def spatiotemporal(ds, **kwargs):
onset = 0
for arg in kwargs:
if (arg == 'onset'):
onset = kwargs[arg]
if (arg == 'duration'):
duration = kwargs[arg]
if (arg == 'enable_results'):
enable_results = kwargs[arg]
if (arg == 'permutations'):
permutations = int(kwargs[arg])
events = find_events(targets=ds.sa.targets, chunks=ds.sa.chunks)
if 'duration' in locals():
events = [e for e in events if e['duration'] >= duration]
else:
duration = np.min([ev['duration'] for ev in events])
for e in events:
e['onset'] += onset
e['duration'] = duration
evds = eventrelated_dataset(ds, events=events)
[fclf, cvte] = setup_classifier(**kwargs)
logger.info('Cross validation is performing ...')
res = cvte(evds)
print(cvte.ca.stats)
if permutations != 0:
print(cvte.ca.null_prob.samples)
dist_len = len(cvte.null_dist.dists())
err_arr = np.zeros(dist_len)
for i in range(dist_len):
err_arr[i] = 1 - cvte.ca.stats.stats['ACC']
total_p_value = np.mean(cvte.null_dist.p(err_arr))
p_value = cvte.ca.null_prob.samples
else:
total_p_value = 0.
p_value = np.array([0, 0])
try:
sensana = fclf.get_sensitivity_analyzer()
res_sens = sensana(evds)
except Exception as err:
allowed_keys = [
'map', 'sensitivities', 'stats', 'mapper', 'classifier', 'ds',
'perm_pvalue', 'p'
]
allowed_results = [
None, None, cvte.ca.stats, evds.a.mapper, fclf, evds, p_value,
total_p_value
]
results_dict = dict(zip(allowed_keys, allowed_results))
results = dict()
if not 'enable_results' in locals():
enable_results = allowed_keys[:]
for elem in enable_results:
if elem in allowed_keys:
results[elem] = results_dict[elem]
return results
sens_comb = res_sens.get_mapped(mean_sample())
mean_map = map2nifti(evds, evds.a.mapper.reverse1(sens_comb))
l_maps = []
for m in res_sens:
maps = ds.a.mapper.reverse1(m)
nifti = map2nifti(evds, maps)
l_maps.append(nifti)
l_maps.append(mean_map)
# Packing results (to be sobstitute with a function)
results = dict()
if not 'enable_results' in locals():
enable_results = [
'map', 'sensitivities', 'stats', 'mapper', 'classifier', 'ds',
'pvalue', 'p'
]
allowed_keys = [
'map', 'sensitivities', 'stats', 'mapper', 'classifier', 'ds',
'pvalue', 'p'
]
allowed_results = [
l_maps, res_sens, cvte.ca.stats, evds.a.mapper, fclf, evds, p_value,
total_p_value
]
results_dict = dict(zip(allowed_keys, allowed_results))
for elem in enable_results:
if elem in allowed_keys:
results[elem] = results_dict[elem]
else:
print('******** ' + elem + ' result is not allowed! *********')
return results
def spatial(ds, **kwargs):
# gc.enable()
# gc.set_debug(gc.DEBUG_LEAK)
cvte = None
permutations = 0
for arg in kwargs:
if arg == 'clf_type':
clf_type = kwargs[arg]
if arg == 'enable_results':
enable_results = kwargs[arg].split(',')
if arg == 'permutations':
permutations = int(kwargs[arg])
if arg == 'cvte':
cvte = kwargs[arg]
fclf = cvte.learner # Send crossvalidation object
if cvte == None:
[fclf, cvte] = setup_classifier(**kwargs)
logger.info('Cross validation is performing ...')
error_ = cvte(ds)
logger.debug(cvte.ca.stats)
#print error_.samples
#Plot permutations
#plot_cv_results(cvte, error_, 'Permutations analysis')
if permutations != 0:
dist_len = len(cvte.null_dist.dists())
err_arr = np.zeros(dist_len)
for i in range(dist_len):
err_arr[i] = 1 - cvte.ca.stats.stats['ACC']
total_p_value = np.mean(cvte.null_dist.p(err_arr))
p_value = cvte.ca.null_prob.samples
else:
p_value = np.array([0, 0])
total_p_value = 0
predictions_ds = fclf.predict(ds)
# If classifier didn't have sensitivity
try:
sensana = fclf.get_sensitivity_analyzer()
except Exception as err:
allowed_keys = [
'map', 'sensitivities', 'stats', 'mapper', 'classifier', 'ds_src',
'perm_pvalue', 'p'
]
allowed_results = [
None, None, cvte.ca.stats, ds.a.mapper, fclf, ds, p_value,
total_p_value
]
results_dict = dict(zip(allowed_keys, allowed_results))
results = dict()
if not 'enable_results' in locals():
enable_results = allowed_keys[:]
for elem in enable_results:
if elem in allowed_keys:
results[elem] = results_dict[elem]
return results
res_sens = sensana(ds)
sens_comb = res_sens.get_mapped(mean_sample())
mean_map = map2nifti(ds, ds.a.mapper.reverse1(sens_comb))
l_maps = []
for m in res_sens:
maps = ds.a.mapper.reverse1(m)
nifti = map2nifti(ds, maps)
l_maps.append(nifti)
l_maps.append(mean_map)
# Packing results (to be sobstitute with a function)
results = dict()
classifier = fclf
allowed_keys = [
'map', 'sensitivities', 'stats', 'mapper', 'classifier', 'ds_src',
'pvalue', 'p'
]
allowed_results = [
l_maps, res_sens, cvte.ca.stats, ds.a.mapper, classifier, ds, p_value,
total_p_value
]
results_dict = dict(zip(allowed_keys, allowed_results))
if not 'enable_results' in locals():
enable_results = allowed_keys[:]
for elem in enable_results:
if elem in allowed_keys:
results[elem] = results_dict[elem]
else:
logger.error('******** ' + elem +
' result is not allowed! *********')
return results
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)
p_value, total_p_value]
results_dict = dict(zip(allowed_keys, allowed_results))
results = dict()
if not 'enable_results' in locals():
enable_results = allowed_keys[:]
for elem in enable_results:
if elem in allowed_keys:
results[elem] = results_dict[elem]
return results
res_sens = sensana(ds)
sens_comb = res_sens.get_mapped(mean_sample())
mean_map = map2nifti(ds, ds.a.mapper.reverse1(sens_comb))
l_maps = []
for m in res_sens:
maps = ds.a.mapper.reverse1(m)
nifti = map2nifti(ds, maps)
l_maps.append(nifti)
l_maps.append(mean_map)
# Packing results (to be sobstitute with a function)
results = dict()
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 try: