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 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)
