# 2. cross validation __________________________________________________________________
# setting up classifier
clf = mv.LinearCSVMC(space='targets')
cv = mv.CrossValidation(clf, mv.NFoldPartitioner(attr='chunks'))
cv_within = cv(ds_q2)
cv_within
np.mean(cv_within)
# why is the mean lower?
# 3. searchlight _______________________________________________________________________
fsaverage_gii = os.path.join(main_dir, 'fs_templates',
hemisphere + '.pial.gii')
surf = mv.surf.read(fsaverage_gii)
# note: surf.vertices.shape (81920, 3) and surf.faces.shape (40962, 3) surface = surf,
qe = mv.SurfaceQueryEngine(surf, radius=radii, distance_metric='dijkstra')
sl = mv.Searchlight(cv, queryengine=qe, roi_ids=cortical_vertices)
sl_q2 = sl(ds_q2)
# 4. save output _______________________________________________________________________
if not os.path.exists(
os.path.join(main_dir, 'analysis', 'searchlight', sub_name)):
os.makedirs(os.path.join(main_dir, 'analysis', 'searchlight', sub_name))
# save as NIML dataset
niml_q1_filename = os.path.join(
main_dir, 'analysis', 'searchlight', sub_name, sub_name + '_ques-02_' +
hemisphere + '_searchlight_radii-' + str(radii) + '.niml.dset')
mv.niml.write(niml_q1_filename, sl_q2)
# save as GIFTI sub-rid000001_ques-01_lh_searchlight_radii-10
searchlight_q1_filename = os.path.join(
d.sa['conditions'] = conditions
d.sa['taxonomy'] = taxonomy
d.sa['behavior'] = behavior
if ds is None:
ds = d
else:
ds = mv.vstack((ds, d))
ds.fa['node_indices'] = range(ds.shape[1])
# zscore all of our samples
mv.zscore(ds, chunks_attr='chunks', dtype='float32')
# load in surgace and get searchlight query
radius = 10
surface = mv.surf.read(join(data_path, '{0}.pial.gii'.format(hemi)))
# this is an arbitrary radius and distance metric!
query = mv.SurfaceQueryEngine(surface, radius, distance_metric='dijkstra')
# based off PyMVPA tutorial
clf = mv.LinearNuSVMC(space=predict)
cv = mv.CrossValidation(clf,
mv.NFoldPartitioner(attr=train_on),
errorfx=lambda p, t: np.mean(p == t),
enable_ca=['stats'])
searchlights = mv.Searchlight(cv,
queryengine=query,
postproc=mv.mean_sample(),
roi_ids=None)
sl_clf_results = searchlights(ds)
outstr = save_path + 'results/sub' + sub + '_sl_clf_' + predict + '_' + hemi
res = np.array(sl_clf_results)
np.save(outstr, res)
# 2. cross validation __________________________________________________________________
# setting up classifier
clf = mv.LinearCSVMC()
cv = mv.CrossValidation(clf, mv.NFoldPartitioner())
cv_within = cv(ds_q3)
cv_within
np.mean(cv_within)
# why is the mean lower?
# 3. searchlight _______________________________________________________________________
fsaverage_gii = os.path.join(main_dir, 'fs_templates',
hemisphere + '.pial.gii')
surf = mv.surf.read(fsaverage_gii)
# note: surf.vertices.shape (81920, 3) and surf.faces.shape (40962, 3) surface = surf,
qe = mv.SurfaceQueryEngine(surf, radius=radii, distance_metric='dijkstra')
sl = mv.Searchlight(cv, queryengine=qe, nproc=4)
sl_q3 = sl(ds_q3)
# 4. save output _______________________________________________________________________
if not os.path.exists(
os.path.join(main_dir, 'analysis', 'searchlight', sub_name)):
os.makedirs(os.path.join(main_dir, 'analysis', 'searchlight', sub_name))
# save as NIML dataset
niml_q1_filename = os.path.join(
main_dir, 'analysis', 'searchlight', sub_name, sub_name + '_ques-03_' +
hemisphere + '_searchlight_radii-' + str(radii) + '.niml.dset')
niml.write(niml_q1_filename, sl_q3)
# save as GIFTI sub-rid000001_ques-01_lh_searchlight_radii-10
searchlight_q1_filename = os.path.join(
main_dir, 'analysis', 'searchlight', sub_name, sub_name + '_ques-03_' +
from scipy.spatial.distance import pdist
import sys
hemi = sys.argv[1]
subid = [1, 12, 17, 27, 32, 33, 34, 36, 37, 41]
subjs = ['{:0>6}'.format(i) for i in subid]
taxonomy = np.repeat(['bird', 'insect', 'primate', 'reptile', 'ungulate'], 4)
behavior = np.tile(['eating', 'fighting', 'running', 'swimming'], 5)
conditions = [' '.join((beh, tax)) for beh, tax in zip(behavior, taxonomy)]
data_path = '/dartfs-hpc/scratch/psyc164/mvpaces/glm/'
dsm = rsa.PDist(center_data=True)
radius = 9
surface = mv.surf.read(pjoin(data_path, '{0}.pial.gii'.format(hemi)))
# this is an arbitrary radius and distance metric!
query = mv.SurfaceQueryEngine(surface, radius, distance_metric='dijkstra')
sl = mv.Searchlight(dsm, query)
mv.debug.active += ['SLC']
print('made our sls')
all_slres = []
for sub in subjs:
# get all our data files for this subj
ds = None
prefix = data_path + 'sub-rid' + sub
suffix = hemi + '.coefs.gii'
fn = prefix + '*' + suffix
files = sorted(glob.glob(fn))
for x in range(len(files)):
if x < 5:
chunks = [x + 1] * 20
else:
#cortical_vertices = = np.load(join(mvpa_dir, 'cortical_vertices_{0}.npy').tolist()
# Z-score features across samples
#mv.zscore(ds, chunks_attr='runs')
ds.samples = ((ds.samples - np.mean(ds.samples, axis=1)[:, None]) /
np.std(ds.samples, axis=1)[:, None])
clf = mv.LinearCSVMC(space=targets)
cv = mv.CrossValidation(clf,
mv.NFoldPartitioner(attr=chunks),
errorfx=mv.mean_match_accuracy)
sl = mv.Searchlight(cv,
queryengine=qe,
enable_ca=['roi_sizes'],
nproc=1,
roi_ids=cortical_vertices)
#sl = mv.Searchlight(cv_rsa, queryengine=qe, enable_ca=['roi_sizes'],
# nproc=1, results_backend='native', roi_ids=cortical_vertices)
#tmp_prefix='/local/tmp/sam_sl_p{0}_{1}_'.format(participant_id, hemi)
mv.debug.active += ['SLC']
sl_result = sl(ds)
# Average across folds and finalize result on surface
print("Average searchlight size = {0}".format(np.mean(sl.ca.roi_sizes)))
assert sl_result.shape[1] == len(cortical_vertices)
sl_final = np.zeros((1, n_vertices))
np.put(sl_final, cortical_vertices, np.mean(sl_result, axis=0))
assert sl_final.shape == (1, n_vertices)
# to just return samples
from mvpa2.testing.tools import assert_collections_equal
from mvpa2.base.collections import SampleAttributesCollection
from mvpa2.base.node import Node
def lean_errorfx(ds):#Node):
#def __call__(self, ds):
assert_collections_equal(ds.sa, target_sa)
# since equal, we could just replace with a blank one
ds.sa = SampleAttributesCollection()
return ds
# the one with the lean one
cv_rsa = mv.CrossValidation(mv.CDist(pairwise_metric='correlation'),
mv.HalfPartitioner(attr='sessions'),
errorfx=None, postproc=lean_errorfx)
sl = mv.Searchlight(cv_rsa, queryengine=qe, enable_ca=['roi_sizes'],
nproc=1, results_backend='native')
#sl = mv.Searchlight(cv_rsa, queryengine=qe, enable_ca=['roi_sizes'],
# nproc=1, results_backend='native', roi_ids=cortical_vertices)
#tmp_prefix='/local/tmp/sam_sl_p{0}_{1}_'.format(participant_id, hemi)
mv.debug.active += ['SLC']
sl_result = sl(ds)
assert len(sl_result.sa) == 0 # we didn't pass any
sl_result.sa = target_sa
print '>>>', np.mean(sl.ca.roi_sizes), np.std(sl.ca.roi_sizes)
sl_means = np.mean(np.dstack((sl_result.samples[:n_conditions**2, :],
sl_result.samples[n_conditions**2:, :])),
axis=2)
sl_final = mv.Dataset(
sl_means,
ds = d
else:
ds = mv.vstack((ds, d))
ds.fa['node_indices'] = range(n_vertices)
ds.samples = zscore(ds.samples, axis=1)
mtgs = mean_group_sample(['conditions'])
mtds = mtgs(ds)
query = mv.SurfaceQueryEngine(surface,
radius,
distance_metric='dijkstra',
fa_node_key='node_indices')
query.train(ds)
dsm = rsa.PDist(square=False)
print('made dsms')
sl = mv.Searchlight(dsm, query, roi_ids=query.query_byid(max_node))
slres = sl(mtds)
mv.debug.active += ['SLC']
print('made our sls')
slres.samples = np.nan_to_num(slres.samples)
all_ROI_res.append(slres.samples)
all_ROI_res = np.array(all_ROI_res)
# this array is (#participants, 190, #nodes)
#all_ROI_res = np.swapaxes(all_slres, 0, 2)
results = np.mean(all_ROI_res, axis=0)
respath = '/dartfs-hpc/scratch/psyc164/mvpaces/lab2/results/'
resname = 'rsa_eb_roi_{0}_{1}'.format(region, hemi)
np.save(respath + resname, results)
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')
return mvpa.Dataset(np.array(res)[np.newaxis])
# Call inter-subject correlation measure
cor = Corr(subj1, subj2)
# Prepare single voxel Searchlight
sl = mvpa.Searchlight(cor,
mvpa.IndexQueryEngine(voxel_indices=mvpa.Sphere(0)),
nproc=int(nproc))
#Iterate the inter-subject correlation measure over all voxels
slmap = sl(evds)
# Name the output
filename = 'sl_' + subj1 + '_' + subj2 + dsfile
# Mapping of the searchlight results into group template space
nimg = mvpa.map2nifti(ds, slmap.samples[:, :ds.nfeatures], imghdr=ds.a.imghdr)
#Save result as nifti in maps directory
try:
os.mkdir(os.path.join(path, 'maps'))
except:
self._subj2 = subj2 def _call(self, evds): dsm1 = sd.pdist(evds[evds.sa.subj==self._subj1].samples, metric='correlation') dsm2 = sd.pdist(evds[evds.sa.subj==self._subj2].samples, metric='correlation') res = 1-Bio.Cluster.distancematrix(np.vstack((dsm1,dsm2)),dist='s')[1][0] return mvpa.Dataset(np.array(res)[np.newaxis]) #Call representational similarity analysis measure rsa = RSA(subj1,subj2) #Prepare Searchlight sl = mvpa.Searchlight(rsa, mvpa.IndexQueryEngine( voxel_indices=mvpa.Sphere(sp), event_offsetidx=lambda x: range(samples_size / TR)), roi_ids='1stidx', postproc=mvpa.mean_sample(), results_fx=spherepack.fx, nproc=int(nproc), enable_ca=['roi_feature_ids']) #Iterate the Representational similarity analysis measure over all searchlight spheres slmap = sl(evds) #Name the output filename = 'sl_'+subj1+'_'+subj2+'_s'+str(sp)+'_sparse'+str(sparse)+dsfile #Mapping of the searchlight results into group template space nimg = mvpa.map2nifti(ds, slmap.samples[:,:ds.nfeatures], imghdr=ds.a.imghdr) #Save result as nifti in maps directory
# 2. cross validation __________________________________________________________________
# setting up classifier
clf = mv.LinearCSVMC()
cv = mv.CrossValidation(clf, mv.NFoldPartitioner())
cv_within = cv(ds_q2)
cv_within
np.mean(cv_within)
# why is the mean lower?
# 3. searchlight _______________________________________________________________________
fsaverage_gii = os.path.join(main_dir, 'fs_templates',
hemisphere + '.pial.gii')
surf = mv.surf.read(fsaverage_gii)
# note: surf.vertices.shape (81920, 3) and surf.faces.shape (40962, 3) surface = surf,
qe = mv.SurfaceQueryEngine(surf, radius=radii, distance_metric='dijkstra')
sl = mv.Searchlight(cv, queryengine=qe)
sl_q2 = sl(ds_q2)
# 4. save output _______________________________________________________________________
# save as NIML dataset
niml_q1_filename = os.path.join(
main_dir, 'analysis', 'searchlight', sub_name, sub_name + '_ques-02_' +
hemisphere + '_searchlight_radii-' + str(radii) + '.niml.dset')
niml.write(niml_q1_filename, sl_q2)
# save as GIFTI sub-rid000001_ques-01_lh_searchlight_radii-10
searchlight_q1_filename = os.path.join(
main_dir, 'analysis', 'searchlight', sub_name, sub_name + '_ques-02_' +
hemisphere + '_searchlight_radii-' + str(radii) + '.gii')
nimg = mv.map2gifti(sl_q2,
