split(fnames[0])[0], 'mc_params.png'))
return transforms
def create(rootdir):
out = None
prev = None
out_is_there = False
dirs = sorted(os.listdir(rootdir))
for d in dirs:
files = os.listdir(os.path.join(rootdir, d))
for f in files:
input_image = tif.imread(os.path.join(rootdir, d, f))
# print input_image
# print type(input_image)
# print 'ccc',input_image.flatten()
if out_is_there:
#out = np.concatenate([out, input_image.flatten()])
out = np.dstack([out, input_image])
else:
# out = input_image.flatten()
out = input_image
out_is_there = True
# return
# i = 0
# for root, dirs, files in os.walk(rootdir):
# fullpaths = [(os.path.join(root, name)) for name in files]
# for f in fullpaths:
# print f
# input_image = tif.imread(f)
# # print input_image
# # print type(input_image)
# # print 'ccc',input_image.flatten()
# if out_is_there:
# #out = np.concatenate([out, input_image.flatten()])
# out = np.dstack([out, input_image])
# else:
# # out = input_image.flatten()
# out = input_image
# out_is_there = True
#>>> from nipy.io.api import save_image
#>>> data = np.zeros((91,109,91), dtype=np.uint8)
#>>> cmap = AffineTransform('kji', 'zxy', np.eye(4))
#>>> img = Image(data, cmap)
#>>> fname1 = os.path.join(tmpdir, 'img1.nii.gz')
#>>> saved_img1 = save_image(img, fname1)
# image_data = PILImage.open(os.path.join(subdir,file))
# if image_data.mode != "RGB":
# image_data = image_data.convert("RGB")
# print image_data.toString()
# break
# shutil.copy(os.path.join(subdir, file), '/tmp/'+str(i)+'.tif')
# i+=1
length = out.shape[0]
print 'aaa'
print out
# out = out.reshape((512, 512, length/512/512))
print out
cmap = AffineTransform('kji', 'zxy', np.eye(4))
img = Image(out, cmap)
save_image(img, '/tmp/out.nii.gz')
def run_model(subj, run):
"""
Single subject fitting of FIAC model
"""
#----------------------------------------------------------------------
# Set initial parameters of the FIAC dataset
#----------------------------------------------------------------------
# Number of volumes in the fMRI data
nvol = 121
# The TR of the experiment
TR = 2.5
# The time of the first volume
Tstart = 0.0
# The array of times corresponding to each volume in the fMRI data
volume_times = np.arange(nvol) * TR + Tstart
# This recarray of times has one column named 't'. It is used in the
# function design.event_design to create the design matrices.
volume_times_rec = make_recarray(volume_times, 't')
# Get a path description dictionary that contains all the path data relevant
# to this subject/run
path_info = futil.path_info_run(subj,run)
#----------------------------------------------------------------------
# Experimental design
#----------------------------------------------------------------------
# Load the experimental description from disk. We have utilities in futil
# that reformat the original FIAC-supplied format into something where the
# factorial structure of the design is more explicit. This has already
# been run once, and get_experiment_initial() will simply load the
# newly-formatted design description files (.csv) into record arrays.
experiment = futil.get_experiment(path_info)
# Create design matrices for the "initial" and "experiment" factors, saving
# the default contrasts.
# The function event_design will create design matrices, which in the case
# of "experiment" will have num_columns = (# levels of speaker) * (# levels
# of sentence) * len(delay.spectral) = 2 * 2 * 2 = 8. For "initial", there
# will be (# levels of initial) * len([hrf.glover]) = 1 * 1 = 1.
# Here, delay.spectral is a sequence of 2 symbolic HRFs that are described
# in:
#
# Liao, C.H., Worsley, K.J., Poline, J-B., Aston, J.A.D., Duncan, G.H.,
# Evans, A.C. (2002). \'Estimating the delay of the response in fMRI
# data.\' NeuroImage, 16:593-606.
# The contrast definitions in ``cons_exper`` are a dictionary with keys
# ['constant_0', 'constant_1', 'speaker_0', 'speaker_1', 'sentence_0',
# 'sentence_1', 'sentence:speaker_0', 'sentence:speaker_1'] representing the
# four default contrasts: constant, main effects + interactions, each
# convolved with 2 HRFs in delay.spectral. For example, sentence:speaker_0
# is the interaction of sentence and speaker convolved with the first (=0)
# of the two HRF basis functions, and sentence:speaker_1 is the interaction
# convolved with the second (=1) of the basis functions.
# XXX use the hrf __repr__ for naming contrasts
X_exper, cons_exper = design.block_design(experiment, volume_times_rec,
hrfs=delay.spectral,
level_contrasts=True)
# In addition to factors, there is typically a "drift" term. In this case,
# the drift is a natural cubic spline with a not at the midpoint
# (volume_times.mean())
vt = volume_times # shorthand
drift = np.array( [vt**i for i in range(4)] +
[(vt-vt.mean())**3 * (np.greater(vt, vt.mean()))] )
for i in range(drift.shape[0]):
drift[i] /= drift[i].max()
# We transpose the drift so that its shape is (nvol,5) so that it will have
# the same number of rows as X_exper.
drift = drift.T
# There are helper functions to create these drifts: design.fourier_basis,
# design.natural_spline. Therefore, the above is equivalent (except for
# the normalization by max for numerical stability) to
#
# >>> drift = design.natural_spline(t, [volume_times.mean()])
# Stack all the designs, keeping the new contrasts which has the same keys
# as cons_exper, but its values are arrays with 15 columns, with the
# non-zero entries matching the columns of X corresponding to X_exper
X, cons = design.stack_designs((X_exper, cons_exper),
(drift, {}))
# Sanity check: delete any non-estimable contrasts
for k in cons.keys():
if not isestimable(cons[k], X):
del(cons[k])
warnings.warn("contrast %s not estimable for this run" % k)
# The default contrasts are all t-statistics. We may want to output
# F-statistics for 'speaker', 'sentence', 'speaker:sentence' based on the
# two coefficients, one for each HRF in delay.spectral
# We reproduce the same constrasts as in the data base
# outputting an F using both HRFs, as well as the
# t using only the first HRF
for obj1, obj2 in [('face', 'scrambled'),
('house', 'scrambled'),
('chair', 'scrambled'),
('face', 'house')]:
cons['%s_vs_%s_F' % (obj1, obj2)] = \
np.vstack([cons['object_%s_0' % obj1] -
cons['object_%s_0' % obj2],
cons['object_%s_1' % obj1] -
cons['object_%s_1' % obj2]])
cons['%s_vs_%s_t' % (obj1, obj2)] = (cons['object_%s_0' % obj1] -
cons['object_%s_0' % obj2])
#----------------------------------------------------------------------
# Data loading
#----------------------------------------------------------------------
# Load in the fMRI data, saving it as an array. It is transposed to have
# time as the first dimension, i.e. fmri[t] gives the t-th volume.
fmri_im = futil.get_fmri(path_info) # an Image
fmri_im = rollimg(fmri_im, 't')
fmri = fmri_im.get_data() # now, it's an ndarray
nvol, volshape = fmri.shape[0], fmri.shape[1:]
nx, sliceshape = volshape[0], volshape[1:]
#----------------------------------------------------------------------
# Model fit
#----------------------------------------------------------------------
# The model is a two-stage model, the first stage being an OLS (ordinary
# least squares) fit, whose residuals are used to estimate an AR(1)
# parameter for each voxel.
m = OLSModel(X)
ar1 = np.zeros(volshape)
# Fit the model, storing an estimate of an AR(1) parameter at each voxel
for s in range(nx):
d = np.array(fmri[:,s])
flatd = d.reshape((d.shape[0], -1))
result = m.fit(flatd)
ar1[s] = ((result.resid[1:] * result.resid[:-1]).sum(0) /
(result.resid**2).sum(0)).reshape(sliceshape)
# We round ar1 to nearest one-hundredth and group voxels by their rounded
# ar1 value, fitting an AR(1) model to each batch of voxels.
# XXX smooth here?
# ar1 = smooth(ar1, 8.0)
ar1 *= 100
ar1 = ar1.astype(np.int) / 100.
# We split the contrasts into F-tests and t-tests.
# XXX helper function should do this
fcons = {}; tcons = {}
for n, v in cons.items():
v = np.squeeze(v)
if v.ndim == 1:
tcons[n] = v
else:
fcons[n] = v
# Setup a dictionary to hold all the output
# XXX ideally these would be memmap'ed Image instances
output = {}
for n in tcons:
tempdict = {}
for v in ['sd', 't', 'effect']:
tempdict[v] = np.memmap(NamedTemporaryFile(prefix='%s%s.nii'
% (n,v)), dtype=np.float,
shape=volshape, mode='w+')
output[n] = tempdict
for n in fcons:
output[n] = np.memmap(NamedTemporaryFile(prefix='%s%s.nii'
% (n,v)), dtype=np.float,
shape=volshape, mode='w+')
# Loop over the unique values of ar1
for val in np.unique(ar1):
armask = np.equal(ar1, val)
m = ARModel(X, val)
d = fmri[:,armask]
results = m.fit(d)
# Output the results for each contrast
for n in tcons:
resT = results.Tcontrast(tcons[n])
output[n]['sd'][armask] = resT.sd
output[n]['t'][armask] = resT.t
output[n]['effect'][armask] = resT.effect
for n in fcons:
output[n][armask] = results.Fcontrast(fcons[n]).F
# Dump output to disk
odir = futil.output_dir(path_info,tcons,fcons)
# The coordmap for a single volume in the time series
vol0_map = fmri_im[0].coordmap
for n in tcons:
for v in ['t', 'sd', 'effect']:
im = Image(output[n][v], vol0_map)
save_image(im, pjoin(odir, n, '%s.nii' % v))
for n in fcons:
im = Image(output[n], vol0_map)
save_image(im, pjoin(odir, n, "F.nii"))
ind_roi = img_roi.get_data().astype(bool) & img_mask.get_data().astype(bool)
ind_noroi = ~img_roi.get_data().astype(bool) & img_mask.get_data().astype(bool)
roi_data = np.random.randn(np.sum(ind_roi))
for i in range(1, n_subjects + 1):
print('subject %02g' % i)
s_dir = os.path.join(root, '%02g' % i)
if not os.path.isdir(s_dir):
os.makedirs(s_dir)
roi_dir = os.path.join(s_dir, 'roi')
if not os.path.isdir(roi_dir):
os.makedirs(roi_dir)
if mode == 'SVR':
img_mask.get_data(
)[ind_roi] = (i - (i > n_subjects / 2) * n_subjects /
2) * roi_data + 0.4 * np.random.randn(roi_data.shape[0])
y = (i - (i > n_subjects / 2) * n_subjects / 2)
else:
img_mask.get_data()[ind_roi] = (1 + (i > n_subjects / 2)) * roi_data + 0.4\
*np.random.randn(roi_data.shape[0])
y = 1 + (i > n_subjects / 2)
img_mask.get_data()[ind_noroi] = np.random.randn(np.sum(ind_noroi))
img_mask = smooth(img_mask, fwhm)
save_image(img_mask, os.path.join(roi_dir, roi_name))
json.dump({'y': y}, open(os.path.join(s_dir, 'y_%s.json' % mode), 'w+'))
