def test_calculate_signed_r_square_swapaxes(self):
"""caluclate_r_square must work with nonstandard classaxis."""
dat = calculate_signed_r_square(swapaxes(self.dat, 0, 2), classaxis=2)
# the class-axis just dissapears during
# calculate_signed_r_square, so axis 2 becomes axis 1
dat = dat.swapaxes(0, 1)
dat2 = calculate_signed_r_square(self.dat)
np.testing.assert_array_equal(dat, dat2)
def test_calculate_signed_r_square_swapaxes(self):
"""caluclate_r_square must work with nonstandard classaxis."""
dat = calculate_signed_r_square(swapaxes(self.dat, 0, 2), classaxis=2)
# the class-axis just dissapears during
# calculate_signed_r_square, so axis 2 becomes axis 1
dat = dat.swapaxes(0, 1)
dat2 = calculate_signed_r_square(self.dat)
np.testing.assert_array_equal(dat, dat2)
def test_calculate_signed_r_square_swapaxes(self):
"""caluclate_r_square must work with nonstandard classaxis."""
dat = calculate_signed_r_square(swapaxes(self.dat, 0, 2), classaxis=2)
# the class-axis just dissapears during
# calculate_signed_r_square, so axis 2 becomes axis 1
dat = dat.swapaxes(0, 1)
dat2 = calculate_signed_r_square(self.dat)
# this used to work with numpy 1.8, but with 1.9 the arrays
# differ slightly after e-15, I don't see why this change
# happened, but it is barely noticeable wo we check for almost
# equality
# np.testing.assert_array_equal(dat, dat2)
np.testing.assert_array_almost_equal(dat, dat2)
def test_calculate_signed_r_square_swapaxes(self):
"""caluclate_r_square must work with nonstandard classaxis."""
dat = calculate_signed_r_square(swapaxes(self.dat, 0, 2), classaxis=2)
# the class-axis just dissapears during
# calculate_signed_r_square, so axis 2 becomes axis 1
dat = dat.swapaxes(0, 1)
dat2 = calculate_signed_r_square(self.dat)
# this used to work with numpy 1.8, but with 1.9 the arrays
# differ slightly after e-15, I don't see why this change
# happened, but it is barely noticeable wo we check for almost
# equality
# np.testing.assert_array_equal(dat, dat2)
np.testing.assert_array_almost_equal(dat, dat2)
def plot_spatio_temporal_r2_values(dat):
"""Calculate the signed r^2 values and plot them in a heatmap.
Parameters
----------
dat : Data
epoched data
"""
r2 = proc.calculate_signed_r_square(dat)
max = np.max(np.abs(r2))
plt.imshow(r2.T,
aspect='auto',
interpolation='None',
vmin=-max,
vmax=max,
cmap='RdBu')
ax = plt.gca()
# TODO: sort front-back, left-right
# use the locators to fine-tune the ticks
#ax.yaxis.set_major_locator(ticker.MaxNLocator())
#ax.xaxis.set_major_locator(ticker.MaxNLocator())
ax.yaxis.set_major_formatter(ticker.IndexFormatter(dat.axes[-1]))
ax.xaxis.set_major_formatter(
ticker.IndexFormatter(['%.1f' % i for i in dat.axes[-2]]))
plt.xlabel('%s [%s]' % (dat.names[-2], dat.units[-2]))
plt.ylabel('%s [%s]' % (dat.names[-1], dat.units[-1]))
plt.tight_layout(True)
plt.colorbar()
plt.grid(True)
def plot_spatio_temporal_r2_values(dat):
"""Calculate the signed r^2 values and plot them in a heatmap.
Parameters
----------
dat : Data
epoched data
"""
r2 = proc.calculate_signed_r_square(dat)
r2 *= -1
max = np.max(np.abs(r2))
plt.imshow(r2.T, aspect='auto', interpolation='None', vmin=-max, vmax=max, cmap='RdBu')
ax = plt.gca()
# TODO: sort front-back, left-right
# use the locators to fine-tune the ticks
mask = [True if chan.endswith('z') else False for chan in dat.axes[-1]]
ax.yaxis.set_major_locator(ticker.FixedLocator(np.nonzero(mask)[0]))
ax.yaxis.set_major_formatter(ticker.IndexFormatter(dat.axes[-1]))
ax.xaxis.set_major_locator(ticker.MultipleLocator( np.max(dat.axes[-2]) // 100))
ax.xaxis.set_major_formatter(ticker.IndexFormatter(['%.1f' % i for i in dat.axes[-2]]))
plt.xlabel('%s [%s]' % (dat.names[-2], dat.units[-2]))
plt.ylabel('%s [%s]' % (dat.names[-1], dat.units[-1]))
plt.tight_layout(True)
plt.colorbar()
plt.grid(True)
def test_calculate_signed_r_square(self):
"""Calculating signed r**2."""
dat = calculate_signed_r_square(self.dat)
self.assertEqual(dat.ndim + 1, self.dat.data.ndim)
# average over channels (one could also take just one channel)
dat = dat.mean(axis=1)
# check the intervals
self.assertTrue(all(dat[0:20] < .2))
self.assertTrue(all(dat[20:40] > .5))
self.assertTrue(all(dat[40:60] < .2))
self.assertTrue(all(dat[60:80] < .5))
self.assertTrue(all(dat[80:100] < .2))
def test_calculate_signed_r_square(self):
"""Calculating signed r**2."""
dat = calculate_signed_r_square(self.dat)
self.assertEqual(dat.ndim + 1, self.dat.data.ndim)
# average over channels (one could also take just one channel)
dat = dat.mean(axis=1)
# check the intervals
self.assertTrue(all(dat[0:20] < .2))
self.assertTrue(all(dat[20:40] > .5))
self.assertTrue(all(dat[40:60] < .2))
self.assertTrue(all(dat[60:80] < .5))
self.assertTrue(all(dat[80:100] < .2))
def test_calculate_signed_r_square_copy(self):
"""caluclate_r_square must not modify argument."""
cpy = self.dat.copy()
calculate_signed_r_square(self.dat)
self.assertEqual(self.dat, cpy)
def test_calculate_signed_r_square_with_cnt(self):
"""Select epochs must raise an exception if called with cnt argument."""
del(self.dat.class_names)
with self.assertRaises(AssertionError):
calculate_signed_r_square(self.dat)
def test_calculate_signed_r_square_min_max(self):
"""Min and max values must be in [-1, 1]."""
dat = calculate_signed_r_square(self.dat)
self.assertTrue(-1 <= np.min(dat) <= 1)
self.assertTrue(-1 <= np.max(dat) <= 1)
def test_calculate_signed_r_square_copy(self):
"""caluclate_r_square must not modify argument."""
cpy = self.dat.copy()
calculate_signed_r_square(self.dat)
self.assertEqual(self.dat, cpy)
def test_calculate_signed_r_square_with_cnt(self):
"""Select epochs must raise an exception if called with cnt argument."""
del (self.dat.class_names)
with self.assertRaises(AssertionError):
calculate_signed_r_square(self.dat)
def test_calculate_signed_r_square_min_max(self):
"""Min and max values must be in [-1, 1]."""
dat = calculate_signed_r_square(self.dat)
self.assertTrue(-1 <= np.min(dat) <= 1)
self.assertTrue(-1 <= np.max(dat) <= 1)
