def test_SeedCorrelationAnalyzer():
targ = ts.TimeSeries(np.random.rand(10, 10), sampling_interval=1)
# Test single source case
seed = ts.TimeSeries(np.random.rand(10), sampling_interval=1)
corr = nta.SeedCorrelationAnalyzer(seed, targ)
our_coef_array = corr.corrcoef
np_coef_array = np.array(
[np.corrcoef(seed.data, a)[0, 1] for a in targ.data])
npt.assert_array_almost_equal(our_coef_array, np_coef_array)
# Test multiple sources
seed = ts.TimeSeries(np.random.rand(2, 10), sampling_interval=1)
corr = nta.SeedCorrelationAnalyzer(seed, targ)
our_coef_array = corr.corrcoef
for source in [0, 1]:
np_coef_array = np.array(
[np.corrcoef(seed.data[source], a)[0, 1] for a in targ.data])
npt.assert_array_almost_equal(our_coef_array[source], np_coef_array)
def _fit(ts_set1, ts_set2):
import nitime.analysis as nta
analyzer = nta.SeedCorrelationAnalyzer(ts_set1, ts_set2)
n_seeds = ts_set1.data.shape[0] if ts_set1.data.ndim > 1 else 1
if n_seeds == 1:
cor = analyzer.corrcoef
else:
cor = []
for seed in range(n_seeds):
cor.append(analyzer.corrcoef[seed])
return cor
coords = list(np.ndindex(volume_shape))
n_seeds = 3
seeds = np.random.randint(0, len(coords), n_seeds)
coords_seeds = np.array(coords)[seeds].T
coords_target = np.array(coords).T
time_series_seed = io.time_series_from_file(fmri_file, coords_seeds, TR=TR, normalize='percent', filter=dict(lb=f_lb, ub=f_ub, method='boxcar'))
time_series_target = io.time_series_from_file(fmri_file, coords_target, TR=TR, normalize='percent',filter=dict(lb=f_lb, ub=f_ub, method='boxcar'))
A = nta.SeedCoherenceAnalyzer(time_series_seed, time_series_target, method=dict(NFFT=20))
B = nta.SeedCorrelationAnalyzer(time_series_seed, time_series_target)
freq_idx = np.where((A.frequencies > f_lb) * (A.frequencies < f_ub))[0]
cor = []
coh = []
for this_seed in range(n_seeds):
coh.append(np.mean(A.coherence[this_seed][:, freq_idx], -1))
cor.append(B.corrcoef[this_seed])
coords_indices = list(coords_target)
vol_coh = []
vol_cor = []
for this_vol in range(n_seeds):
vol_coh.append(np.empty(volume_shape))
# normalize='percent'
nan_targets = np.isnan(np.mean(target_data,1))
print '\n', nan_targets.sum(), 'voxels with nan values ... removing'
target_ts[i_target] = np.mean(target_data[~nan_targets,:],0) # take average across voxels
target_T = ntts.TimeSeries(target_ts, sampling_interval=TR)
fig_target_tseries = viz.plot_tseries(target_T)
plt.title('target tseries, {}'.format(fmri_file))
target_Cor = nta.CorrelationAnalyzer(target_T)
fig_target_cor = viz.drawmatrix_channels(target_Cor.corrcoef, target_rois, color_anchor=0)
plt.title('correlation, {}'.format(fmri_file))
# correlation analyzer
seed_target_Cor = nta.SeedCorrelationAnalyzer(seed_T, target_T)
# coherence analyzer
seed_target_Coh = nta.SeedCoherenceAnalyzer(seed_T, target_T,
method=dict(NFFT=nfft))
# select frequency band
freq_idx = np.where((seed_target_Coh.frequencies > f_lb) * (seed_target_Coh.frequencies < f_ub))[0]
# extract correlation and coherence values
print 'Calculating correlation and coherence'
cor = seed_target_Cor.corrcoef
coh = np.mean(seed_target_Coh.coherence[:, :, freq_idx], -1)
# show correlation matrix
visualize.display_matrix(cor,