def test_read_csv():
"""Test that reading the data from csv file gives you back a reasonable
time-series object """
CA = nitime.CoherenceAnalyzer()
CA.inputs.TR = 1.89 # bogus value just to pass traits test
CA.inputs.in_file = example_data('fmri_timeseries_nolabels.csv')
yield assert_raises, ValueError, CA._read_csv
CA.inputs.in_file = example_data('fmri_timeseries.csv')
data, roi_names = CA._read_csv()
yield assert_equal, data[0][0], 10125.9
yield assert_equal, roi_names[0], 'WM'
def test_read_csv():
"""Test that reading the data from csv file gives you back a reasonable
time-series object """
CA = nitime.CoherenceAnalyzer()
CA.inputs.TR = 1.89 # bogus value just to pass traits test
CA.inputs.in_file = example_data('fmri_timeseries_nolabels.csv')
with pytest.raises(ValueError):
CA._read_csv()
CA.inputs.in_file = example_data('fmri_timeseries.csv')
data, roi_names = CA._read_csv()
assert data[0][0] == 10125.9
assert roi_names[0] == 'WM'
def test_coherence_analysis(tmpdir):
"""Test that the coherence analyzer works"""
import nitime.analysis as nta
import nitime.timeseries as ts
tmpdir.chdir()
# This is the nipype interface analysis:
CA = nitime.CoherenceAnalyzer()
CA.inputs.TR = 1.89
CA.inputs.in_file = example_data("fmri_timeseries.csv")
if display_available:
tmp_png = tempfile.mkstemp(suffix=".png")[1]
CA.inputs.output_figure_file = tmp_png
tmp_csv = tempfile.mkstemp(suffix=".csv")[1]
CA.inputs.output_csv_file = tmp_csv
o = CA.run()
assert o.outputs.coherence_array.shape == (31, 31)
# This is the nitime analysis:
TR = 1.89
data_rec = np.recfromcsv(example_data("fmri_timeseries.csv"))
roi_names = np.array(data_rec.dtype.names)
n_samples = data_rec.shape[0]
data = np.zeros((len(roi_names), n_samples))
for n_idx, roi in enumerate(roi_names):
data[n_idx] = data_rec[roi]
T = ts.TimeSeries(data, sampling_interval=TR)
assert (CA._csv2ts().data == T.data).all()
T.metadata["roi"] = roi_names
C = nta.CoherenceAnalyzer(
T,
method=dict(
this_method="welch", NFFT=CA.inputs.NFFT, n_overlap=CA.inputs.n_overlap
),
)
freq_idx = np.where(
(C.frequencies > CA.inputs.frequency_range[0])
* (C.frequencies < CA.inputs.frequency_range[1])
)[0]
# Extract the coherence and average across these frequency bands:
# Averaging is done on the last dimension
coh = np.mean(C.coherence[:, :, freq_idx], -1)
assert (o.outputs.coherence_array == coh).all()
def test_coherence_analysis():
"""Test that the coherence analyzer works """
import nitime.analysis as nta
import nitime.timeseries as ts
# This is the nipype interface analysis:
CA = nitime.CoherenceAnalyzer()
CA.inputs.TR = 1.89
CA.inputs.in_file = example_data('fmri_timeseries.csv')
if display_available:
tmp_png = tempfile.mkstemp(suffix='.png')[1]
CA.inputs.output_figure_file = tmp_png
tmp_csv = tempfile.mkstemp(suffix='.csv')[1]
CA.inputs.output_csv_file = tmp_csv
o = CA.run()
yield assert_equal, o.outputs.coherence_array.shape, (31, 31)
# This is the nitime analysis:
TR = 1.89
data_rec = np.recfromcsv(example_data('fmri_timeseries.csv'))
roi_names = np.array(data_rec.dtype.names)
n_samples = data_rec.shape[0]
data = np.zeros((len(roi_names), n_samples))
for n_idx, roi in enumerate(roi_names):
data[n_idx] = data_rec[roi]
T = ts.TimeSeries(data, sampling_interval=TR)
yield assert_equal, CA._csv2ts().data, T.data
T.metadata['roi'] = roi_names
C = nta.CoherenceAnalyzer(T,
method=dict(this_method='welch',
NFFT=CA.inputs.NFFT,
n_overlap=CA.inputs.n_overlap))
freq_idx = np.where((C.frequencies > CA.inputs.frequency_range[0]) *
(C.frequencies < CA.inputs.frequency_range[1]))[0]
# Extract the coherence and average across these frequency bands:
coh = np.mean(C.coherence[:, :, freq_idx],
-1) # Averaging on the last dimension
yield assert_equal, o.outputs.coherence_array, coh