def test_csp(self):
"""
Tests that CSP produces the expected transformation matrix on the data
"""
csp_node = CSPNode(retained_channels=2)
for i in range(self.data.shape[0]):
ts = TimeSeries(input_array = self.data[i:i+1,:],
channel_names = [("test_channel_%s" % j) for j in range(2)],
sampling_frequency = 10, start_time = 0, end_time = 1)
csp_node.train(ts, self.classes[i])
csp_node.stop_training()
self.assert_(numpy.allclose(csp_node.filters,
numpy.array([[-0.75319083, -0.35237094],
[1., -1.]])),
"CSP transformation matrix is wrong! Got:%s, expected:%s"%(
str(csp_node.filters),
str(numpy.array([[-0.75319083, -0.35237094],[1., -1.]]))
))
transformed_data = numpy.zeros(self.data.shape)
for i in range(self.data.shape[0]):
ts = TimeSeries(input_array = self.data[i:i+1,:],
channel_names = [("test_channel_%s" % j) for j in range(2)],
sampling_frequency = 10, start_time = 0, end_time = 1)
transformed_data[i,:] = csp_node.execute(ts)
self.assert_(numpy.allclose(transformed_data[0:2,:],
numpy.array([[0.14525655, -0.83028934],
[0.68796176, -0.23672793]])),
"CSP-transformed data (%s) does not match expectation (%s)!"%(
str(transformed_data[0:2,:]),str(numpy.array([[0.14525655, -0.83028934],
[0.68796176, -0.23672793]]))
))
def test_csp(self):
"""
Tests that CSP produces the expected transformation matrix on the data
"""
csp_node = CSPNode(retained_channels=2)
for i in range(self.data.shape[0]):
ts = TimeSeries(input_array=self.data[i:i + 1, :],
channel_names=[("test_channel_%s" % j)
for j in range(2)],
sampling_frequency=10,
start_time=0,
end_time=1)
csp_node.train(ts, self.classes[i])
csp_node.stop_training()
self.assert_(
numpy.allclose(
csp_node.filters,
numpy.array([[-0.75319083, -0.35237094], [1., -1.]])),
"CSP transformation matrix is wrong! Got:%s, expected:%s" %
(str(csp_node.filters),
str(numpy.array([[-0.75319083, -0.35237094], [1., -1.]]))))
transformed_data = numpy.zeros(self.data.shape)
for i in range(self.data.shape[0]):
ts = TimeSeries(input_array=self.data[i:i + 1, :],
channel_names=[("test_channel_%s" % j)
for j in range(2)],
sampling_frequency=10,
start_time=0,
end_time=1)
transformed_data[i, :] = csp_node.execute(ts)
self.assert_(
numpy.allclose(
transformed_data[0:2, :],
numpy.array([[0.14525655, -0.83028934],
[0.68796176, -0.23672793]])),
"CSP-transformed data (%s) does not match expectation (%s)!" %
(str(transformed_data[0:2, :]),
str(
numpy.array([[0.14525655, -0.83028934],
[0.68796176, -0.23672793]]))))
def store_state(self, result_dir, index=None):
""" Stores this node in the given directory *result_dir* """
if self.store:
try:
node_dir = os.path.join(result_dir, self.__class__.__name__)
create_directory(node_dir)
# This node only stores the learned spatial filters
name = "%s_sp%s.pickle" % ("patterns", self.current_split)
result_file = open(os.path.join(node_dir, name), "wb")
result_file.write(
cPickle.dumps((self.filters, self.wi, self.ai),
protocol=2))
result_file.close()
# Stores the signal to signal plus noise ratio resulted
# by the spatial filter
#fname = "SNR_sp%s.csv" % ( self.current_split)
#numpy.savetxt(os.path.join(node_dir, fname), self.SNR,
# delimiter=',', fmt='%2.5e')
# Store spatial filter plots if desired
if self.visualize_pattern:
from pySPACE.missions.nodes.spatial_filtering.csp \
import CSPNode
# Compute, accumulate and analyze signal components
# estimated by xDAWN
vmin = numpy.inf
vmax = -numpy.inf
signal_components = []
complete_signal = numpy.zeros(
(self.wi.shape[1], self.ai.shape[1]))
for filter_index in range(self.retained_channels):
#self.ai.shape[0]):
signal_component = numpy.outer(
self.wi[filter_index, :], self.ai[filter_index, :])
vmin = min(signal_component.min(), vmin)
vmax = max(signal_component.max(), vmax)
signal_components.append(signal_component)
complete_signal += signal_component
# Plotting
import pylab
for index, signal_component in enumerate(
signal_components):
pylab.figure(0, figsize=(18, 8))
pylab.gcf().clear()
# Plot spatial distribution
ax = pylab.axes([0.0, 0.0, 0.2, 0.5])
CSPNode._plot_spatial_values(ax, self.wi[index, :],
self.channel_names,
'Spatial distribution')
# Plot spatial filter
ax = pylab.axes([0.0, 0.5, 0.2, 0.5])
CSPNode._plot_spatial_values(ax, self.filters[:,
index],
self.channel_names,
'Spatial filter')
# Plot signal component in electrode coordinate system
self._plotTimeSeriesInEC(signal_component,
vmin=vmin,
vmax=vmax,
bb=(0.2, 1.0, 0.0, 1.0))
pylab.savefig("%s%ssignal_component%02d.png" %
(node_dir, os.sep, index))
CSPNode._store_spatial_filter_plots(
self.filters[:, :self.retained_channels],
self.channel_names, node_dir)
# Plot entire signal
pylab.figure(0, figsize=(15, 8))
pylab.gcf().clear()
self._plotTimeSeriesInEC(
complete_signal,
file_name="%s%ssignal_complete.png" %
(node_dir, os.sep))
pylab.savefig("%s%ssignal_complete.png" %
(node_dir, os.sep))
except Exception as e:
print e
raise
super(XDAWNNode, self).store_state(result_dir)
def store_state(self, result_dir, index=None):
""" Stores this node in the given directory *result_dir* """
if self.store:
try:
node_dir = os.path.join(result_dir, self.__class__.__name__)
create_directory(node_dir)
# This node only stores the learned spatial filters
name = "%s_sp%s.pickle" % ("patterns", self.current_split)
result_file = open(os.path.join(node_dir, name), "wb")
result_file.write(cPickle.dumps((self.filters, self.wi,
self.ai), protocol=2))
result_file.close()
# Stores the signal to signal plus noise ratio resulted
# by the spatial filter
#fname = "SNR_sp%s.csv" % ( self.current_split)
#numpy.savetxt(os.path.join(node_dir, fname), self.SNR,
# delimiter=',', fmt='%2.5e')
# Store spatial filter plots if desired
if self.visualize_pattern:
from pySPACE.missions.nodes.spatial_filtering.csp \
import CSPNode
# Compute, accumulate and analyze signal components
# estimated by xDAWN
vmin = numpy.inf
vmax = -numpy.inf
signal_components = []
complete_signal = numpy.zeros((self.wi.shape[1],
self.ai.shape[1]))
for filter_index in range(self.retained_channels):
#self.ai.shape[0]):
signal_component = numpy.outer(self.wi[filter_index, :],
self.ai[filter_index, :])
vmin = min(signal_component.min(), vmin)
vmax = max(signal_component.max(), vmax)
signal_components.append(signal_component)
complete_signal += signal_component
# Plotting
import pylab
for index, signal_component in enumerate(signal_components):
pylab.figure(0, figsize=(18,8))
pylab.gcf().clear()
# Plot spatial distribution
ax=pylab.axes([0.0, 0.0, 0.2, 0.5])
CSPNode._plot_spatial_values(ax, self.wi[index, :],
self.channel_names,
'Spatial distribution')
# Plot spatial filter
ax=pylab.axes([0.0, 0.5, 0.2, 0.5])
CSPNode._plot_spatial_values(ax, self.filters[:, index],
self.channel_names,
'Spatial filter')
# Plot signal component in electrode coordinate system
self._plotTimeSeriesInEC(signal_component, vmin=vmin,
vmax=vmax,
bb=(0.2, 1.0, 0.0, 1.0))
pylab.savefig("%s%ssignal_component%02d.png"
% (node_dir, os.sep, index))
CSPNode._store_spatial_filter_plots(
self.filters[:, :self.retained_channels],
self.channel_names, node_dir)
# Plot entire signal
pylab.figure(0, figsize=(15, 8))
pylab.gcf().clear()
self._plotTimeSeriesInEC(
complete_signal,
file_name="%s%ssignal_complete.png" % (node_dir, os.sep)
)
pylab.savefig(
"%s%ssignal_complete.png" % (node_dir, os.sep))
except Exception as e:
print e
raise
super(XDAWNNode, self).store_state(result_dir)