def setUp(self):
self.locs = [[0, 0, 1], [1, 0, 0], [0, 1, 0], [-1, 0, 0], [0, -1, 0]]
self.vals = [[1, 0, 0, 0, 0], [0, 0, 0, 0, 0], [1, 1, 1, 1, 1]]
self.topo = Topoplot()
self.topo.set_locations(self.locs)
self.maps = sp.prepare_topoplots(self.topo, self.vals)
def _prepare_plots(self, mixing=False, unmixing=False):
if self.locations_ is None:
raise RuntimeError("Need sensor locations for plotting")
if self.topo_ is None:
from scot.eegtopo.topoplot import Topoplot
self.topo_ = Topoplot(clipping=self.topo_clipping)
self.topo_.set_locations(self.locations_)
if mixing and not self.mixmaps_:
premix = self.premixing_ if self.premixing_ is not None else np.eye(self.mixing_.shape[1])
self.mixmaps_ = self.plotting.prepare_topoplots(self.topo_, np.dot(self.mixing_, premix))
#self.mixmaps_ = self.plotting.prepare_topoplots(self.topo_, self.mixing_)
if unmixing and not self.unmixmaps_:
preinv = np.linalg.pinv(self.premixing_) if self.premixing_ is not None else np.eye(self.unmixing_.shape[0])
self.unmixmaps_ = self.plotting.prepare_topoplots(self.topo_, np.dot(preinv, self.unmixing_).T)
# Connectivity Analysis
#
# Extract the full frequency directed transfer function (ffDTF) from the
# activations of each class and calculate the average value over the alpha band (8-12Hz).
freq = np.linspace(0, fs, ws.nfft_)
alpha, beta = {}, {}
for c in np.unique(classes):
ws.set_used_labels([c])
ws.fit_var()
con = ws.get_connectivity('ffDTF')
alpha[c] = np.mean(con[:, :, np.logical_and(8 < freq, freq < 12)], axis=2)
# Prepare topography plots
topo = Topoplot()
topo.set_locations(locs)
mixmaps = plotting.prepare_topoplots(topo, ws.mixing_)
# Force diagonal (self-connectivity) to 0
np.fill_diagonal(alpha['hand'], 0)
np.fill_diagonal(alpha['foot'], 0)
order = None
for cls in ['hand', 'foot']:
np.fill_diagonal(alpha[cls], 0)
w = alpha[cls]
m = alpha[cls] > 4
# use same ordering of components for each class
