Python prepare_topoplotsの例

コード例 #1

ファイル: test_plotting.py プロジェクト: tntroger/scot

    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)

コード例 #2

ファイル: test_plotting.py プロジェクト: cbrnr/scot

    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)

コード例 #3

#
# 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
    if not order:
        order = cuthill_mckee(m)

コード例 #4

ファイル: circular.py プロジェクト: bjura/scot

# Extract the full frequency directed transfer function (ffDTF) from the
# activations of each class and calculate the average value over the alpha band
# (8-12 Hz).

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
    if not order:
        order = cuthill_mckee(m)