sessions, fmri_series)):
# create design matrices
reg = all_reg[2 * sess: 2 * sess + 2] # fixme
design_matrix = make_dmtx(
frametimes, add_regs=reg_matrix, add_reg_names=reg,
drift_model=drift_model, hfcut=hfcut)
# plot the design matrix
ax = design_matrix.show()
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
pylab.savefig(os.path.join(write_dir, 'design_matrix_%s.png') %\
session)
# get the data
if side == False:
Y, _ = data_scaling(load(fmri_data).get_data()[mask_array].T)
affine = load(fmri_data).get_affine()
else:
Y = np.array([x.data for x in read(fmri_data).darrays])
Y, _ = data_scaling(Y)
if sess == 0: # do not redefine the mask later !
mask_array = np.var(Y, 0) > 0
Y = Y[:, mask_array]
# fit the glm
print 'Fitting a GLM'
result = GeneralLinearModel(design_matrix.matrix)
result.fit(Y, model='ar1', steps=100)
for contrast_id, contrast_val in contrasts.iteritems():
if (contrast_val[session] == 0).all():
contrasts["V-H"] = contrasts["damier_V"] - contrasts["damier_H"]
contrasts["left-right"] = contrasts["left"] - contrasts["right"]
contrasts["right-left"] = contrasts["right"] - contrasts["left"]
contrasts["audio-video"] = contrasts["audio"] - contrasts["video"]
contrasts["video-audio"] = contrasts["video"] - contrasts["audio"]
contrasts["computation-sentences"] = contrasts["computation"] - contrasts["sentences"]
contrasts["reading-visual"] = contrasts["sentences"] * 2 - contrasts["damier_H"] - contrasts["damier_V"]
contrasts["effects_of_interest"] = np.eye(25)[::2]
########################################
# Perform a GLM analysis
########################################
print "Fitting a GLM (this takes time)..."
fmri_image = load(data_path)
Y, _ = data_scaling(fmri_image.get_data()[mask_array])
X = design_matrix.matrix
results = GeneralLinearModel(X)
results.fit(Y.T, steps=100)
affine = fmri_image.get_affine()
#########################################
# Estimate the contrasts
#########################################
print "Computing contrasts..."
for index, (contrast_id, contrast_val) in enumerate(contrasts.iteritems()):
print " Contrast % 2i out of %i: %s" % (index + 1, len(contrasts), contrast_id)
contrast_path = op.join(write_dir, "%s_z_map.nii" % contrast_id)
write_array = mask_array.astype(np.float)
write_array[mask_array] = results.contrast(contrast_val).z_score()
# Get multi-run fMRI data
print ("Loading fmri data...")
Y = [load(f) for f in fmri_files]
# Get mask image
print ("Loading mask...")
mask = load(mask_file)
mask_array = mask.get_data() > 0
# GLM fitting
print ("Starting fit...")
results = []
for x, y in zip(X, Y):
# normalize the data to report effects in percent of the baseline
data = y.get_data()[mask_array].T
data, mean = data_scaling(data)
# fit the glm
model = GeneralLinearModel(x)
model.fit(data, "ar1")
results.append(model)
# make a mean volume for display
wmean = mask_array.astype(np.int16)
wmean[mask_array] = mean
def make_fiac_contrasts():
"""Specify some constrasts for the FIAC experiment"""
con = {}
# the design matrices of both runs comprise 13 columns
# the first 5 columns of the design matrices correpond to the following
design_matrix = dmtx.matrix
# Plot the design matrix
if plot_design_matrix:
fig1 = mp.figure(figsize=(10, 6))
dmtx.show()
mp.title(desc)
fig1_file = os.path.join(out_path, label + 'design_matrix_test' + \
str(ntest) + '.png')
mp.savefig(fig1_file)
#-----------------------------------------------------------------
# Mean-scale, de-mean and multiply data by 100
#-----------------------------------------------------------------
mask = np.sum(img.get_data(), axis=-1) > 0
data, mean = data_scaling(img.get_data()[mask].T)
if np.size(data):
mean.shape = mask.shape
#-----------------------------------------------------------------
# Apply a general linear model to all pixels
#-----------------------------------------------------------------
print(' Apply general linear model...')
model = "ar1"
glm = GeneralLinearModel(design_matrix)
glm.fit(data, model=model)
#-----------------------------------------------------------------
# Create a contrast image
#
# Contrast condition 1 vs. condition 2, holding condition 3 constant
sessions, fmri_series)):
# create design matrices
reg = all_reg[4 * sess: 4 * sess + 4] # fixme
design_matrix = make_dmtx(
frametimes, add_regs=reg_matrix, add_reg_names=reg,
drift_model=drift_model, hfcut=hf_cut)
# plot the design matrix
ax = design_matrix.show()
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
pylab.savefig(os.path.join(write_dir, 'design_matrix_%s.png') %\
session)
# get the data
if side == False:
Y, _ = data_scaling(np.array([load(f).get_data()[mask_array]
for f in fmri_data]))
affine = load(fmri_data[0]).get_affine()
else:
Y, _ = data_scaling(np.array(
[load_texture(f) for f in fmri_data]))
mask_array = np.var(Y, 0) > 0
Y = Y[:, mask_array]
# fit the glm
print 'Fitting a GLM (this takes time)...'
result = GeneralLinearModel(design_matrix.matrix)
result.fit(Y, model='ar1', steps=100)
for contrast_id, contrast_val in contrasts.iteritems():
if (contrast_val[session] == 0).all():
continue
# Get multi-session fMRI data
print('Loading fmri data...')
Y = [load_image(f) for f in fmri_files]
# Get mask image
print('Loading mask...')
mask = load_image(mask_file)
mask_array, affine = mask.get_data() > 0, mask.get_affine()
# GLM fitting
print('Starting fit...')
glms = []
for x, y in zip(X, Y):
glm = GeneralLinearModel(x)
data, mean = data_scaling(y.get_data()[mask_array].T)
glm.fit(data, 'ar1')
glms.append(glm)
# Compute the required contrast
print('Computing test contrast image...')
nregressors = X[0].shape[1]
## should check that all design matrices have the same
c = np.zeros(nregressors)
c[0:4] = cvect
z_vals = (glms[0].contrast(c) + glms[1].contrast(c)).z_score()
# Show Zmap image
z_map = mask_array.astype(np.float)
z_map[mask_array] = z_vals
mean_map = mask_array.astype(np.float)
# Get multi-session fMRI data
print('Loading fmri data...')
Y = [load_image(f) for f in fmri_files]
# Get mask image
print('Loading mask...')
mask = load_image(mask_file)
mask_array, affine = mask.get_data() > 0, mask.get_affine()
# GLM fitting
print('Starting fit...')
glms = []
for x, y in zip(X, Y):
glm = GeneralLinearModel(x)
data, mean = data_scaling(y.get_data()[mask_array].T)
glm.fit(data, 'ar1')
glms.append(glm)
# Compute the required contrast
print('Computing test contrast image...')
nregressors = X[0].shape[1]
## should check that all design matrices have the same
c = np.zeros(nregressors)
c[0:4] = cvect
z_vals = (glms[0].contrast(c) + glms[1].contrast(c)).z_score()
# Show Zmap image
z_map = mask_array.astype(np.float)
z_map[mask_array] = z_vals
mean_map = mask_array.astype(np.float)
reg = all_reg[2 * sess:2 * sess + 2] # fixme
design_matrix = make_dmtx(frametimes,
add_regs=reg_matrix,
add_reg_names=reg,
drift_model=drift_model,
hfcut=hfcut)
# plot the design matrix
ax = design_matrix.show()
ax.set_position([.05, .25, .9, .65])
ax.set_title('Design matrix')
pylab.savefig(os.path.join(write_dir, 'design_matrix_%s.png') %\
session)
# get the data
if side == False:
Y, _ = data_scaling(load(fmri_data).get_data()[mask_array].T)
affine = load(fmri_data).get_affine()
else:
Y = np.array([x.data for x in read(fmri_data).darrays])
Y, _ = data_scaling(Y)
if sess == 0: # do not redefine the mask later !
mask_array = np.var(Y, 0) > 0
Y = Y[:, mask_array]
# fit the glm
print 'Fitting a GLM (this takes time)...'
result = GeneralLinearModel(design_matrix.matrix)
result.fit(Y, model='ar1', steps=100)
for contrast_id, contrast_val in contrasts.iteritems():
if (contrast_val[session] == 0).all():
# Get multi-run fMRI data
print('Loading fmri data...')
Y = [load(f) for f in fmri_files]
# Get mask image
print('Loading mask...')
mask = load(mask_file)
mask_array = mask.get_data() > 0
# GLM fitting
print('Starting fit...')
results = []
for x, y in zip(X, Y):
# normalize the data to report effects in percent of the baseline
data = y.get_data()[mask_array].T
data, mean = data_scaling(data)
# fit the glm
model = GeneralLinearModel(x)
model.fit(data, 'ar1')
results.append(model)
# make a mean volume for display
wmean = mask_array.astype(np.int16)
wmean[mask_array] = mean
def make_fiac_contrasts():
"""Specify some contrasts for the FIAC experiment"""
con = {}
# the design matrices of both runs comprise 13 columns
# the first 5 columns of the design matrices correspond to the following
