def _run_interface(self, runtime):
beta_images = [nb.load(beta) for beta in self.inputs.betas]
residuals = [nb.load(r).get_data() for r in self.inputs.residuals]
design_matrices = [h5load(dm) for dm in self.inputs.design_matrices]
mean_beta = np.mean([beta.get_data() for beta in beta_images], 0)
nb.save(
nb.Nifti1Image(mean_beta, beta_images[0].get_affine(), beta_images[0].get_header()),
"sandwiched_beta.nii.gz",
)
V = np.zeros(mean_beta.shape + (mean_beta.shape[-1],))
W = np.sum([bottleneck.ss(r, -1) for r in residuals], 0) / (len(residuals) - 1)
for X, resid in zip(design_matrices, residuals):
# W = resid.T.dot(resid)
X_T_inv = np.linalg.pinv(np.dot(X.T, X))
top_sandwich = np.outer(np.dot(X_T_inv, X.T), W).T.reshape((np.prod(W.shape), X_T_inv.shape[1], X.shape[0]))
sandwich = np.dot(top_sandwich, np.dot(X, X_T_inv))
V = V + sandwich.reshape(V.shape)
V = V / len(design_matrices)
nb.save(
nb.Nifti1Image(V, beta_images[0].get_affine(), beta_images[0].get_header()), "sandwiched_variance.nii.gz"
)
return runtime
def _run_interface(self, runtime):
image = nb.load(self.inputs.data)
data = image.get_data()
design_matrix = h5load(self.inputs.design_matrix)
X = design_matrix
print X.shape, data.shape
X_T_inv = np.linalg.pinv(np.dot(X.T, X))
calc_beta = np.dot(X_T_inv, X.T)
beta = np.dot(calc_beta, data.reshape(np.prod(data.shape[:-1]), data.shape[-1]).T)
predicted = np.dot(X, beta)
predicted = predicted.T.reshape((data.shape[:-1] + (X.shape[0],)))
beta = beta.T.reshape(data.shape[:-1] + (X.shape[1],))
resid = data - predicted
ss = bottleneck.ss(resid, -1)
# ss = resid.T.dot(resid)
ols_var = np.outer(X_T_inv, ss)
ols_var = ols_var.T.reshape(data.shape[:-1] + (ols_var.shape[0],))
nb.save(nb.Nifti1Image(beta, image.get_affine(), image.get_header()), os.path.abspath("betas.nii.gz"))
nb.save(nb.Nifti1Image(ols_var, image.get_affine(), image.get_header()), os.path.abspath("variances.nii.gz"))
nb.save(nb.Nifti1Image(resid, image.get_affine(), image.get_header()), os.path.abspath("residuals.nii.gz"))
return runtime
def load_create_save_ds(ds_save_p, dataset_list, ref_space, warp_files, mask, **kwargs):
detrending = kwargs.get('detrending', True)
use_zscore = kwargs.get('use_zscore', True)
use_events = kwargs.get('use_events', False)
anno_dir = kwargs.get('anno_dir', None)
use_glm_estimates = kwargs.get('use_glm_estimates', False)
targets = kwargs.get('targets', None)
event_offset = kwargs.get('event_offset', None)
event_dur = kwargs.get('event_dur', None)
save_disc_space = kwargs.get('save_disc_space', True)
rois = kwargs.get('rois', None)
if ds_save_p.exists():
ds = mvpa.h5load(str(ds_save_p))
else:
ds = preprocess_datasets(dataset_list, ref_space, warp_files, mask, detrending=detrending,
use_zscore=use_zscore, use_events=use_events, anno_dir=anno_dir,
use_glm_estimates=use_glm_estimates, targets=targets,
event_offset=event_offset, event_dur=event_dur, rois=rois,
save_disc_space=save_disc_space)
mvpa.h5save(str(ds_save_p), ds) # , compression=9
return ds
for i in range(len(cam_list)):
this = cam_list[i]
cam_list[i] = np.concatenate(
(this[3:, :], this[2:-1, :], this[1:-2, :], this[:-3, :]), axis=1)
train_stim = np.concatenate((cam_list[0], cam_list[1], cam_list[3]),
axis=0)
test_stim = cam_list[2]
print(train_stim.shape, test_stim.shape)
for hemi in hemispheres:
# print('\nLoading hyperaligned mappers...')
mappers = mv.h5load(
os.path.join(
mvpa_dir,
'search_hyper_mappers_life_mask_nofsel_{0}.hdf5'.format(hemi)))
all_corrs = []
for test_p in participants:
train_p = [x for x in participants if x != test_p]
print(
'\nLoading fMRI GIFTI data and using {0} as test participant...'
.format(test_p))
train_resp = []
for run in [1, 2, 4]:
avg = []
for participant in train_p:
if run == 4:
resp = mappers[participant].forward(
load_data(
os.path.join(
help="If True, the results of the "
"glm will be plotted as a timeseries per run.",
default=False)
parser.add_argument(
'-ar',
'--include_all_regressors',
help="If you are plotting the "
"time series, do you want the plot to contain all of the"
" regressors?",
default=False)
args = parser.parse_args()
# get the data
ds_file = args.inputfile
ds = mv.h5load(ds_file)
# are there glm inputs?
if args.eventdir:
eventdir = args.eventdir
if args.annotation:
annot_dir = args.annotation
results_dir = '/' + args.output + '/'
# create the output dir if it doesn't exist
if not os.path.isdir(results_dir):
os.makedirs(results_dir)
ds_type = args.dataset
glm = args.glm
bilateral = args.bilateral
'9': 'sid000009',
'10': 'sid000012',
'11': 'sid000034',
'12': 'sid000007'
}
n_conditions = 90
n_vertices = 40962
base_dir = '/home/nastase/social_actions'
scripts_dir = join(base_dir, 'scripts')
data_dir = join(base_dir, 'fmri', '1021_actions', 'derivatives')
suma_dir = join(data_dir, 'freesurfer', 'fsaverage6', 'SUMA')
mvpa_dir = join(data_dir, 'pymvpa')
condition_order = mv.h5load(join(scripts_dir, 'condition_order.hdf5'))
reorder = condition_order['reorder']
sparse_ordered_labels = condition_order['sparse_ordered_labels']
# Load in searchlight RDMs
sl_rdms = {}
for hemi in ['lh', 'rh']:
sl_rdms[hemi] = {}
for participant in participants.keys():
sl_result = mv.h5load(
join(
mvpa_dir, 'no_roi_ids',
'search_RDMs_sq_zscore_p{0}_{1}.hdf5'.format(
participant, hemi)))
sl_sq = sl_result.samples.reshape(n_conditions, n_conditions,
n_vertices)
should be done on the full dataset or on the dataset \
with only ROIs: 'full' or 'stripped' (default: stripped)",
type=str, default='stripped')
parser.add_argument('-o', '--output', help="Please specify an output directory"
"name (absolute path) to store the analysis results", type=str)
parser.add_argument('--classifier', help="Which classifier do you want to use? Options:"
"linear Gaussian Naive Bayes ('gnb'), linear (binary) stochastic "
"gradient descent (l-sgd)",
type=str, required=True)
args = parser.parse_args()
# get the data
ds_movie_path = args.inputfile1
ds_loc_path = args.inputfile2
ds_movie = mv.h5load(ds_movie_path)
ds_loc = mv.h5load(ds_loc_path)
# prepare the output path
results_dir = '/' + args.output + '/'
# create the output dir if it doesn't exist
if not os.path.isdir(results_dir):
os.makedirs(results_dir)
# get more information about what is being calculated
ds_type = args.dataset # stripped --> no brain, no overlap,
if args.bilateral == 'True' or args.bilateral == True:
bilateral = True
elif args.bilateral == 'False' or args.bilateral == False:
bilateral = False # True or False
samples_size = 12 #Length of segments in sec
if align == 'nonlinear':
maskfile = os.path.join(datapath, 'templates', 'grpbold7Tad', 'qa',
'dico7Tad2grpbold7Tad_nl',
'brain_mask_intersection.nii.gz')
elif align == 'linear':
maskfile = os.path.join(datapath, 'templates', 'grpbold7Tad', 'qa',
'dico7Tad2grpbold7Tad7Tad',
'brain_mask_intersection.nii.gz')
ds = mvpa.fmri_dataset(maskfile, mask=maskfile)
dsfile = '_z' + str(zsc) + '_' + str(samples_size) + '_' + align
#Load dataset of two subjects and reorganise for univariate analysis
evds1 = mvpa.h5load(os.path.join('dataset', subj1 + dsfile + '.hdf5'))
evds1 = evds1.mapper.reverse(evds1)
evds2 = mvpa.h5load(os.path.join('dataset', subj2 + dsfile + '.hdf5'))
evds2 = evds1.mapper.reverse(evds2)
evds = mvpa.vstack([evds1, evds2])
del evds1, evds2
# Prepare inter-subject correlation measure
class Corr(mvpa.Measure):
is_trained = True
def __init__(self, subj1, subj2, **kwargs):
mvpa.Measure.__init__(self, **kwargs)
self._subj1 = subj1
self._subj2 = subj2
else:
train_stim, test_stim = get_narrative_stim_for_fold(
'{0}_{1}'.format(model, stimfile), fold_shifted, included)
for test_p in participants:
if align == 'ws':
train_resp, test_resp = get_ws_data(test_p, fold_shifted, included,
hemi)
elif align == 'aa':
train_resp, test_resp = get_aa_data(test_p, fold_shifted, included,
hemi)
else:
print('\nLoading hyperaligned mappers...')
mappers = mv.h5load(
os.path.join(
mvpa_dir,
'search_hyper_mappers_life_mask_nofsel_{0}_leftout_{1}.hdf5'.
format(hemi, fold_shifted)))
if align == 'ha_common':
train_resp, test_resp = get_ha_common_data(test_p, mappers,
fold_shifted, included,
hemi)
elif align == 'ha_testsubj':
train_resp, test_resp = get_ha_testsubj_data(
test_p, mappers, fold_shifted, included, hemi)
alphas = np.logspace(0, 3, 20)
nboots = len(included)
chunklen = 15
nchunks = 15
from scipy.io import loadmat
from sklearn.linear_model import RidgeCV,BayesianRidge
from sklearn.preprocessing import StandardScaler
from sklearn.cross_decomposition import PLSRegression
from pandas import read_csv
from sklearn.externals import joblib
from encoding_helpers import *
from itertools import combinations
T3 = False
for subj in xrange(12,13):
subj_preprocessed_path = os.path.join('/home','mboos','SpeechEncoding','PreProcessed','subj%02dnpp.gzipped.hdf5' % subj)
s1ds = mvpa.h5load(subj_preprocessed_path)
events = mvpa.find_events(targets=s1ds.sa.targets,chunks=s1ds.sa.chunks)
rvstr_TS = rolling_window(s1ds.sa['targets'][::-1].copy(),4)
s1ds.sa['targets'].value[(np.where(np.apply_along_axis(lambda x : len(np.unique(x)) == 1 and x[0] != 'rest',1,rvstr_TS)[::-1])[0]+3)] = 'rest'
labelsTS = s1ds.sa['targets'].value.copy()
# <codecell>
#unroll audio features
#cut last 500ms
featureTS = np.zeros((labelsTS.shape[0],20*ft_freq))
featureTS[labelsTS!='rest',:] = np.reshape(np.vstack([feature_dict[ev['targets']][:60,:] for ev in events if ev['targets']!='rest']),(-1,ft_freq*20))
# <codecell>
datapath = os.path.join('/home','data','exppsy','forrest_gump','openfmri.org')
##Parameter
zsc = 1 #Voxelwise zscoring
samples_size = 12 #Length of segments in sec
if align=='nonlinear':
maskfile = os.path.join(datapath,'templates', 'grpbold7Tad','qa', 'dico7Tad2grpbold7Tad_nl','brain_mask_intersection.nii.gz')
elif align=='linear':
maskfile = os.path.join(datapath,'templates', 'grpbold7Tad','qa', 'dico7Tad2grpbold7Tad7Tad','brain_mask_intersection.nii.gz')
ds = mvpa.fmri_dataset(maskfile, mask=maskfile)
dsfile = '_z'+str(zsc)+'_'+str(samples_size)+'_'+align
#Load dataset of two subjects and reorganise for univariate analysis
evds1 = mvpa.h5load(os.path.join('dataset',subj1+dsfile+'.hdf5'))
evds1 = evds1.mapper.reverse(evds1)
evds2 = mvpa.h5load(os.path.join('dataset',subj2+dsfile+'.hdf5'))
evds2 = evds1.mapper.reverse(evds2)
evds = mvpa.vstack([evds1,evds2])
del evds1, evds2
# Prepare inter-subject correlation measure
class Corr(mvpa.Measure):
is_trained = True
def __init__(self,subj1,subj2, **kwargs):
mvpa.Measure.__init__(self, **kwargs)
self._subj1 = subj1
self._subj2 = subj2
def _call(self, evds):
res = 1-sd.pdist(np.hstack((evds[evds.sa.subj==self._subj1].samples,evds[evds.sa.subj==self._subj2].samples)).T,'correlation')
def _run_interface(self, runtime):
print self.inputs.contrast
contrast = np.array(self.inputs.contrast)
design_matrices = [mvpa.h5load(d) for d in self.inputs.design_matrices]
residuals = [nb.load(r).get_data() for r in self.inputs.residuals]
filename_W_var = os.path.join(mkdtemp(), "data.dat")
# W_var = np.zeros(residuals[0].shape[:-1] + residuals[0].shape[-1:] + residuals[0].shape[-1:])
W_var = np.memmap(
filename_W_var,
dtype="float32",
mode="w+",
shape=(residuals[0].shape[:-1] + residuals[0].shape[-1:] + residuals[0].shape[-1:]),
)
# Create Autocorrelation matrices, within voxels and across voxel and comparison voxel
for fd in np.arange(W_var.shape[0]):
for i, r in enumerate(residuals):
W_var[fd] += np.einsum("...k,...h", r[fd], r[fd])
# Normalize without bias
W_var[fd] = W_var[fd] / (len(residuals) - 1)
# Set up covariance matrices, both within voxel and across voxels
V_var = np.zeros(residuals[0].shape[:-1] + (design_matrices[0].shape[1],) * 2)
# Fill in covariance matrix using sandwich
for fd in np.arange(W_var.shape[0]):
for i, X in enumerate(design_matrices):
X_T_inv = np.linalg.pinv(np.dot(X.T, X))
V_var[fd] += np.rollaxis(X_T_inv.dot(X.T).dot(W_var[fd]).dot(X).dot(X_T_inv), 0, -1)
# Normalize covariance matrix by number of replications
V_var = V_var / len(design_matrices)
variance_contrast = contrast.dot(V_var).dot(contrast.T)
nb.save(
nb.Nifti1Image(
variance_contrast,
nb.load(self.inputs.residuals[0]).get_affine(),
nb.load(self.inputs.residuals[0]).get_header(),
),
"variance_contrast.nii.gz",
)
if isdefined(self.inputs.comparison_voxel):
filename_W_covar = os.path.join(mkdtemp(), "data.dat")
# W_covar = np.zeros(residuals[0].shape[:-1] + residuals[0].shape[-1:] + residuals[0].shape[-1:])
W_covar = np.memmap(
filename_W_var,
dtype="float32",
mode="w+",
shape=(residuals[0].shape[:-1] + residuals[0].shape[-1:] + residuals[0].shape[-1:]),
)
for fd in np.arange(W_var.shape[0]):
for i, r in enumerate(residuals):
W_covar[fd] += np.einsum("...k,...h", r[fd], r[self.inputs.comparison_voxel])
W_covar[fd] = W_covar[fd] / (len(residuals) - 1)
V_covar = np.zeros(residuals[0].shape[:-1] + (design_matrices[0].shape[1],) * 2)
for fd in np.arange(W_var.shape[0]):
for i, X in enumerate(design_matrices):
X_T_inv = np.linalg.pinv(np.dot(X.T, X))
V_covar[fd] += np.rollaxis(X_T_inv.dot(X.T).dot(W_covar[fd]).dot(X).dot(X_T_inv), 0, -1)
V_covar = V_covar / len(design_matrices)
covariance_contrast = contrast.dot(V_covar).dot(contrast.T)
covar_filename = "covariance_contrast_comp_voxel_%s.nii.gz" % "_".join(
[str(e) for e in self.inputs.comparison_voxel]
)
nb.save(
nb.Nifti1Image(
covariance_contrast,
nb.load(self.inputs.residuals[0]).get_affine(),
nb.load(self.inputs.residuals[0]).get_header(),
),
covar_filename,
)
return runtime
def _run_interface(self, runtime):
clusters = nb.load(self.inputs.clusters).get_data()
if np.sum(clusters) == 0:
nb.save(nb.Nifti1Image(np.zeros(clusters.shape), np.identity(4)), "in_limbo.nii.gz")
return runtime
if self.inputs.global_threshold:
clusters[clusters > 0] = 1
# Get nearest-neighbors
cluster_centers = [sp.ndimage.measurements.center_of_mass(clusters == c) for c in np.unique(clusters)[1:]]
kdtree = sp.spatial.KDTree(cluster_centers)
grid = np.mgrid[[slice(0, e) for e in clusters.shape]]
r = kdtree.query(np.array([g.ravel() for g in grid]).T)
assigned_clusters = r[1].reshape(clusters.shape) + 1
# get minimum value within cluster
z = nb.load(self.inputs.zmap).get_data()
X = h5load(self.inputs.design_matrix)
# get index of minimum per cluster
cl_min_z = {}
for cl in np.unique(clusters[clusters > 0]):
ind = z[clusters == cl].argmin()
cl_min_z[cl] = tuple(np.array(np.where(clusters == cl))[:, ind])
# get complete variances
variances = nb.load(self.inputs.variance).get_data()
residuals = nb.load(self.inputs.residuals).get_data()
betas = nb.load(self.inputs.beta).get_data()
# per cluster, get t-value
ts = np.zeros(z.shape[:-1])
covars = np.zeros(z.shape[:-1])
for cl in np.unique(clusters[clusters > 0]):
# Index of minimum in cluster
ind_min = tuple([slice(None)] + [e for e in cl_min_z[1]] + [0])
var_min = variances[cl_min_z[cl]][0]
beta_min = betas[cl_min_z[cl]][0]
current_ind = (assigned_clusters == cl) & (clusters == 0)
# Get covariances
current_covars = np.zeros(np.sum(current_ind))
for i in np.arange(residuals.shape[-1]):
print residuals[:, current_ind, i].shape
print residuals[ind_min].shape
current_covars += np.dot(residuals[:, current_ind, i].squeeze().T, residuals[ind_min])
current_covars = current_covars / residuals.shape[-1]
pinv = np.linalg.pinv(np.dot(X.T, X))
current_covars = np.outer(np.dot(np.dot(pinv, X.T), np.dot(X, pinv)), current_covars)[0, :]
current_vars = variances[current_ind][:, 0]
current_betas = betas[current_ind][:, 0]
contrast = beta_min - current_betas
denom_var = np.sqrt(var_min + current_vars - 2 * current_covars)
ts[current_ind] = contrast / denom_var
covars[current_ind] = current_covars
threshold = sp.stats.t.ppf(1.0 - self.inputs.alpha, self.inputs.ddof)
self.in_limbo = (np.abs(ts) < threshold) & (clusters == 0)
nb.save(nb.Nifti1Image(np.array(self.in_limbo, dtype=int), np.identity(4)), "in_limbo.nii.gz")
nb.save(nb.Nifti1Image(np.array(ts, dtype=int), np.identity(4)), "t.nii.gz")
return runtime
"the second dataset "
"(movie or localizer)")
parser.add_argument('-c', '--classifier', help="Which classifier"
"to use: gnb or"
"l-sgd")
parser.add_argument('-b', '--bilateral', help="Is the dataset"
"lateralized or do we"
"have combined ROIs?",
default=True)
parser.add_argument('-o', '--output', help="Where to save fig?")
args = parser.parse_args()
# load the data
ds_1_path = args.input1
ds_2_path = args.input2
ds_1 = mv.h5load(ds_1_path)
ds_2 = mv.h5load(ds_2_path)
# check ds status
if args.bilateral:
bilateral = True
else:
bilateral = False
if args.output:
output = args.output
else:
output = None
# get the classifier information
valid_clfs = ['gnb', 'l-sgd']
qe = mv.SurfaceQueryEngine(surf, 20.0, distance_metric='dijkstra')
print("Finished creating surface-based searchlight query engine")
rdm = mv.PDist(pairwise_metric='correlation', center_data=False)
sl = mv.Searchlight(rdm,
queryengine=qe,
enable_ca=['roi_sizes'],
nproc=6,
roi_ids=cortical_vertices)
mv.debug.active += ['SLC']
for run in runs:
if hyperalign:
mappers = mv.h5load(
join(
mvpa_dir,
'search_hyper_mappers_life_mask_nofsel_{0}_leftout_{1}.hdf5'
.format(hemi, run)))
for participant in participants:
# print(ds.sa['intents'])
# to_avg = mean_group_feature()
# averaged_response = to_avg(ds)
print(
"loading data for run {0}, hemisphere {1}, participant {2}...".
format(run, hemi, participant))
ds = mv.gifti_dataset(
join(
sam_data_dir,
'{0}_task-life_acq-{1}vol_run-0{2}.{3}.tproject.gii'.
# switch = int(sys.argv[1])
# run = (switch % 4) + 1
# if switch < 4:
# model ='aa'
# else:
# model = 'ha'
# print(run, model)
for model in ['ha']:
for run in [3]:
# Load in surface data sets
if model == 'ha':
lh_mappers = mv.h5load(
os.path.join(
mvpa_dir,
'search_hyper_mappers_life_mask_nofsel_lh_leftout_{0}.hdf5'
.format(run)))
rh_mappers = mv.h5load(
os.path.join(
mvpa_dir,
'search_hyper_mappers_life_mask_nofsel_rh_leftout_{0}.hdf5'
.format(run)))
fc = 0
fmri = []
for participant in participants:
print('\nLoading fMRI GIFTI data for {0}'.format(participant))
if model == 'ha':
rh = rh_mappers[participant].forward(
load_data(
os.path.join(
participants = ['sub-rid000001','sub-rid000005','sub-rid000006','sub-rid000009','sub-rid000012',\
'sub-rid000014','sub-rid000017','sub-rid000019','sub-rid000024','sub-rid000027',\
'sub-rid000031','sub-rid000032','sub-rid000033','sub-rid000034','sub-rid000036',\
'sub-rid000037','sub-rid000038','sub-rid000041']
hemispheres = ['lh', 'rh']
for hemi in hemispheres:
# Load surface and create searchlight query engine
surf = mv.surf.read(join(suma_dir, '{0}.pial.gii'.format(hemi)))
qe = mv.SurfaceQueryEngine(surf, 20.0, distance_metric='dijkstra')
print("Finished creating surface-based searchlight query engine")
# Optional load hyperalignment mappers
if hyperalign:
mappers1 = mv.h5load(join(mvpa_dir, 'search_hyper_mappers_life_mask_nofsel_{0}_leftout_1.hdf5'.format(hemi)))
mappers2 = mv.h5load(join(mvpa_dir, 'search_hyper_mappers_life_mask_nofsel_{0}_leftout_2.hdf5'.format(hemi)))
mappers3 = mv.h5load(join(mvpa_dir, 'search_hyper_mappers_life_mask_nofsel_{0}_leftout_3.hdf5'.format(hemi)))
mappers4 = mv.h5load(join(mvpa_dir, 'search_hyper_mappers_life_mask_nofsel_{0}_leftout_4.hdf5'.format(hemi)))
mappers = [mappers1, mappers2, mappers3, mappers4]
print("Loaded hyperalignment mappers")
for participant in participants:
# Load in functional data
ds1 = mv.gifti_dataset(join(sam_data_dir, '{0}_task-life_acq-{1}vol_run-0{2}.{3}.tproject.gii'.format(participant, tr[1], 1, hemi)))
ds2 = mv.gifti_dataset(join(sam_data_dir, '{0}_task-life_acq-{1}vol_run-0{2}.{3}.tproject.gii'.format(participant, tr[2], 2, hemi)))
ds3 = mv.gifti_dataset(join(sam_data_dir, '{0}_task-life_acq-{1}vol_run-0{2}.{3}.tproject.gii'.format(participant, tr[3], 3, hemi)))
ds4 = mv.gifti_dataset(join(sam_data_dir, '{0}_task-life_acq-{1}vol_run-0{2}.{3}.tproject.gii'.format(participant, tr[4], 4, hemi)))
# Exclude medial wall
from __future__ import division
import mvpa2.suite as mvpa
from joblib import dump
import numpy as np
import sys
from sklearn.cross_validation import KFold
subj = sys.argv[1]
subj = int(subj)
spenc_dir = '/home/mboos/SpeechEncoding/'
subj_preprocessed_path = 'PreProcessed/FG_subj{}.gzipped.hdf5'.format(subj)
s1ds = mvpa.h5load(spenc_dir + subj_preprocessed_path)
duration = np.array([902,882,876,976,924,878,1084,676])
# i did not kick out the first/last 4 samples per run yet
slice_nr_per_run = [dur/2 for dur in duration]
# use broadcasting to get indices to delete around the borders
idx_borders = np.cumsum(slice_nr_per_run[:-1])[:,np.newaxis] + \
np.arange(-4,4)[np.newaxis,:]
fmri_data = np.delete(s1ds.samples, idx_borders, axis=0)
# and we're going to remove the last fmri slice
# since it does not correspond to a movie part anymore
fmri_data = fmri_data[:-1, :]
# shape of TR samples
import os
import numpy as np
from mvpa2.suite import h5load
DATA_DIR = '/idata/DBIC/fma/id_studies/home/preprocess/aligned/raiders-32ch/'
NP_DATA_DIR = '/ihome/cara/cvu_thesis/hyper_raiders_brain_data/'
subjects = ['sub-rid000001', 'sub-rid000008', 'sub-rid000009', 'sub-rid000012', 'sub-rid000013', \
'sub-rid000016', 'sub-rid000017', 'sub-rid000018', 'sub-rid000019', 'sub-rid000021', \
'sub-rid000022', 'sub-rid000024', 'sub-rid000025', 'sub-rid000026', 'sub-rid000027', \
'sub-rid000031', 'sub-rid000032', 'sub-rid000036', 'sub-rid000037', 'sub-rid000041']
hemispheres = ['lh', 'rh']
runs = ['1-2-3-4', '5-6-7-8']
for subj in subjects:
for hemi in hemispheres:
for run in runs:
print('{0}_{1}_runs{2}_hyperalign'.format(subj, hemi, run))
filename = DATA_DIR + '{0}_{1}_qhyper-to-raiders-8ch_ico32_z_r20.0_sl-avg_reflection_scaling_non-norm-row_runs{2}.hdf5.gz'.format(
subj, hemi, run)
# get pymvpa dataset
ds = h5load(filename)
# save numpy array
np.save(
NP_DATA_DIR +
'{0}_{1}_runs{2}_hyperalign.npy'.format(subj, hemi, run),
ds.samples)
