def test_cut_epochs(self):
triggers = [100, 200, 300, 400, 500, 600, 700, 800, 900]
rawdata = np.random.randn(5, 1000)
rawcopy = rawdata.copy()
start, stop = -10, 50
x = datatools.cut_segments(rawdata, triggers, start, stop)
self.assertTrue(np.all(rawdata == rawcopy))
self.assertEqual(x.shape,
(len(triggers), rawdata.shape[0], stop - start))
# test if it works with float indices
start, stop = -10.0, 50.0
x = datatools.cut_segments(rawdata, triggers, start, stop)
self.assertEqual(x.shape,
(len(triggers), x.shape[1], int(stop) - int(start)))
self.assertRaises(ValueError, datatools.cut_segments, rawdata,
triggers, 0, 10.001)
self.assertRaises(ValueError, datatools.cut_segments, rawdata,
triggers, -10.1, 50)
for it in range(len(triggers)):
a = rawdata[:, triggers[it] + start:triggers[it] + stop]
b = x[it, :, :]
self.assertTrue(np.all(a == b))
def test_cut_epochs(self):
triggers = [100, 200, 300, 400, 500, 600, 700, 800, 900]
rawdata = np.random.randn(5, 1000)
rawcopy = rawdata.copy()
start, stop = -10, 50
x = datatools.cut_segments(rawdata, triggers, start, stop)
self.assertTrue(np.all(rawdata == rawcopy))
self.assertEqual(x.shape, (len(triggers), rawdata.shape[0], stop - start))
# test if it works with float indices
start, stop = -10.0, 50.0
x = datatools.cut_segments(rawdata, triggers, start, stop)
self.assertEqual(x.shape, (len(triggers), x.shape[1], int(stop) - int(start)))
self.assertRaises(ValueError, datatools.cut_segments,
rawdata, triggers, 0, 10.001)
self.assertRaises(ValueError, datatools.cut_segments,
rawdata, triggers, -10.1, 50)
for it in range(len(triggers)):
a = rawdata[:, triggers[it] + start: triggers[it] + stop]
b = x[it, :, :]
self.assertTrue(np.all(a == b))
def test_cut_epochs(self):
triggers = [100, 200, 300, 400, 500, 600, 700, 800, 900]
rawdata = np.random.randn(1000, 5)
rawcopy = rawdata.copy()
start = -10
stop = 50
x = datatools.cut_segments(rawdata, triggers, -10, 50)
self.assertTrue(np.all(rawdata == rawcopy))
self.assertEqual(x.shape, (stop - start, x.shape[1], len(triggers)))
for it in range(len(triggers)):
a = rawdata[triggers[it] + start: triggers[it] + stop, :]
b = x[:, :, it]
self.assertTrue(np.all(a == b))
def test_cut_epochs(self):
triggers = [100, 200, 300, 400, 500, 600, 700, 800, 900]
rawdata = np.random.randn(1000, 5)
rawcopy = rawdata.copy()
start = -10
stop = 50
x = datatools.cut_segments(rawdata, triggers, -10, 50)
self.assertTrue(np.all(rawdata == rawcopy))
self.assertEqual(x.shape, (stop - start, x.shape[1], len(triggers)))
for it in range(len(triggers)):
a = rawdata[triggers[it] + start:triggers[it] + stop, :]
b = x[:, :, it]
self.assertTrue(np.all(a == b))
raweeg = midata.eeg.T
triggers = midata.triggers
classes = midata.classes
fs = midata.samplerate
locs = midata.locations
# Set random seed for repeatable results
np.random.seed(42)
# Prepare data
#
# Here we cut out segments from 3s to 4s after each trigger. This is right in
# the middle of the motor imagery period.
data = cut_segments(raweeg, triggers, 3 * fs, 4 * fs)
m = 4 # number of sources to estimate
h = 66 # number of lags for whiteness test
i = 0
for p in [22, 33]:
i += 1
print("Model order:", p)
print(" Performing CSPVARICA")
var = scot.backend["var"](p)
result = cspvarica(data, var, classes, m)
if result.a.is_stable():
s = ""
midata = fetch("mi")[0]
raweeg = midata["eeg"]
triggers = midata["triggers"]
classes = midata["labels"]
fs = midata["fs"]
locs = midata["locations"]
# Set random seed for repeatable results
np.random.seed(42)
# Prepare data
#
# Here we cut out segments from 3s to 4s after each trigger. This is right in
# the middle of the motor imagery period.
data = cut_segments(raweeg, triggers, 3 * fs, 4 * fs)
m = 4 # number of sources to estimate
h = 66 # number of lags for whiteness test
i = 0
for p in [22, 33]:
i += 1
print('Model order:', p)
print(' Performing CSPVARICA')
var = scot.backend['var'](p)
result = cspvarica(data, var, classes, m)
if result.a.is_stable():
s = ''