def configure(self, time_series, t_start, t_end):
"""
Store the input shape to be later used to estimate memory usage. Also create the algorithm instance.
:param time_series: the input time-series for which correlation coefficient should be computed
:param t_start: the physical time interval start for the analysis
:param t_end: physical time, interval end
"""
if t_start >= t_end or t_start < 0:
raise LaunchException("Can not launch operation without monitors selected !!!")
shape_tuple = time_series.read_data_shape()
self.input_shape = [shape_tuple[0], shape_tuple[1], shape_tuple[2], shape_tuple[3]]
self.input_shape[0] = int((t_end - t_start) / time_series.sample_period)
log_debug_array(LOG, time_series, "time_series")
self.algorithm = CorrelationCoefficient(time_series=time_series, t_start=t_start, t_end=t_end)
def get_input_tree(self):
"""
Return a list of lists describing the interface to the analyzer. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
algorithm = CorrelationCoefficient()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
tree[0]['conditions'] = FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
class PearsonCorrelationCoefficientAdapter(ABCAsynchronous):
""" TVB adapter for calling the Pearson CrossCorrelation algorithm. """
_ui_name = "Pearson correlation coefficients"
_ui_description = "Cross Correlation"
_ui_subsection = "ccpearson"
def get_input_tree(self):
"""
Return a list of lists describing the interface to the analyzer. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
algorithm = CorrelationCoefficient()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
tree[0]['conditions'] = FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
def get_output(self):
return [CorrelationCoefficients]
def configure(self, time_series, t_start, t_end):
"""
Store the input shape to be later used to estimate memory usage. Also create the algorithm instance.
:param time_series: the input time-series for which correlation coefficient should be computed
:param t_start: the physical time interval start for the analysis
:param t_end: physical time, interval end
"""
if t_start >= t_end or t_start < 0:
raise LaunchException("Can not launch operation without monitors selected !!!")
shape_tuple = time_series.read_data_shape()
self.input_shape = [shape_tuple[0], shape_tuple[1], shape_tuple[2], shape_tuple[3]]
self.input_shape[0] = int((t_end - t_start) / time_series.sample_period)
log_debug_array(LOG, time_series, "time_series")
self.algorithm = CorrelationCoefficient(time_series=time_series, t_start=t_start, t_end=t_end)
def get_required_memory_size(self, **kwargs):
"""
Returns the required memory to be able to run this adapter.
"""
in_memory_input = [self.input_shape[0], 1, self.input_shape[2], 1]
input_size = numpy.prod(in_memory_input) * 8.0
output_size = self.algorithm.result_size(self.input_shape)
return input_size + output_size
def get_required_disk_size(self, **kwargs):
"""
Returns the required disk size to be able to run the adapter (in kB).
"""
output_size = self.algorithm.result_size(self.input_shape)
return self.array_size2kb(output_size)
def launch(self, time_series, t_start, t_end):
"""
Launch algorithm and build results.
:param time_series: the input time-series for which correlation coefficient should be computed
:param t_start: the physical time interval start for the analysis
:param t_end: physical time, interval end
:returns: the correlation coefficient for the given time series
:rtype: `CorrelationCoefficients`
"""
not_stored_result = self.algorithm.evaluate()
result = CorrelationCoefficients(storage_path=self.storage_path, source=time_series)
result.array_data = not_stored_result.array_data
if isinstance(time_series, TimeSeriesEEG) or isinstance(time_series, TimeSeriesMEG) \
or isinstance(time_series, TimeSeriesSEEG):
result.labels_ordering = ["Sensor", "Sensor", "1", "1"]
else:
result.labels_ordering[0] = time_series.labels_ordering[2]
result.labels_ordering[1] = time_series.labels_ordering[2]
return result
