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 = NodeCovariance()
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 = NodeCovariance()
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 = NodeCovariance()
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 NodeCovarianceAdapter(ABCAsynchronous):
""" TVB adapter for calling the NodeCovariance algorithm. """
_ui_name = "Temporal covariance of nodes"
_ui_description = "Compute Temporal Node Covariance for a TimeSeries input DataType."
_ui_subsection = "covariance"
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 = NodeCovariance()
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 [Covariance]
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 = NodeCovariance()
def get_required_memory_size(self, **kwargs):
"""
Return the required memory to run this algorithm.
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], 1)
input_size = numpy.prod(used_shape) * 8.0
output_size = self.algorithm.result_size(used_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).
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], 1)
return self.algorithm.result_size(used_shape) * TVBSettings.MAGIC_NUMBER / 8 / 2 ** 10
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `Covariance` built with the given timeseries as source
"""
#Create a FourierSpectrum dataType object.
covariance = Covariance(source=time_series, storage_path=self.storage_path)
#NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [slice(self.input_shape[0]), None, slice(self.input_shape[2]), None]
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries(use_storage=False)
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_cov = self.algorithm.evaluate()
covariance.write_data_slice(partial_cov.array_data)
covariance.close_file()
return covariance
class NodeCovarianceAdapter(ABCAsynchronous):
""" TVB adapter for calling the NodeCovariance algorithm. """
_ui_name = "Temporal covariance of nodes"
_ui_description = "Compute Temporal Node Covariance for a TimeSeries input DataType."
_ui_subsection = "covariance"
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 = NodeCovariance()
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 [Covariance]
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 = NodeCovariance()
def get_required_memory_size(self, **kwargs):
"""
Return the required memory to run this algorithm.
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], 1)
input_size = numpy.prod(used_shape) * 8.0
output_size = self.algorithm.result_size(used_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).
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], 1)
return self.array_size2kb(self.algorithm.result_size(used_shape))
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `Covariance` built with the given timeseries as source
"""
#Create a FourierSpectrum dataType object.
covariance = Covariance(source=time_series,
storage_path=self.storage_path)
#NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [
slice(self.input_shape[0]), None,
slice(self.input_shape[2]), None
]
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries(use_storage=False)
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_cov = self.algorithm.evaluate()
covariance.write_data_slice(partial_cov.array_data)
covariance.close_file()
return covariance