def test_no_stat(self):
"""Test on no stats / no permutations / don't repeat computations."""
y, gt = sim_mi_cc(x, snr=1.)
dt = DatasetEphy(x, y, roi, times=time)
# compute permutations but not statistics
kernel = np.hanning(3)
wf = WfMi('cc', 'ffx', kernel=kernel, verbose=False)
assert isinstance(wf.wf_stats, WfStats)
wf.fit(dt, mcp='nostat', **kw_mi)
assert len(wf.mi) == len(wf.mi_p) == n_roi
assert len(wf.mi_p[0].shape) != 0
# don't compute permutations nor stats
wf = WfMi('cc', 'ffx', verbose=False)
mi, pv = wf.fit(dt, mcp=None, **kw_mi)
assert wf.mi_p[0].shape == (0, )
assert pv.min() == pv.max() == 1.
# don't compute permutations twice
wf = WfMi('cc', 'ffx', verbose=False)
t_start_1 = tst()
wf.fit(dt, mcp='fdr', **kw_mi)
t_end_1 = tst()
t_start_2 = tst()
wf.fit(dt, mcp='maxstat', **kw_mi)
t_end_2 = tst()
assert t_end_1 - t_start_1 > t_end_2 - t_start_2
def test_definition(self):
"""Test workflow definition."""
y, gt = sim_mi_cc(x, snr=1.)
dt = DatasetEphy(x, y, roi, times=time)
wf = WfMi(mi_type='cc', inference='rfx')
wf.fit(dt, **kw_mi)
wf.tvalues
def test_conjunction_analysis(self):
"""Test the conjunction analysis."""
y, gt = sim_mi_cc(x, snr=1.)
dt = DatasetEphy(x, y, roi, times=time)
wf = WfMi(mi_type='cc', inference='rfx')
mi, pv = wf.fit(dt, **kw_mi)
cj_ss, cj = wf.conjunction_analysis(dt)
assert cj_ss.shape == (n_subjects, n_times, n_roi)
assert cj.shape == (n_times, n_roi)
def test_mi_cc(self):
"""Test method fit."""
# built the regressor
y, gt = sim_mi_cc(x, snr=1.)
# run workflow
for mi_meth in ['gc', 'bin']:
dt = DatasetEphy(x, y, roi, times=time)
WfMi(mi_type='cc',
inference='ffx',
mi_method=mi_meth,
verbose=False).fit(dt, **kw_mi)
WfMi(mi_type='cc',
inference='rfx',
mi_method=mi_meth,
verbose=False).fit(dt, **kw_mi)
def test_mi_cc(self):
"""Test method fit."""
# built the regressor
y, gt = sim_mi_cc(x, snr=1.)
# run workflow
dt = DatasetEphy(x.copy(), y=y, roi=roi, times=time)
for est in est_list:
estimator = est(mi_type='cc')
WfMi(mi_type='cc',
inference='ffx',
estimator=estimator,
verbose=False).fit(dt, **kw_mi)
WfMi(mi_type='cc',
inference='rfx',
estimator=estimator,
verbose=False).fit(dt, **kw_mi)
n_epochs=n_epochs,
n_times=n_times,
modality=modality,
random_state=0)
###############################################################################
# Simulate mutual information
# ---------------------------
#
# Once the data have been created, we simulate an increase of mutual
# information by creating a continuous variable `y` using the function
# :func:`frites.simulations.sim_mi_cc`. This allows to simulate model-based
# analysis by computing $I(data; y)$ where `data` and `y` are two continuous
# variables
y, _ = sim_mi_cc(data, snr=.1)
###############################################################################
# Create an electrophysiological dataset
# --------------------------------------
#
# Now, we use the :class:`frites.dataset.DatasetEphy` in order to create a
# compatible electrophysiological dataset
dt = DatasetEphy(data, y, roi=roi, times=time, verbose=False)
###############################################################################
# Define the workflow
# -------------------
#
# We now define the workflow for computing mi and evaluate statistics using the
plt.plot(time, mi)
plt.xlabel("Time (s)"), plt.ylabel("MI (bits)")
plt.title("Multivariate regressor")
plt.show()
###############################################################################
# Evaluate the statistics
# -----------------------
#
# In the section above, the input parameter `stat_method=None` specifies that
# no statistics are going to be computed. Here, we show how to compute either
# within (ffx) or between subject (rfx) statistics.
mi_type = 'cc'
n_perm = 200
y, _ = sim_mi_cc(x, snr=.1)
# within subject statistics (ffx=fixed-effect)
ffx_stat = 'ffx_cluster_tfce'
dt_ffx = DatasetEphy(x, y, roi)
wf_ffx = WfMi(mi_type, 'ffx')
mi_ffx, pv_ffx = wf_ffx.fit(dt_ffx,
mcp='cluster',
cluster_th='tfce',
n_perm=n_perm,
n_jobs=1)
# between-subject statistics (rfx=random-effect)
dt_rfx = DatasetEphy(x, y, roi)
wf_rfx = WfMi(mi_type, 'rfx')
mi_rfx, pv_rfx = wf_rfx.fit(dt_rfx,
def test_sim_mi_cc(self):
"""Test function sim_mi_cc."""
y, gt = sim_mi_cc(x, snr=.8)
assert len(y) == len(x)
assert all([k.shape == (n_epochs, ) for k in y])
assert (len(gt) == n_times) and (gt.dtype == bool)