def _tseries_from_nifti_helper(coords, data, TR, filter, normalize, average):
"""
Helper function for the function time_series_from_nifti, which does the
core operations of pulling out data from a data array given coords and then
normalizing and averaging if needed
"""
if coords is not None:
out_data = np.asarray(data[coords[0], coords[1], coords[2]])
else:
out_data = data
tseries = ts.TimeSeries(out_data, sampling_interval=TR)
if filter is not None:
if filter['method'] not in ('boxcar', 'fourier', 'fir', 'iir'):
e_s = "Filter method %s is not recognized" % filter['method']
raise ValueError(e_s)
else:
#Construct the key-word arguments to FilterAnalyzer:
kwargs = dict(lb=filter.get('lb', 0),
ub=filter.get('ub', None),
boxcar_iterations=filter.get('boxcar_iterations', 2),
filt_order=filter.get('filt_order', 64),
gpass=filter.get('gpass', 1),
gstop=filter.get('gstop', 60),
iir_ftype=filter.get('iir_ftype', 'ellip'),
fir_win=filter.get('fir_win', 'hamming'))
F = tsa.FilterAnalyzer(tseries, **kwargs)
if filter['method'] == 'boxcar':
tseries = F.filtered_boxcar
elif filter['method'] == 'fourier':
tseries = F.filtered_fourier
elif filter['method'] == 'fir':
tseries = F.fir
elif filter['method'] == 'iir':
tseries = F.iir
if normalize == 'percent':
tseries = tsa.NormalizationAnalyzer(tseries).percent_change
elif normalize == 'zscore':
tseries = tsa.NormalizationAnalyzer(tseries).z_score
if average:
if coords is None:
tseries.data = np.mean(
np.reshape(
tseries.data,
(np.array(tseries.shape[:-1]).prod(), tseries.shape[-1])),
0)
else:
tseries.data = np.mean(tseries.data, 0)
return tseries
def test_NormalizationAnalyzer():
"""Testing the NormalizationAnalyzer """
t1 = ts.TimeSeries(data=[[99, 100, 101], [99, 100, 101]], sampling_interval=1.)
t2 = ts.TimeSeries(data=[[-1, 0, 1], [-1, 0, 1]], sampling_interval=1.)
N1 = nta.NormalizationAnalyzer(t1)
npt.assert_almost_equal(N1.percent_change[0], t2[0])
t3 = ts.TimeSeries(data=[[100, 102], [1, 3]], sampling_interval=1.)
t4 = ts.TimeSeries(data=[[-1, 1], [-1, 1]], sampling_interval=1.)
N2 = nta.NormalizationAnalyzer(t3)
npt.assert_almost_equal(N2.z_score[0], t4[0])