def __init__(self):
BurstBaseController.__init__(self)
self.available_models = get_traited_subclasses(models.Model)
self.available_integrators = get_traited_subclasses(integrators.Integrator)
self.cache = SessionCache()
# Work around a numexpr thread safety issue. See TVB-1639.
self.traj_lock = threading.Lock()
def __init__(self):
BurstBaseController.__init__(self)
self.available_models = get_traited_subclasses(models.Model)
self.available_integrators = get_traited_subclasses(
integrators.Integrator)
self.cache = SessionCache()
# Work around a numexpr thread safety issue. See TVB-1639.
self.traj_lock = threading.Lock()
def test_simulator_region(self):
AVAILABLE_MODELS = get_traited_subclasses(models.Model)
AVAILABLE_METHODS = get_traited_subclasses(integrators.Integrator)
MODEL_NAMES = AVAILABLE_MODELS.keys()
METHOD_NAMES = AVAILABLE_METHODS.keys()
METHOD_NAMES.append('RungeKutta4thOrderDeterministic')
#init
test_simulator = Simulator()
#test cases
for model_name, method_name in itertools.product(
MODEL_NAMES, METHOD_NAMES):
test_simulator.configure(model=model_name,
method=method_name,
surface_sim=False)
test_simulator.run_simulation()
def build_noise(parent_parameters):
"""
Build Noise entity from dictionary of parameters.
:param parent_parameters: dictionary of parameters for the entity having Noise as attribute. \
The dictionary is after UI form-submit and framework pre-process.
:return: Noise entity.
"""
if KEY_NOISE not in parent_parameters:
return None
available_noise = parameters_factory.get_traited_subclasses(Noise)
if 'Noise' not in available_noise:
available_noise['Noise'] = Noise
selected_noise = parent_parameters[KEY_NOISE]
noise_params = parent_parameters[PARAMS_NOISE]
del parent_parameters[KEY_NOISE]
del parent_parameters[PARAMS_NOISE]
if PARAMS_RANDOM_STREAM in noise_params:
stream_params = noise_params[PARAMS_RANDOM_STREAM]
random_stream = numpy.random.RandomState(
seed=stream_params['init_seed'])
del noise_params[PARAMS_RANDOM_STREAM]
del noise_params[KEY_RANDOM_STREAM]
noise_params[KEY_RANDOM_STREAM] = random_stream
if PARAMS_EQUATION in noise_params:
available_equations = parameters_factory.get_traited_subclasses(
equations.Equation)
eq_parameters = noise_params[PARAMS_EQUATION]["parameters"]
equation = noise_params[KEY_EQUATION]
equation = available_equations[equation](parameters=eq_parameters)
del noise_params[PARAMS_EQUATION]
del noise_params[KEY_EQUATION]
noise_params[KEY_EQUATION] = equation
noise_entity = available_noise[str(selected_noise)](**noise_params)
parent_parameters[KEY_NOISE] = noise_entity
return noise_entity
def build_noise(parent_parameters):
"""
Build Noise entity from dictionary of parameters.
:param parent_parameters: dictionary of parameters for the entity having Noise as attribute. \
The dictionary is after UI form-submit and framework pre-process.
:return: Noise entity.
"""
if KEY_NOISE not in parent_parameters:
return None
available_noise = parameters_factory.get_traited_subclasses(Noise)
if 'Noise' not in available_noise:
available_noise['Noise'] = Noise
selected_noise = parent_parameters[KEY_NOISE]
noise_params = parent_parameters[PARAMS_NOISE]
del parent_parameters[KEY_NOISE]
del parent_parameters[PARAMS_NOISE]
if PARAMS_RANDOM_STREAM in noise_params:
stream_params = noise_params[PARAMS_RANDOM_STREAM]
random_stream = numpy.random.RandomState(seed=stream_params['init_seed'])
del noise_params[PARAMS_RANDOM_STREAM]
del noise_params[KEY_RANDOM_STREAM]
noise_params[KEY_RANDOM_STREAM] = random_stream
if PARAMS_EQUATION in noise_params:
available_equations = parameters_factory.get_traited_subclasses(equations.Equation)
eq_parameters = noise_params[PARAMS_EQUATION]["parameters"]
equation = noise_params[KEY_EQUATION]
equation = available_equations[equation](parameters=eq_parameters)
del noise_params[PARAMS_EQUATION]
del noise_params[KEY_EQUATION]
noise_params[KEY_EQUATION] = equation
noise_entity = available_noise[str(selected_noise)](**noise_params)
parent_parameters[KEY_NOISE] = noise_entity
return noise_entity
def test_traitedsubclassed(self):
"""
Tests successful creation of traited classes.
"""
# We imported array so we should have all these traited classes registered
expected = [
'IntegerArray', 'StringArray', 'PositionArray', 'IndexArray',
'BoolArray', 'OrientationArray', 'FloatArray', 'ComplexArray',
'Array', 'SparseMatrix'
]
subclasses = get_traited_subclasses(Array)
for key in expected:
assert key in subclasses
"""
#SpecificTestModel = TestModel
#SpecificTestModel.__bases__ = (BaseModel,)
#tester = SpecificTestModel(**kwargs)
tester = TestModel(model = BaseModel(**kwargs))
tester.configure()
tester.model.configure_initial(1.0, (1, tester.model.nvar, 1, tester.model.number_of_modes))
return tester
if __name__ == '__main__':
# Do some stuff that tests or makes use of this module...
LOG.info("Generating phase-plane images for tvb.simulator.models...")
from tvb.basic.traits.parameters_factory import get_traited_subclasses
AVAILABLE_MODELS = get_traited_subclasses(models.Model)
MODEL_NAMES = AVAILABLE_MODELS.keys()
for model_name in MODEL_NAMES:
LOG.info("Generating phase-planes for %s" % model_name)
tester = test_factory(AVAILABLE_MODELS[model_name])
tester.pplane()
### EoF ###
class TimeseriesMetricsAdapter(ABCAsynchronous):
"""
TVB adapter for exposing as a group the measure algorithm.
"""
_ui_name = "TimeSeries Metrics"
_ui_description = "Compute a single number for a TimeSeries input DataType."
_ui_subsection = "timeseries"
available_algorithms = get_traited_subclasses(
BaseTimeseriesMetricAlgorithm)
def get_input_tree(self):
"""
Compute interface based on introspected algorithms found.
"""
algorithm = BaseTimeseriesMetricAlgorithm()
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])
algo_names = self.available_algorithms.keys()
options = []
for name in algo_names:
options.append({
ABCAdapter.KEY_NAME: name,
ABCAdapter.KEY_VALUE: name
})
tree.append({
'name':
'algorithms',
'label':
'Selected metrics to be applied',
'type':
ABCAdapter.TYPE_MULTIPLE,
'required':
False,
'options':
options,
'description':
'The selected metric algorithms will be applied on the input TimeSeries'
})
return tree
def get_output(self):
return [DatatypeMeasure]
def configure(self, time_series, **kwargs):
"""
Store the input shape to be later used to estimate memory usage.
"""
self.input_shape = time_series.read_data_shape()
def get_required_memory_size(self, **kwargs):
"""
Return the required memory to run this algorithm.
"""
input_size = numpy.prod(self.input_shape) * 8.0
return input_size
def get_required_disk_size(self, **kwargs):
"""
Returns the required disk size to be able to run the adapter (in kB).
"""
return 0
def launch(self,
time_series,
algorithms=None,
start_point=None,
segment=None):
"""
Launch algorithm and build results.
:param time_series: the time series on which the algorithms are run
:param algorithms: the algorithms to be run for computing measures on the time series
:type algorithms: any subclass of BaseTimeseriesMetricAlgorithm
(KuramotoIndex, GlobalVariance, VarianceNodeVariance)
:rtype: `DatatypeMeasure`
"""
if algorithms is None:
algorithms = self.available_algorithms.keys()
shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
metrics_results = {}
for algorithm_name in algorithms:
##------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
node_slice = [
slice(shape[0]),
slice(shape[1]),
slice(shape[2]),
slice(shape[3])
]
##---------- Iterate over slices and compose final result ------------##
unstored_ts = TimeSeries(use_storage=False)
unstored_ts.data = time_series.read_data_slice(tuple(node_slice))
##-------------------- Fill Algorithm for Analysis -------------------##
algorithm = self.available_algorithms[algorithm_name](
time_series=unstored_ts)
if segment is not None:
algorithm.segment = segment
if start_point is not None:
algorithm.start_point = start_point
## Validate that current algorithm's filter is valid.
if (algorithm.accept_filter is not None and not algorithm.
accept_filter.get_python_filter_equivalent(time_series)):
LOG.warning(
'Measure algorithm will not be computed because of incompatibility on input. '
'Filters failed on algo: ' + str(algorithm_name))
continue
else:
LOG.debug("Applying measure: " + str(algorithm_name))
unstored_result = algorithm.evaluate()
##----------------- Prepare a Float object(s) for result ----------------##
if isinstance(unstored_result, dict):
metrics_results.update(unstored_result)
else:
metrics_results[algorithm_name] = unstored_result
result = DatatypeMeasure(analyzed_datatype=time_series,
storage_path=self.storage_path,
data_name=self._ui_name,
metrics=metrics_results)
return result
class SimulatorAdapter(ABCAsynchronous):
"""
Interface between the Simulator and the Framework.
"""
_ui_name = "Simulation Core"
algorithm = None
available_models = get_traited_subclasses(Model)
available_monitors = get_traited_subclasses(Monitor)
available_integrators = get_traited_subclasses(Integrator)
available_couplings = get_traited_subclasses(Coupling)
# This is a list with the monitors that actually return multi dimensions for the state variable dimension.
# We exclude from this for example EEG, MEG or Bold which return
HAVE_STATE_VARIABLES = [
"GlobalAverage", "SpatialAverage", "Raw", "SubSample",
"TemporalAverage"
]
def __init__(self):
super(SimulatorAdapter, self).__init__()
self.log.debug("%s: Initialized..." % str(self))
def get_input_tree2(self):
sim = Simulator()
sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
result = sim.interface_experimental
return result
def get_input_tree(self):
"""
Return a list of lists describing the interface to the simulator. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
sim = Simulator()
sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
result = sim.interface[self.INTERFACE_ATTRIBUTES]
# We should add as hidden the Simulator State attribute.
result.append({
self.KEY_NAME: 'simulation_state',
self.KEY_TYPE: 'tvb.datatypes.simulation_state.SimulationState',
self.KEY_LABEL: "Continuation of",
self.KEY_REQUIRED: False,
self.KEY_UI_HIDE: True
})
return result
def get_output(self):
"""
:returns: list of classes for possible results of the Simulator.
"""
return [time_series.TimeSeries]
def configure(self,
model,
model_parameters,
integrator,
integrator_parameters,
connectivity,
monitors,
monitors_parameters=None,
surface=None,
surface_parameters=None,
stimulus=None,
coupling=None,
coupling_parameters=None,
initial_conditions=None,
conduction_speed=None,
simulation_length=0,
simulation_state=None):
"""
Make preparations for the adapter launch.
"""
self.log.debug("available_couplings: %s..." %
str(self.available_couplings))
self.log.debug("coupling: %s..." % str(coupling))
self.log.debug("coupling_parameters: %s..." % str(coupling_parameters))
self.log.debug("%s: Initializing Model..." % str(self))
noise_framework.build_noise(model_parameters)
model_instance = self.available_models[str(model)](**model_parameters)
self._validate_model_parameters(model_instance, connectivity, surface)
self.log.debug("%s: Initializing Integration scheme..." % str(self))
noise_framework.build_noise(integrator_parameters)
integr = self.available_integrators[integrator](
**integrator_parameters)
self.log.debug("%s: Instantiating Monitors..." % str(self))
monitors_list = []
for monitor_name in monitors:
if (monitors_parameters is not None) and (str(monitor_name)
in monitors_parameters):
current_monitor_parameters = monitors_parameters[str(
monitor_name)]
HRFKernelEquation.build_equation_from_dict(
'hrf_kernel', current_monitor_parameters, True)
monitors_list.append(
self.available_monitors[str(monitor_name)](
**current_monitor_parameters))
else:
### We have monitors without any UI settable parameter.
monitors_list.append(
self.available_monitors[str(monitor_name)]())
if len(monitors) < 1:
raise LaunchException(
"Can not launch operation without monitors selected !!!")
self.log.debug("%s: Initializing Coupling..." % str(self))
coupling_inst = self.available_couplings[str(coupling)](
**coupling_parameters)
self.log.debug("Initializing Cortex...")
if self._is_surface_simulation(surface, surface_parameters):
cortex_entity = Cortex(use_storage=False).populate_cortex(
surface, surface_parameters)
if cortex_entity.region_mapping_data.connectivity.number_of_regions != connectivity.number_of_regions:
raise LaunchException(
"Incompatible RegionMapping -- Connectivity !!")
if cortex_entity.region_mapping_data.surface.number_of_vertices != surface.number_of_vertices:
raise LaunchException(
"Incompatible RegionMapping -- Surface !!")
select_loc_conn = cortex_entity.local_connectivity
if select_loc_conn is not None and select_loc_conn.surface.number_of_vertices != surface.number_of_vertices:
raise LaunchException(
"Incompatible LocalConnectivity -- Surface !!")
else:
cortex_entity = None
self.log.debug("%s: Instantiating requested simulator..." % str(self))
if conduction_speed not in (0.0, None):
connectivity.speed = numpy.array([conduction_speed])
else:
raise LaunchException("conduction speed cannot be 0 or missing")
self.algorithm = Simulator(connectivity=connectivity,
coupling=coupling_inst,
surface=cortex_entity,
stimulus=stimulus,
model=model_instance,
integrator=integr,
monitors=monitors_list,
initial_conditions=initial_conditions,
conduction_speed=conduction_speed)
self.simulation_length = simulation_length
self.log.debug("%s: Initializing storage..." % str(self))
try:
self.algorithm.preconfigure()
except ValueError as err:
raise LaunchException(
"Failed to configure simulator due to invalid Input Values. It could be because "
"of an incompatibility between different version of TVB code.",
err)
def get_required_memory_size(self, **kwargs):
"""
Return the required memory to run this algorithm.
"""
return self.algorithm.memory_requirement()
def get_required_disk_size(self, **kwargs):
"""
Return the required disk size this algorithm estimates it will take. (in kB)
"""
return self.algorithm.storage_requirement(
self.simulation_length) / 2**10
def get_execution_time_approximation(self, **kwargs):
"""
Method should approximate based on input arguments, the time it will take for the operation
to finish (in seconds).
"""
# This is just a brute approx so cluster nodes won't kill operation before
# it's finished. This should be done with a higher grade of sensitivity
# Magic number connecting simulation length to simulation computation time
# This number should as big as possible, as long as it is still realistic, to
magic_number = 6.57e-06 # seconds
approx_number_of_nodes = 500
approx_nvar = 15
approx_modes = 15
simulation_length = int(float(kwargs['simulation_length']))
approx_integrator_dt = float(kwargs['integrator_parameters']['dt'])
if approx_integrator_dt == 0.0:
approx_integrator_dt = 1.0
if 'surface' in kwargs and kwargs[
'surface'] is not None and kwargs['surface'] != '':
approx_number_of_nodes *= approx_number_of_nodes
estimation = magic_number * approx_number_of_nodes * approx_nvar * approx_modes * simulation_length \
/ approx_integrator_dt
return max(int(estimation), 1)
def _try_find_mapping(self, mapping_class, connectivity_gid):
"""
Try to find a DataType instance of class "mapping_class", linked to the given Connectivity.
Entities in the current project will have priority.
:param mapping_class: DT class, with field "_connectivity" on it
:param connectivity_gid: GUID
:return: None or instance of "mapping_class"
"""
dts_list = dao.get_generic_entity(mapping_class, connectivity_gid,
'_connectivity')
if len(dts_list) < 1:
return None
for dt in dts_list:
dt_operation = dao.get_operation_by_id(dt.fk_from_operation)
if dt_operation.fk_launched_in == self.current_project_id:
return dt
return dts_list[0]
def launch(self,
model,
model_parameters,
integrator,
integrator_parameters,
connectivity,
monitors,
monitors_parameters=None,
surface=None,
surface_parameters=None,
stimulus=None,
coupling=None,
coupling_parameters=None,
initial_conditions=None,
conduction_speed=None,
simulation_length=0,
simulation_state=None):
"""
Called from the GUI to launch a simulation.
*: string class name of chosen model, etc...
*_parameters: dictionary of parameters for chosen model, etc...
connectivity: tvb.datatypes.connectivity.Connectivity object.
surface: tvb.datatypes.surfaces.CorticalSurface: or None.
stimulus: tvb.datatypes.patters.* object
"""
result_datatypes = dict()
start_time = self.algorithm.current_step * self.algorithm.integrator.dt
self.algorithm.configure(full_configure=False)
if simulation_state is not None:
simulation_state.fill_into(self.algorithm)
region_map = self._try_find_mapping(region_mapping.RegionMapping,
connectivity.gid)
region_volume_map = self._try_find_mapping(
region_mapping.RegionVolumeMapping, connectivity.gid)
for monitor in self.algorithm.monitors:
m_name = monitor.__class__.__name__
ts = monitor.create_time_series(self.storage_path, connectivity,
surface, region_map,
region_volume_map)
self.log.debug("Monitor %s created the TS %s" % (m_name, ts))
# Now check if the monitor will return results for each state variable, in which case store
# the labels for these state variables.
# todo move these into monitors as well
# and replace check if ts.user_tag_1 with something better (e.g. pre_ex & post)
state_variable_dimension_name = ts.labels_ordering[1]
if ts.user_tag_1:
ts.labels_dimensions[
state_variable_dimension_name] = ts.user_tag_1.split(';')
elif m_name in self.HAVE_STATE_VARIABLES:
selected_vois = [
self.algorithm.model.variables_of_interest[idx]
for idx in monitor.voi
]
ts.labels_dimensions[
state_variable_dimension_name] = selected_vois
ts.start_time = start_time
result_datatypes[m_name] = ts
#### Create Simulator State entity and persist it in DB. H5 file will be empty now.
if not self._is_group_launch():
simulation_state = SimulationState(storage_path=self.storage_path)
self._capture_operation_results([simulation_state])
### Run simulation
self.log.debug("%s: Starting simulation..." % str(self))
for result in self.algorithm(simulation_length=simulation_length):
for j, monitor in enumerate(monitors):
if result[j] is not None:
result_datatypes[monitor].write_time_slice([result[j][0]])
result_datatypes[monitor].write_data_slice([result[j][1]])
self.log.debug(
"%s: Completed simulation, starting to store simulation state " %
str(self))
### Populate H5 file for simulator state. This step could also be done while running sim, in background.
if not self._is_group_launch():
simulation_state.populate_from(self.algorithm)
self._capture_operation_results([simulation_state])
self.log.debug("%s: Simulation state persisted, returning results " %
str(self))
final_results = []
for result in result_datatypes.values():
result.close_file()
final_results.append(result)
self.log.info("%s: Adapter simulation finished!!" % str(self))
return final_results
def _validate_model_parameters(self, model_instance, connectivity,
surface):
"""
Checks if the size of the model parameters is set correctly.
"""
ui_configurable_params = model_instance.ui_configurable_parameters
for param in ui_configurable_params:
param_value = eval('model_instance.' + param)
if isinstance(param_value, numpy.ndarray):
if len(param_value) == 1 or connectivity is None:
continue
if surface is not None:
if (len(param_value) != surface.number_of_vertices
and len(param_value) !=
connectivity.number_of_regions):
msg = str(surface.number_of_vertices) + ' or ' + str(
connectivity.number_of_regions)
msg = self._get_exception_message(
param, msg, len(param_value))
self.log.error(msg)
raise LaunchException(msg)
elif len(param_value) != connectivity.number_of_regions:
msg = self._get_exception_message(
param, connectivity.number_of_regions,
len(param_value))
self.log.error(msg)
raise LaunchException(msg)
@staticmethod
def _get_exception_message(param_name, expected_size, actual_size):
"""
Creates the message that will be displayed to the user when the size of a model parameter is incorrect.
"""
msg = "The length of the parameter '" + param_name + "' is not correct."
msg += " It is expected to be an array of length " + str(
expected_size) + "."
msg += " It is an array of length " + str(actual_size) + "."
return msg
@staticmethod
def _is_surface_simulation(surface, surface_parameters):
"""
Is this a surface simulation?
"""
return surface is not None and surface_parameters is not None
class SimulatorAdapter(ABCAsynchronous):
"""
Interface between the Simulator and the Framework.
"""
_ui_name = "Simulation Core"
algorithm = None
available_models = get_traited_subclasses(Model)
available_monitors = get_traited_subclasses(Monitor)
available_integrators = get_traited_subclasses(Integrator)
available_couplings = get_traited_subclasses(Coupling)
available_noise = get_traited_subclasses(Noise)
### Info: This are the possible results returned with this adapter from different Monitors.
### When a list appears(surface & region), we actually return only one based on param surface being None or not.
# MONITOR_RESULTS = {"Raw": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
# "SubSample": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
# "SpatialAverage": time_series.TimeSeries,
# "GlobalAverage": time_series.TimeSeries,
# "TemporalAverage": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
# "EEG": time_series.TimeSeriesEEG,
# "SphericalEEG": time_series.TimeSeriesEEG,
# "SphericalMEG": time_series.TimeSeriesMEG,
# "Bold": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface]}
RESULTS_MAP = {time_series.TimeSeriesEEG: ["SphericalEEG", "EEG"],
time_series.TimeSeriesMEG: ["SphericalMEG"], # Add here also "MEG" monitor reference
time_series.TimeSeries: ["GlobalAverage", "SpatialAverage"]}
# time_series.TimeSeriesVolume: ["Bold"],
#SK: For a number of reasons, it's probably best to avoid returning TimeSeriesVolume ,
# from a simulation directly, instead just stick with the source, i.e. Region and Surface,
# then later we can add a voxelisation "analyser" to produce TimeSeriesVolume on which Volume
# based analysers and visualisers (which don't exist yet) can operate.
# This is a list with the monitors that actually return multi dimensions for the state variable dimension.
# We exclude from this for example EEG, MEG or Bold which return
HAVE_STATE_VARIABLES = ["GlobalAverage", "SpatialAverage", "Raw", "SubSample", "TemporalAverage"]
def __init__(self):
super(SimulatorAdapter, self).__init__()
self.log.debug("%s: Initialized..." % str(self))
def get_input_tree(self):
"""
Return a list of lists describing the interface to the simulator. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
sim = Simulator()
sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
result = sim.interface[self.INTERFACE_ATTRIBUTES]
# We should add as hidden the Simulator State attribute.
result.append({'name': 'simulation_state', 'type': SimulationState, 'required': False, 'ui_hidden': True})
return result
def get_output(self):
"""
:returns: list of classes for possible results of the Simulator.
"""
return [time_series.TimeSeries]
def configure(self, model, model_parameters, integrator, integrator_parameters, connectivity,
monitors, monitors_parameters=None, surface=None, surface_parameters=None, stimulus=None,
coupling=None, coupling_parameters=None, initial_conditions=None,
conduction_speed=None, simulation_length=0, simulation_state=None):
"""
Make preparations for the adapter launch.
"""
self.log.debug("available_couplings: %s..." % str(self.available_couplings))
self.log.debug("coupling: %s..." % str(coupling))
self.log.debug("coupling_parameters: %s..." % str(coupling_parameters))
self.log.debug("%s: Initializing Model..." % str(self))
noise_framework.build_noise(model_parameters)
model_instance = self.available_models[str(model)](**model_parameters)
self._validate_model_parameters(model_instance, connectivity, surface)
self.log.debug("%s: Initializing Integration scheme..." % str(self))
noise_framework.build_noise(integrator_parameters)
integr = self.available_integrators[integrator](**integrator_parameters)
self.log.debug("%s: Instantiating Monitors..." % str(self))
monitors_list = []
for monitor_name in monitors:
if (monitors_parameters is not None) and (str(monitor_name) in monitors_parameters):
monitors_list.append(self.available_monitors[str(monitor_name)
](**monitors_parameters[str(monitor_name)]))
else:
### We have monitors without any UI settable parameter.
monitors_list.append(self.available_monitors[str(monitor_name)]())
if len(monitors) < 1:
raise LaunchException("Can not launch operation without monitors selected !!!")
self.log.debug("%s: Initializing Coupling..." % str(self))
coupling_inst = self.available_couplings[str(coupling)](**coupling_parameters)
self.log.debug("Initializing Cortex...")
if surface is not None and surface_parameters is not None:
cortex_entity = Cortex(use_storage=False).populate_cortex(surface, surface_parameters)
if cortex_entity.region_mapping_data.connectivity.number_of_regions != connectivity.number_of_regions:
raise LaunchException("Incompatible RegionMapping -- Connectivity !!")
if cortex_entity.region_mapping_data.surface.number_of_vertices != surface.number_of_vertices:
raise LaunchException("Incompatible RegionMapping -- Surface !!")
select_loc_conn = cortex_entity.local_connectivity
if select_loc_conn is not None and select_loc_conn.surface.number_of_vertices != surface.number_of_vertices:
raise LaunchException("Incompatible LocalConnectivity -- Surface !!")
else:
cortex_entity = None
self.log.debug("%s: Instantiating requested simulator..." % str(self))
connectivity.configure()
self.algorithm = Simulator(connectivity=connectivity, coupling=coupling_inst, surface=cortex_entity,
stimulus=stimulus, model=model_instance, integrator=integr,
monitors=monitors_list, initial_conditions=initial_conditions,
conduction_speed=conduction_speed)
self.simulation_length = simulation_length
self.log.debug("%s: Initializing storage..." % str(self))
try:
self.algorithm.configure()
if simulation_state is not None:
simulation_state.fill_into(self.algorithm)
except ValueError, err:
raise LaunchException("Failed to configure simulator due to invalid Input Values. It could be because "
"of an incompatibility between different version of TVB code.", err)
import numpy
import itertools
from tvb.tests.library.base_testcase import BaseTestCase
from tvb.simulator.common import get_logger
from tvb.simulator import simulator, models, coupling, integrators, monitors, noise
from tvb.datatypes.connectivity import Connectivity
from tvb.datatypes.cortex import Cortex
from tvb.datatypes.local_connectivity import LocalConnectivity
from tvb.datatypes.region_mapping import RegionMapping
from tvb.basic.traits.parameters_factory import get_traited_subclasses
from tvb.datatypes.projections import ProjectionMatrix
from tvb.datatypes.sensors import SensorsEEG, SensorsMEG
LOG = get_logger(__name__)
AVAILABLE_MODELS = get_traited_subclasses(models.Model)
AVAILABLE_METHODS = get_traited_subclasses(integrators.Integrator)
MODEL_CLASSES = AVAILABLE_MODELS.values()
METHOD_NAMES = AVAILABLE_METHODS.keys()
METHOD_NAMES.append('RungeKutta4thOrderDeterministic')
class Simulator(object):
"""
Simulator test class
"""
def __init__(self):
"""
Initialise the structural information, coupling function, and monitors.
class SimulatorAdapter(ABCAsynchronous):
"""
Interface between the Simulator and the Framework.
"""
_ui_name = "Simulation Core"
algorithm = None
available_models = get_traited_subclasses(Model)
available_monitors = get_traited_subclasses(Monitor)
available_integrators = get_traited_subclasses(Integrator)
available_couplings = get_traited_subclasses(Coupling)
# This is a list with the monitors that actually return multi dimensions for the state variable dimension.
# We exclude from this for example EEG, MEG or Bold which return
HAVE_STATE_VARIABLES = [
"GlobalAverage", "SpatialAverage", "Raw", "SubSample",
"TemporalAverage"
]
def __init__(self):
super(SimulatorAdapter, self).__init__()
self.log.debug("%s: Initialized..." % str(self))
def get_input_tree(self):
"""
Return a list of lists describing the interface to the simulator. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
sim = Simulator()
sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
result = sim.interface[self.INTERFACE_ATTRIBUTES]
# We should add as hidden the Simulator State attribute.
result.append({
self.KEY_NAME: 'simulation_state',
self.KEY_TYPE: SimulationState,
self.KEY_LABEL: "Continuation of",
self.KEY_REQUIRED: False,
self.KEY_UI_HIDE: True
})
return result
def get_output(self):
"""
:returns: list of classes for possible results of the Simulator.
"""
return [time_series.TimeSeries]
def configure(self,
model,
model_parameters,
integrator,
integrator_parameters,
connectivity,
monitors,
monitors_parameters=None,
surface=None,
surface_parameters=None,
stimulus=None,
coupling=None,
coupling_parameters=None,
initial_conditions=None,
conduction_speed=None,
simulation_length=0,
simulation_state=None):
"""
Make preparations for the adapter launch.
"""
self.log.debug("available_couplings: %s..." %
str(self.available_couplings))
self.log.debug("coupling: %s..." % str(coupling))
self.log.debug("coupling_parameters: %s..." % str(coupling_parameters))
self.log.debug("%s: Initializing Model..." % str(self))
noise_framework.build_noise(model_parameters)
model_instance = self.available_models[str(model)](**model_parameters)
self._validate_model_parameters(model_instance, connectivity, surface)
self.log.debug("%s: Initializing Integration scheme..." % str(self))
noise_framework.build_noise(integrator_parameters)
integr = self.available_integrators[integrator](
**integrator_parameters)
self.log.debug("%s: Instantiating Monitors..." % str(self))
monitors_list = []
for monitor_name in monitors:
if (monitors_parameters is not None) and (str(monitor_name)
in monitors_parameters):
current_monitor_parameters = monitors_parameters[str(
monitor_name)]
HRFKernelEquation.build_equation_from_dict(
'hrf_kernel', current_monitor_parameters, True)
monitors_list.append(
self.available_monitors[str(monitor_name)](
**current_monitor_parameters))
else:
### We have monitors without any UI settable parameter.
monitors_list.append(
self.available_monitors[str(monitor_name)]())
if len(monitors) < 1:
raise LaunchException(
"Can not launch operation without monitors selected !!!")
self.log.debug("%s: Initializing Coupling..." % str(self))
coupling_inst = self.available_couplings[str(coupling)](
**coupling_parameters)
self.log.debug("Initializing Cortex...")
if self._is_surface_simulation(surface, surface_parameters):
cortex_entity = Cortex(use_storage=False).populate_cortex(
surface, surface_parameters)
if cortex_entity.region_mapping_data.connectivity.number_of_regions != connectivity.number_of_regions:
raise LaunchException(
"Incompatible RegionMapping -- Connectivity !!")
if cortex_entity.region_mapping_data.surface.number_of_vertices != surface.number_of_vertices:
raise LaunchException(
"Incompatible RegionMapping -- Surface !!")
select_loc_conn = cortex_entity.local_connectivity
if select_loc_conn is not None and select_loc_conn.surface.number_of_vertices != surface.number_of_vertices:
raise LaunchException(
"Incompatible LocalConnectivity -- Surface !!")
else:
cortex_entity = None
self.log.debug("%s: Instantiating requested simulator..." % str(self))
if conduction_speed not in (0.0, None):
connectivity.speed = numpy.array([conduction_speed])
else:
raise LaunchException("conduction speed cannot be 0 or missing")
self.algorithm = Simulator(connectivity=connectivity,
coupling=coupling_inst,
surface=cortex_entity,
stimulus=stimulus,
model=model_instance,
integrator=integr,
monitors=monitors_list,
initial_conditions=initial_conditions,
conduction_speed=conduction_speed)
self.simulation_length = simulation_length
self.log.debug("%s: Initializing storage..." % str(self))
try:
self.algorithm.preconfigure()
except ValueError, err:
raise LaunchException(
"Failed to configure simulator due to invalid Input Values. It could be because "
"of an incompatibility between different version of TVB code.",
err)
#SpecificTestModel = TestModel
#SpecificTestModel.__bases__ = (BaseModel,)
#tester = SpecificTestModel(**kwargs)
tester = TestModel(model = BaseModel(**kwargs))
tester.configure()
tester.model.configure_initial(1.0, (1, tester.model.nvar, 1, tester.model.number_of_modes))
return tester
if __name__ == '__main__':
# Do some stuff that tests or makes use of this module...
LOG.info("Generating phase-plane images for tvb.simulator.models...")
from tvb.basic.traits.parameters_factory import get_traited_subclasses
AVAILABLE_MODELS = get_traited_subclasses(models.Model)
##AVAILABLE_MODELS = {models.LarterBreakspear.__name__: models.LarterBreakspear}
MODEL_NAMES = AVAILABLE_MODELS.keys()
for model_name in MODEL_NAMES:
LOG.info("Generating phase-planes for %s" % model_name)
tester = test_factory(AVAILABLE_MODELS[model_name])
tester.pplane()
### EoF ###
