def configure(self, view_model):
# type: (BalloonModelAdapterModel) -> 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 -------------------##
algorithm = BalloonModel()
if view_model.dt is not None:
algorithm.dt = view_model.dt
else:
algorithm.dt = self.input_time_series_index.sample_period / 1000.
if view_model.tau_s is not None:
algorithm.tau_s = view_model.tau_s
if view_model.tau_f is not None:
algorithm.tau_f = view_model.tau_f
if view_model.bold_model is not None:
algorithm.bold_model = view_model.bold_model
if view_model.RBM is not None:
algorithm.RBM = view_model.RBM
if view_model.neural_input_transformation is not None:
algorithm.neural_input_transformation = view_model.neural_input_transformation
self.algorithm = algorithm
def configure(self, time_series, dt=None, bold_model=None, RBM=None, neural_input_transformation=None):
"""
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 -------------------##
algorithm = BalloonModel()
if dt is not None:
algorithm.dt = dt
else:
algorithm.dt = time_series.sample_period / 1000.
if bold_model is not None:
algorithm.bold_model = bold_model
if RBM is not None:
algorithm.RBM = RBM
if neural_input_transformation is not None:
algorithm.neural_input_transformation = neural_input_transformation
self.algorithm = algorithm
def configure(self,
time_series,
dt=None,
bold_model=None,
RBM=None,
neural_input_transformation=None):
"""
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 -------------------##
algorithm = BalloonModel()
if dt is not None:
algorithm.dt = dt
else:
algorithm.dt = time_series.sample_period / 1000.
if bold_model is not None:
algorithm.bold_model = bold_model
if RBM is not None:
algorithm.RBM = RBM
if neural_input_transformation is not None:
algorithm.neural_input_transformation = neural_input_transformation
self.algorithm = algorithm
self.algorithm.time_series = time_series
def configure(self, time_series, dt=None, bold_model=None, RBM=None, neural_input_transformation=None):
"""
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 -------------------##
algorithm = BalloonModel()
if dt is not None:
algorithm.dt = dt
else:
algorithm.dt = time_series.sample_period / 1000.
if bold_model is not None:
algorithm.bold_model = bold_model
if RBM is not None:
algorithm.RBM = RBM
if neural_input_transformation is not None:
algorithm.neural_input_transformation = neural_input_transformation
self.algorithm = algorithm
self.algorithm.time_series = time_series
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 current analysis.
"""
algorithm = BalloonModel()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
for node in tree:
if node['name'] == 'time_series':
node['conditions'] = FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
def get_traited_datatype(self):
return BalloonModel()