def configure(self, time_series, sw, sp):
"""
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 fcd matrix should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
"""
"""
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(self.log, time_series, "time_series")
actual_sp = float(sp) / time_series.sample_period
actual_sw = float(sw) / time_series.sample_period
actual_ts_length = self.input_shape[0]
if actual_sw >= actual_ts_length or actual_sp >= actual_ts_length or actual_sp >= actual_sw:
raise LaunchException(
"Spanning (Sp) and Sliding (Sw) window size parameters need to be less than the TS length, "
"and Sp < Sw. After calibration with sampling period, current values are: Sp=%d, Sw=%d, Ts=%d). "
"Please configure valid input parameters." %
(actual_sp, actual_sw, actual_ts_length))
# -------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = FcdCalculator(time_series=time_series, sw=sw, sp=sp)
def configure(self, time_series, sw, sp):
"""
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 fcd matrix should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
"""
"""
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(self.log, time_series, "time_series")
actual_sp = float(sp) / time_series.sample_period
actual_sw = float(sw) / time_series.sample_period
actual_ts_length = self.input_shape[0]
if actual_sw >= actual_ts_length or actual_sp >= actual_ts_length or actual_sp >= actual_sw:
raise LaunchException(
"Spanning (Sp) and Sliding (Sw) window size parameters need to be less than the TS length, "
"and Sp < Sw. After calibration with sampling period, current values are: Sp=%d, Sw=%d, Ts=%d). "
"Please configure valid input parameters." % (actual_sp, actual_sw, actual_ts_length))
# -------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = FcdCalculator(time_series=time_series, sw=sw, sp=sp)
def configure(self, time_series, sw, sp):
"""
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 fcd matrix should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
"""
"""
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(self.log, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = FcdCalculator(time_series=time_series, sw=sw, sp=sp)
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 = FcdCalculator()
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, sw, sp):
"""
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 fcd matrix should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
"""
"""
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(self.log, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = FcdCalculator(time_series=time_series, sw=sw, sp=sp)
class FunctionalConnectivityDynamicsAdapter(ABCAsynchronous):
""" TVB adapter for calling the Pearson CrossCorrelation algorithm. """
_ui_name = "FCD matrix"
_ui_description = "Functional Connectivity Dynamics metric"
_ui_subsection = "fcd_calculator"
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 = FcdCalculator()
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 [Fcd, ConnectivityMeasure]
def configure(self, time_series, sw, sp):
"""
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 fcd matrix should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
"""
"""
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(self.log, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = FcdCalculator(time_series=time_series, sw=sw, sp=sp)
def get_required_memory_size(self, **kwargs):
# We do not know how much memory is needed.
return -1
def get_required_disk_size(self, **kwargs):
return 0
def launch(self, time_series, sw, sp):
"""
Launch algorithm and build results.
:param time_series: the input time-series for which correlation coefficient should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
:returns: the fcd matrix for the given time-series, with that sw and that sp
:rtype: `Fcd`,`ConnectivityMeasure`
"""
result = [
] # where fcd, fcd_segmented (eventually), and connectivity measures will be stored
[fcd, fcd_segmented, eigvect_dict, eigval_dict,
Connectivity] = self.algorithm.evaluate()
# Create a Fcd dataType object.
result_fcd = Fcd(storage_path=self.storage_path,
source=time_series,
sw=sw,
sp=sp)
result_fcd.array_data = fcd
result.append(result_fcd)
if np.amax(fcd_segmented) == 1.1:
result_fcd_segmented = Fcd(storage_path=self.storage_path,
source=time_series,
sw=sw,
sp=sp)
result_fcd_segmented.array_data = fcd_segmented
result.append(result_fcd_segmented)
for mode in eigvect_dict.keys():
for var in eigvect_dict[mode].keys():
for ep in eigvect_dict[mode][var].keys():
for eig in range(3):
result_eig = ConnectivityMeasure(
storage_path=self.storage_path)
result_eig.connectivity = Connectivity
result_eig.array_data = eigvect_dict[mode][var][ep][
eig]
result_eig.title = "Epoch # %d, \n eigenvalue = %s,\n variable = %s,\n mode = %s." % (
ep, eigval_dict[mode][var][ep][eig], var, mode)
result.append(result_eig)
return result
class FunctionalConnectivityDynamicsAdapter(ABCAsynchronous):
""" TVB adapter for calling the Pearson CrossCorrelation algorithm. """
_ui_name = "FCD matrix"
_ui_description = "Functional Connectivity Dynamics metric"
_ui_subsection = "fcd_calculator"
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 = FcdCalculator()
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 [Fcd, ConnectivityMeasure]
def configure(self, time_series, sw, sp):
"""
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 fcd matrix should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
"""
"""
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(self.log, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = FcdCalculator(time_series=time_series, sw=sw, sp=sp)
def get_required_memory_size(self, **kwargs):
# We do not know how much memory is needed.
return -1
def get_required_disk_size(self, **kwargs):
return 0
def launch(self, time_series, sw, sp):
"""
Launch algorithm and build results.
:param time_series: the input time-series for which correlation coefficient should be computed
:param sw: length of the sliding window
:param sp: spanning time: distance between two consecutive sliding window
:returns: the fcd matrix for the given time-series, with that sw and that sp
:rtype: `Fcd`,`ConnectivityMeasure`
"""
result = [] # where fcd, fcd_segmented (eventually), and connectivity measures will be stored
[fcd, fcd_segmented, eigvect_dict, eigval_dict, Connectivity] = self.algorithm.evaluate()
# Create a Fcd dataType object.
result_fcd = Fcd(storage_path=self.storage_path, source=time_series, sw=sw, sp=sp)
result_fcd.array_data = fcd
result.append(result_fcd)
if np.amax(fcd_segmented)==1.1 :
result_fcd_segmented = Fcd(storage_path=self.storage_path, source=time_series, sw=sw, sp=sp)
result_fcd_segmented.array_data = fcd_segmented
result.append(result_fcd_segmented)
for mode in eigvect_dict.keys():
for var in eigvect_dict[mode].keys():
for ep in eigvect_dict[mode][var].keys():
for eig in range(3):
result_eig = ConnectivityMeasure(storage_path=self.storage_path)
result_eig.connectivity = Connectivity
result_eig.array_data = eigvect_dict[mode][var][ep][eig]
result_eig.title = "Epoch # %d, \n eigenvalue = %s,\n variable = %s,\n mode = %s." % (ep,eigval_dict[mode][var][ep][eig], var, mode)
result.append(result_eig)
return result
