def configure(self, time_series):
"""
Store the input shape to be later used to estimate memory usage. Also
create the algorithm instance.
"""
self.input_shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = PCA()
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 = PCA()
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):
"""
Store the input shape to be later used to estimate memory usage. Also
create the algorithm instance.
"""
self.input_time_series_index = time_series
self.input_shape = (self.input_time_series_index.data_length_1d,
self.input_time_series_index.data_length_2d,
self.input_time_series_index.data_length_3d,
self.input_time_series_index.data_length_4d)
LOG.debug("Time series shape is %s" % str(self.input_shape))
# -------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = PCA()
def configure(self, view_model):
# type: (PCAAdapterModel) -> None
"""
Store the input shape to be later used to estimate memory usage. Also
create the algorithm instance.
"""
self.input_time_series_index = self.load_entity_by_gid(
view_model.time_series)
self.input_shape = (self.input_time_series_index.data_length_1d,
self.input_time_series_index.data_length_2d,
self.input_time_series_index.data_length_3d,
self.input_time_series_index.data_length_4d)
self.log.debug("Time series shape is %s" % str(self.input_shape))
# -------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = PCA()
def get_traited_datatype(self):
return PCA()
