def test_calculate_classwise_average(self):
"""Calculate classwise average."""
avg_dat = calculate_classwise_average(self.dat)
# check for two datches (one for each class)
self.assertEqual(avg_dat.data.shape[0], 2)
# check if the data is correct
self.assertEqual(np.average(avg_dat.data[0]), 1)
self.assertEqual(np.average(avg_dat.data[1]), 2)
# check if we have as many classes on axes as we have in data
self.assertEqual(avg_dat.data.shape[0], len(avg_dat.axes[0]))
#
self.assertEqual(avg_dat.class_names, self.dat.class_names)
def test_calculate_classwise_average(self):
"""Calculate classwise average."""
avg_dat = calculate_classwise_average(self.dat)
# check for two datches (one for each class)
self.assertEqual(avg_dat.data.shape[0], 2)
# check if the data is correct
self.assertEqual(np.average(avg_dat.data[0]), 1)
self.assertEqual(np.average(avg_dat.data[1]), 2)
# check if we have as many classes on axes as we have in data
self.assertEqual(avg_dat.data.shape[0], len(avg_dat.axes[0]))
#
self.assertEqual(avg_dat.class_names, self.dat.class_names)
def plot_channels(dat, figsize=(12, 12), ncols=8, chanaxis=-1, otheraxis=-2):
"""Plot all channels for a continuous or epo.
In case of an epoched Data object, the classwise average is
calculated, and for each channel the respective classes are plotted.
Parameters
----------
dat : Data
continous or epoched Data object
ncols : int, optional
the number of colums in the grid. The number of rows is
calculated depending on ``ncols`` and the number of channels
"""
# test if epo
is_epo = False
if dat.data.ndim == 3:
is_epo = True
dat = proc.calculate_classwise_average(dat)
ax = []
n_channels = dat.data.shape[chanaxis]
nrows = int(np.ceil(n_channels / ncols))
f, ax = plt.subplots(nrows=nrows,
ncols=ncols,
squeeze=False,
sharex=True,
sharey=True,
figsize=figsize)
for i, chan in enumerate(dat.axes[chanaxis]):
a = ax[i // ncols, i % ncols]
dat.axes[otheraxis], dat.data.take([i], chanaxis)
if is_epo:
for j, name in enumerate(dat.class_names):
cnt = proc.select_classes(dat, [j])
a.plot(cnt.axes[otheraxis],
cnt.data.take([i], chanaxis).squeeze(),
label=name)
else:
a.plot(dat.axes[otheraxis], dat.data.take([i], chanaxis).squeeze())
a.set_title(chan)
a.axvline(x=0, color='black')
a.axhline(y=0, color='black')
plt.legend()
def plot_channels(dat, ncols=8, chanaxis=-1, otheraxis=-2):
"""Plot all channels for a continuous or epo.
In case of an epoched Data object, the classwise average is
calculated, and for each channel the respective classes are plotted.
Parameters
----------
dat : Data
continous or epoched Data object
ncols : int, optional
the number of colums in the grid. The number of rows is
calculated depending on ``ncols`` and the number of channels
"""
# test if epo
is_epo = False
if dat.data.ndim == 3:
is_epo = True
dat = proc.calculate_classwise_average(dat)
ax = []
n_channels = dat.data.shape[chanaxis]
nrows = int(np.ceil(n_channels / ncols))
f, ax = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, sharey=True);
for i, chan in enumerate(dat.axes[chanaxis]):
a = ax[i // ncols, i % ncols]
dat.axes[otheraxis], dat.data.take([i], chanaxis)
if is_epo:
for j, name in enumerate(dat.class_names):
cnt = proc.select_classes(dat, [j])
a.plot(cnt.axes[otheraxis], cnt.data.take([i], chanaxis).squeeze(), label=name)
else:
a.plot(dat.axes[otheraxis], dat.data.take([i], chanaxis).squeeze())
a.set_title(chan)
a.axvline(x=0, color='black')
a.axhline(y=0, color='black')
plt.legend()
def test_calculate_classwise_average_copy(self):
"""Calculate classwise avg must not modify the argument."""
cpy = self.dat.copy()
calculate_classwise_average(self.dat)
self.assertEqual(self.dat, cpy)
def test_calculate_classwise_average_with_cnt(self):
"""Calculate classwise avg must raise an error if called with continouos data."""
del (self.dat.class_names)
with self.assertRaises(AssertionError):
calculate_classwise_average(self.dat)
def test_calculate_classwise_average_copy(self):
"""Calculate classwise avg must not modify the argument."""
cpy = self.dat.copy()
calculate_classwise_average(self.dat)
self.assertEqual(self.dat, cpy)
def test_calculate_classwise_average_with_cnt(self):
"""Calculate classwise avg must raise an error if called with continouos data."""
del(self.dat.class_names)
with self.assertRaises(AssertionError):
calculate_classwise_average(self.dat)
