def _nbs_helper(x, y, expected_pval, atol=.05, thresh=.1, ntrials=25,
paired=False):
# comment
pval, _, _ = bct.nbs_bct(x, y, thresh, k=ntrials, paired=paired)
print(pval, expected_pval)
assert np.allclose(pval, expected_pval, atol=atol)
def _nbs_helper(x, y, expected_pval, atol=.05, thresh=.1, ntrials=25,
paired=False):
# comment
pval, _, _ = bct.nbs_bct(x, y, thresh, k=ntrials, paired=paired)
print pval, expected_pval
assert np.allclose(pval, expected_pval, atol=atol)
def nbs_and_graphs(corr1, corr2, p_thresh, k, atlas, verbose):
coordinates = plotting.find_parcellation_cut_coords(labels_img=atlas)
corr1 = np.asarray(corr1, dtype="float")
corr2 = np.asarray(corr2, dtype="float")
if corr1.shape[0] != corr1.shape[1]:
corr1 = np.moveaxis(corr1, 0, -1)
corr2 = np.moveaxis(corr2, 0, -1)
thresh = stats.t.isf(p_thresh, corr1.shape[2])
pval, adj, _ = bct.nbs_bct(
corr1, corr2, thresh, k=k, tail="both", paired=True, verbose=verbose
)
print(pval)
gridkw = dict(width_ratios=[1, 2])
fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw=gridkw, figsize=(15, 4))
g = sns.heatmap(adj, square=True, ax=ax1, cmap="Greys")
h = plotting.plot_connectome_strength(
adj, node_coords=coordinates, cmap="YlGnBu", axes=ax2
)
return pval, adj, fig
)
f_phys_corrmats.append(corrmat.values)
f_phy = np.dstack((f_phys_corrmats))
# run nbs for female > male in physics
d_freedom = len(f_df.index) - 2
d_freedom
t_crit_sex = t.ppf(0.95, d_freedom)
m_phy.shape
phy_pval, phy_adj, phy_null = bct.nbs_bct(f_phy,
m_phy,
thresh=t_crit_sex,
k=1000,
tail="left",
paired=False,
verbose=False)
pd.DataFrame(phy_adj, index=labels, columns=labels).to_csv(
join(data_dir, "m-gt-f_phy-regionwise_comp_adj_{0}.csv".format(phy_pval)))
phy_pval, phy_adj, phy_null = bct.nbs_bct(f_phy,
m_phy,
thresh=t_crit_sex,
k=1000,
tail="right",
paired=False,
verbose=False)
pd.DataFrame(phy_adj, index=labels, columns=labels).to_csv(
join(data_dir, "f-gt-m_phy-regionwise_comp_adj_{0}.csv".format(phy_pval)))