def read_sensors_of_type(self, sensors_file, type):
"""
:param
sensors_file: Path towards a custom Sensors H5 file
type: Senors type
:return: Sensors object
"""
if not os.path.exists(sensors_file):
self.logger.warning("Senors file %s does not exist!" % sensors_file)
return None
self.logger.info("Starting to read sensors of type %s from: %s" % (type, sensors_file))
h5_file = h5py.File(sensors_file, 'r', libver='latest')
labels = h5_file['/' + SensorsH5Field.LABELS][()]
locations = h5_file['/' + SensorsH5Field.LOCATIONS][()]
if '/orientations' in h5_file:
orientations = h5_file['/orientations'][()]
else:
orientations = None
if '/' + SensorsH5Field.GAIN_MATRIX in h5_file:
gain_matrix = h5_file['/' + SensorsH5Field.GAIN_MATRIX][()]
else:
gain_matrix = None
h5_file.close()
sensors = Sensors(labels, locations, orientations=orientations, gain_matrix=gain_matrix, s_type=type)
self.logger.info("Successfully read sensors from: %s" % sensors_file)
return sensors
def select_signals_seeg(self, signals, rois, auto_selection, **kwargs):
sensors = Sensors(self.sensors_labels, self.sensors_locations, gain_matrix=self.gain_matrix)
inds = range(signals.shape[1])
head_service = HeadService()
if auto_selection.find("rois") >= 0:
if sensors.gain_matrix is not None:
current_selection = head_service.select_sensors_rois(sensors, kwargs.get("rois", rois),
self.signals_inds,
kwargs.get("gain_matrix_th", None))
inds = np.where([s in current_selection for s in self.signals_inds])[0]
self.signals_inds = np.array(self.signals_inds)[inds].tolist()
signals = signals[:, inds]
if auto_selection.find("correlation-power") >= 0:
power = kwargs.get("power", np.sum((signals - np.mean(signals, axis=0)) ** 2, axis=0) / signals.shape[0])
correlation = kwargs.get("correlation", np.corrcoef(signals.T))
current_selection = head_service.select_sensors_corr(sensors, correlation, self.signals_inds, power=power,
n_electrodes=kwargs.get("n_electrodes"),
sensors_per_electrode=kwargs.get(
"sensors_per_electrode", 1),
group_electrodes=kwargs.get("group_electrodes", True))
inds = np.where([s in current_selection for s in self.signals_inds])[0]
self.signals_inds = np.array(self.signals_inds)[inds].tolist()
elif auto_selection.find("power"):
power = kwargs.get("power", np.sum(signals ** 2, axis=0) / signals.shape[0])
inds = select_greater_values_array_inds(power, kwargs.get("power_th", None))
self.signals_inds = (np.array(self.signals_inds)[inds]).tolist()
self.n_signals = len(self.signals_inds)
return signals[:, inds]
def _prepare_dummy_head(self):
reader = H5Reader()
connectivity = reader.read_connectivity(
os.path.join(self.config.input.HEAD, "Connectivity.h5"))
cort_surface = Surface([], [])
seeg_sensors = Sensors(numpy.array(["sens1", "sens2"]),
numpy.array([[0, 0, 0], [0, 1, 0]]))
head = Head(connectivity, cort_surface, sensorsSEEG=seeg_sensors)
return head
class BaseTest(object):
config = Config(output_base=os.path.join(os.getcwd(), "vep_test_out"))
dummy_connectivity = Connectivity("",
numpy.array([[1.0, 2.0, 3.0],
[2.0, 3.0, 1.0],
[3.0, 2.0, 1.0]]),
numpy.array([[4, 5, 6], [5, 6, 4],
[6, 4, 5]]),
labels=["a", "b", "c"],
centres=numpy.array([1.0, 2.0, 3.0]),
normalized_weights=numpy.array(
[[1.0, 2.0, 3.0], [2.0, 3.0, 1.0],
[3.0, 2.0, 1.0]]))
dummy_surface = Surface(numpy.array([[1, 2, 3], [2, 3, 1], [3, 1, 2]]),
numpy.array([[0, 1, 2]]))
dummy_sensors = Sensors(numpy.array(["sens1", "sens2"]),
numpy.array([[0, 0, 0], [0, 1, 0]]),
gain_matrix=numpy.array([[1, 2, 3], [2, 3, 4]]))
def _prepare_dummy_head_from_dummy_attrs(self):
return Head(self.dummy_connectivity,
self.dummy_surface,
sensorsSEEG=[self.dummy_sensors])
def _prepare_dummy_head(self):
reader = H5Reader()
connectivity = reader.read_connectivity(
os.path.join(self.config.input.HEAD, "Connectivity.h5"))
cort_surface = Surface([], [])
seeg_sensors = Sensors(numpy.array(["sens1", "sens2"]),
numpy.array([[0, 0, 0], [0, 1, 0]]))
head = Head(connectivity, cort_surface, sensorsSEEG=seeg_sensors)
return head
@classmethod
def setup_class(cls):
for direc in (cls.config.out.FOLDER_LOGS, cls.config.out.FOLDER_RES,
cls.config.out.FOLDER_FIGURES,
cls.config.out.FOLDER_TEMP):
if not os.path.exists(direc):
os.makedirs(direc)
@classmethod
def teardown_class(cls):
for direc in (cls.config.out.FOLDER_LOGS, cls.config.out.FOLDER_RES,
cls.config.out.FOLDER_FIGURES,
cls.config.out.FOLDER_TEMP):
for dir_file in os.listdir(direc):
os.remove(os.path.join(os.path.abspath(direc), dir_file))
os.removedirs(direc)
def read_sensors(self, filename, root_folder, s_type):
filename = ensure_list(filename)
path = os.path.join(root_folder, filename[0])
if os.path.isfile(path):
if s_type == Sensors.TYPE_EEG:
tvb_sensors = sensors.SensorsEEG.from_file(path)
elif s_type == Sensors.TYPE_MEG:
tvb_sensors = sensors.SensorsMEG.from_file(path)
else:
tvb_sensors = sensors.SensorsInternal.from_file(path)
if len(filename) > 1:
gain_matrix = self.read_gain_matrix(
os.path.join(root_folder, filename[1]), s_type)
else:
gain_matrix = np.array([])
return Sensors(tvb_sensors.labels,
tvb_sensors.locations,
orientations=tvb_sensors.orientations,
gain_matrix=gain_matrix,
s_type=s_type)
else:
self.logger.warning("\nNo Sensor file found at path " + path + "!")
return None
def correlate_sensors(
empirical_file="/Users/lia.domide/Downloads/TRECempirical/110223B-EEX_0004.EEG.mat",
existing_ep_file="/WORK/episense/episense-root/trunk/demo-data/SensorsSEEG_125.h5"
):
data = loadmat(empirical_file)
desired_labels = [str(i).strip().lower() for i in data["channel_names"]]
reader = H5Reader()
labels, locations = reader.read_sensors_of_type(existing_ep_file, "SEEG")
new_labels = []
new_locations = []
ignored_indices = []
for i, label in enumerate(desired_labels):
if label not in labels:
print "Ignored channel", label
ignored_indices.append(i)
continue
idx = numpy.where(labels == label)
new_labels.append(label)
new_locations.append(locations[idx])
H5Writer().write_sensors(
Sensors(new_labels, new_locations),
os.path.join(os.path.dirname(PATIENT_VIRTUAL_HEAD),
"SensorsSEEG_" + str(len(labels)) + ".h5"))
return ignored_indices