def test_append_cnt_swapaxes(self):
"""append_cnt must work with nonstandard timeaxis."""
dat = append_cnt(swapaxes(self.dat, 0, 1),
swapaxes(self.dat, 0, 1),
timeaxis=1)
dat = swapaxes(dat, 0, 1)
dat2 = append_cnt(self.dat, self.dat)
self.assertEqual(dat, dat2)
def test_append_cnt_with_extra(self):
"""append_cnt with extra must work with list and ndarrays."""
self.dat.a = range(10)
self.dat.b = np.arange(10)
dat = append_cnt(self.dat, self.dat, extra=['a', 'b'])
self.assertEqual(dat.a, range(10) + range(10))
np.testing.assert_array_equal(dat.b, np.concatenate([np.arange(10), np.arange(10)]))
self.assertEqual(dat.markers, 2 * self.dat.markers)
def test_append_cnt(self):
"""append_cnt."""
dat = append_cnt(self.dat, self.dat)
self.assertEqual(dat.data.shape[0], 2*self.dat.data.shape[0])
self.assertEqual(len(dat.axes[0]), 2*len(self.dat.axes[0]))
np.testing.assert_array_equal(dat.data, np.concatenate([self.dat.data, self.dat.data], axis=0))
np.testing.assert_array_equal(dat.axes[0], np.concatenate([self.dat.axes[0], self.dat.axes[0]]))
self.assertEqual(dat.markers, 2 * self.dat.markers)
def test_append_cnt_with_extra(self):
"""append_cnt with extra must work with list and ndarrays."""
self.dat.a = list(range(10))
self.dat.b = np.arange(10)
dat = append_cnt(self.dat, self.dat, extra=['a', 'b'])
self.assertEqual(dat.a, list(range(10)) + list(range(10)))
np.testing.assert_array_equal(dat.b, np.concatenate([np.arange(10), np.arange(10)]))
self.assertEqual(dat.markers, self.dat.markers + [[x[0] + 3000, x[1]] for x in self.dat.markers])
def test_append_cnt(self):
"""append_cnt."""
dat = append_cnt(self.dat, self.dat)
self.assertEqual(dat.data.shape[0], 2*self.dat.data.shape[0])
self.assertEqual(len(dat.axes[0]), 2*len(self.dat.axes[0]))
np.testing.assert_array_equal(dat.data, np.concatenate([self.dat.data, self.dat.data], axis=0))
np.testing.assert_array_equal(dat.axes[0], np.linspace(0, 6000, 60, endpoint=False))
self.assertEqual(dat.markers, self.dat.markers + [[x[0] + 3000, x[1]] for x in self.dat.markers])
def test_append_cnt_with_extra(self):
"""append_cnt with extra must work with list and ndarrays."""
self.dat.a = list(range(10))
self.dat.b = np.arange(10)
dat = append_cnt(self.dat, self.dat, extra=['a', 'b'])
self.assertEqual(dat.a, list(range(10)) + list(range(10)))
np.testing.assert_array_equal(
dat.b, np.concatenate([np.arange(10), np.arange(10)]))
self.assertEqual(
dat.markers,
self.dat.markers + [[x[0] + 3000, x[1]] for x in self.dat.markers])
def test_append_cnt(self):
"""append_cnt."""
dat = append_cnt(self.dat, self.dat)
self.assertEqual(dat.data.shape[0], 2 * self.dat.data.shape[0])
self.assertEqual(len(dat.axes[0]), 2 * len(self.dat.axes[0]))
np.testing.assert_array_equal(
dat.data, np.concatenate([self.dat.data, self.dat.data], axis=0))
np.testing.assert_array_equal(dat.axes[0],
np.linspace(0, 6000, 60, endpoint=False))
self.assertEqual(
dat.markers,
self.dat.markers + [[x[0] + 3000, x[1]] for x in self.dat.markers])
def load(self):
cnt = self.set_loaders[0].load()
for loader in self.set_loaders[1:]:
next_cnt = loader.load()
# always sample down to lowest common denominator
if next_cnt.fs > cnt.fs:
log.warn("Next set has larger sampling rate ({:d}) "
"than before ({:d}), resampling next set".format(
next_cnt.fs, cnt.fs))
next_cnt = resample_cnt(next_cnt, cnt.fs)
if next_cnt.fs < cnt.fs:
log.warn("Next set has smaller sampling rate ({:d}) "
"than before ({:d}), resampling set so far".format(
next_cnt.fs, cnt.fs))
cnt = resample_cnt(cnt, next_cnt.fs)
cnt = append_cnt(cnt, next_cnt)
return cnt
def append(self, dat):
"""Append data to the Block Buffer.
This method accumulates the incoming data.
Parameters
----------
dat : Data
continuous Data object
"""
if self.dat is None:
self.dat = dat.copy()
elif not dat:
pass
else:
self.dat = append_cnt(self.dat, dat)
def init_training_vars(self):
assert self.n_folds is None, "Cannot use folds on train test split"
assert self.restricted_n_trials is None, "Not implemented yet"
self.filterbands = generate_filterbank(min_freq=self.min_freq,
max_freq=self.max_freq, last_low_freq=self.last_low_freq,
low_width=self.low_width, low_overlap=self.low_overlap,
high_width=self.high_width, high_overlap=self.high_overlap,
low_bound=self.low_bound)
assert filterbank_is_stable(self.filterbands, self.filt_order,
self.cnt.fs), (
"Expect filter bank to be stable given filter order.")
self.class_pairs = list(itertools.combinations([0,1,2,3],2))
train_fold = range(len(self.cnt.markers))
test_fold = np.arange(len(self.test_cnt.markers)) + len(train_fold)
self.folds = [{'train': train_fold, 'test': test_fold}]
assert np.intersect1d(self.folds[0]['test'],
self.folds[0]['train']).size == 0
# merge cnts!!
self.cnt = append_cnt(self.cnt, self.test_cnt)
def init_training_vars(self):
assert self.n_folds is None, "Cannot use folds on train test split"
assert self.restricted_n_trials is None, "Not implemented yet"
self.filterbands = generate_filterbank(
min_freq=self.min_freq,
max_freq=self.max_freq,
last_low_freq=self.last_low_freq,
low_width=self.low_width,
low_overlap=self.low_overlap,
high_width=self.high_width,
high_overlap=self.high_overlap,
low_bound=self.low_bound)
assert filterbank_is_stable(
self.filterbands, self.filt_order, self.cnt.fs), (
"Expect filter bank to be stable given filter order.")
# check if number of selected features is not too large
if self.n_selected_features is not None:
n_spatial_filters = self.n_top_bottom_csp_filters
if n_spatial_filters is None:
n_spatial_filters = len(self.sensor_names)
n_max_features = len(self.filterbands) * n_spatial_filters
assert n_max_features >= self.n_selected_features, (
"Cannot select more features than will be originally created "
"Originally: {:d}, requested: {:d}".format(
n_max_features, self.n_selected_features))
n_classes = len(self.marker_def)
self.class_pairs = list(itertools.combinations(range(n_classes), 2))
# check that markers are all for trials
for _, mrk_code in self.cnt.markers + self.test_cnt.markers:
assert mrk_code in list(itertools.chain(*self.marker_def.values()))
train_fold = range(len(self.cnt.markers))
test_fold = np.arange(len(self.test_cnt.markers)) + len(train_fold)
self.folds = [{'train': train_fold, 'test': test_fold}]
assert np.intersect1d(self.folds[0]['test'],
self.folds[0]['train']).size == 0
# merge cnts!!
self.cnt = append_cnt(self.cnt, self.test_cnt)
def test_append_cnt_copy(self):
"""append_cnt means must not modify argument."""
cpy = self.dat.copy()
append_cnt(self.dat, self.dat)
self.assertEqual(self.dat, cpy)
def test_append_cnt_swapaxes(self):
"""append_cnt must work with nonstandard timeaxis."""
dat = append_cnt(swapaxes(self.dat, 0, 1), swapaxes(self.dat, 0, 1), timeaxis=1)
dat = swapaxes(dat, 0, 1)
dat2 = append_cnt(self.dat, self.dat)
self.assertEqual(dat, dat2)
def test_append_cnt_copy(self):
"""append_cnt means must not modify argument."""
cpy = self.dat.copy()
append_cnt(self.dat, self.dat)
self.assertEqual(self.dat, cpy)