def isc_between(E, condA, condB, method=("inter-subject", "subject-total", "total-total"), threshold=True):
condnameA = condA.name.split("/")[-1]
condnameB = condB.name.split("/")[-1]
g_name = "correlations/%s" % condnameB
g_out = condA[g_name] if g_name in condA else condA.create_group(g_name)
iter_runs = E.iter_runs(condnameA)
A_dlist = [run.load(standardized=True, threshold=threshold) for run in iter_runs]
if "inter-subject" in method:
# wg isc
B_runs = E.iter_runs(condnameB)
B_dlist = [run.load(standardized=True, threshold=threshold) for run in B_runs]
C = crosscor(A_dlist, B_dlist, standardized=True)
C_mean = np.squeeze(np.apply_over_axes(nanmean, C, [-1, -2]))
dset_overwrite(g_out, "isc_mat", C)
dset_overwrite(g_out, "inter-subject", C_mean)
if "subject-total" in method:
# WG subject-total
sub_ttl_corr = [corcomposite(dat, condB["composite"]) for dat in A_dlist]
sub_ttl_mean = nanmean(sub_ttl_corr, axis=0)
dset_overwrite(g_out, "subject-total", sub_ttl_mean)
if "total-total" in method:
# BG correlation for composites
gB_out = condB[g_name] if g_name in condB else condB.create_group(g_name)
ttl_ttl_corr = corsubs(condA["composite"][...], condB["composite"][...])
dset_overwrite(g_out, "total-total", ttl_ttl_corr)
dset_overwrite(gB_out, "total-total", ttl_ttl_corr)
def isc_between(E, condA, condB, method=('inter-subject', 'subject-total', 'total-total'), threshold=True):
condnameA = condA.name.split('/')[-1]
condnameB = condB.name.split('/')[-1]
g_name = 'correlations/%s'%condnameB
g_out = condA[g_name] if g_name in condA else condA.create_group(g_name)
iter_runs = E.iter_runs(condnameA)
A_dlist = [run.load(standardized=True, threshold=threshold) for run in iter_runs]
if 'inter-subject' in method:
#wg isc
B_runs = E.iter_runs(condnameB)
B_dlist = [run.load(standardized=True, threshold=threshold) for run in B_runs]
C = crosscor(A_dlist, B_dlist, standardized=True)
C_mean = np.squeeze(np.apply_over_axes(nanmean, C, [-1,-2]))
dset_overwrite(g_out, 'isc_mat', C)
dset_overwrite(g_out, 'inter-subject', C_mean)
if 'subject-total' in method:
#WG subject-total
sub_ttl_corr = [corcomposite(dat, condB['composite']) for dat in A_dlist]
sub_ttl_mean = nanmean(sub_ttl_corr, axis=0)
dset_overwrite(g_out, 'subject-total', sub_ttl_mean)
if 'total-total' in method:
#BG correlation for composites
gB_out = condB[g_name] if g_name in condB else condB.create_group(g_name)
ttl_ttl_corr = corsubs(condA['composite'][...], condB['composite'][...])
dset_overwrite(g_out, 'total-total', ttl_ttl_corr)
dset_overwrite(gB_out, 'total-total', ttl_ttl_corr)
def isc_within(E, condA, method=("inter-subject", "subject-total"), threshold=True):
condnameA = condA.name.split("/")[-1]
g_name = "correlations/%s" % condnameA
g_out = condA[g_name] if g_name in condA else condA.create_group(g_name)
iter_runs = E.iter_runs(condnameA)
dlist = [run.load(standardized=True, threshold=threshold) for run in iter_runs]
if "inter-subject" in method:
# wg isc
C = crosscor(dlist, standardized=True)
C_tmp = C - np.diag([np.nan] * C.shape[-1]) # not done in place, just in case
C_mean = np.squeeze(np.apply_over_axes(nanmean, C_tmp, [-1, -2]))
dset_overwrite(g_out, "isc_mat", C)
dset_overwrite(g_out, "inter-subject", C_mean)
if "subject-total" in method:
# WG subject-total
sub_ttl_corr = [corcomposite(dat, condA["composite"]) for dat in dlist]
sub_ttl_mean = nanmean(sub_ttl_corr, axis=0)
dset_overwrite(g_out, "subject-total", sub_ttl_mean)
def isc_within(E, condA, method=('inter-subject', 'subject-total'), threshold=True):
condnameA = condA.name.split('/')[-1]
g_name = 'correlations/%s'%condnameA
g_out = condA[g_name] if g_name in condA else condA.create_group(g_name)
iter_runs = E.iter_runs(condnameA)
dlist = [run.load(standardized=True, threshold=threshold) for run in iter_runs]
if 'inter-subject' in method:
#wg isc
C = crosscor(dlist, standardized=True)
C_tmp = C - np.diag([np.nan]*C.shape[-1]) #not done in place, just in case
C_mean = np.squeeze(np.apply_over_axes(nanmean, C_tmp, [-1,-2]))
dset_overwrite(g_out, 'isc_mat', C)
dset_overwrite(g_out, 'inter-subject', C_mean)
if 'subject-total' in method:
#WG subject-total
sub_ttl_corr = [corcomposite(dat, condA['composite']) for dat in dlist]
sub_ttl_mean = nanmean(sub_ttl_corr, axis=0)
dset_overwrite(g_out, 'subject-total', sub_ttl_mean)
def isc_within_diff(A, B, standardized=False):
"""Contrast within-group subject-total correlation for A and B.
This function operates on the timecourse data, so is slower
than isc_corrmat_within_diff. Inputs may be multi-dimensional.
The last dimension is used for correlations (e.g. time should be last).
Arguments:
A (list): List of timecourse data for each member of group A.
B (list): Timecourses of same length as A.
Returns:
ndarray with isc for A minus isc for B.
"""
isc = lambda L, ttl: nanmean([corcomposite(dat, ttl, standardized=standardized) for dat in L],
axis=0)
A_composite = sum_tc(A)
B_composite = sum_tc(B)
A_mean_isc = isc(A, A_composite)
B_mean_isc = isc(B, B_composite)
return A_mean_isc - B_mean_isc
