def build_simulator(self, n=4):
self.conn = numpy.zeros((n, n)) # , numpy.int32)
for i in range(self.conn.shape[0] - 1):
self.conn[i, i + 1] = 1
self.dist = numpy.r_[:n * n].reshape((n, n))
self.dist = numpy.array(self.dist, dtype=float)
self.sim = Simulator(
conduction_speed=1.0,
coupling=IdCoupling(),
surface=None,
stimulus=None,
integrator=Identity(dt=1.0),
initial_conditions=numpy.ones((n * n, 1, n, 1)),
simulation_length=10.0,
connectivity=Connectivity(region_labels=numpy.array(['']),
weights=self.conn,
tract_lengths=self.dist,
speed=numpy.array([1.0]),
centres=numpy.array([0.0])),
model=Sum(),
monitors=(Raw(), ),
)
self.sim.configure()
def test_server_fire_simulation(self, mocker, connectivity_factory):
input_folder = self.files_helper.get_project_folder(self.test_project)
sim_dir = os.path.join(input_folder, 'test_sim')
if not os.path.isdir(sim_dir):
os.makedirs(sim_dir)
simulator = Simulator()
simulator.connectivity = connectivity_factory()
sim_serializer = SimulatorSerializer()
sim_serializer.serialize_simulator(simulator, simulator.gid.hex, None,
sim_dir)
zip_filename = shutil.make_archive(sim_dir, 'zip', input_folder)
# Mock flask.request.files to return a dictionary
request_mock = mocker.patch.object(flask, 'request')
fp = open(zip_filename, 'rb')
request_mock.files = {
'file': FileStorage(fp, os.path.basename(zip_filename))
}
def launch_sim(self, user_id, project, algorithm, zip_folder_path,
simulator_file):
return Operation('', '', '', {})
# Mock simulation launch
mocker.patch.object(SimulatorService, 'prepare_simulation_on_server',
launch_sim)
operation_gid, status = self.simulation_resource.post(
self.test_project.gid)
fp.close()
assert type(operation_gid) is str
assert status == 201
def _create_sim(self, integrator=None, inhom_mmpr=False, delays=False,
run_sim=True):
mpr = MontbrioPazoRoxin()
conn = Connectivity.from_file()
if inhom_mmpr:
dispersion = 1 + np.random.randn(conn.weights.shape[0])*0.1
mpr = MontbrioPazoRoxin(eta=mpr.eta*dispersion)
conn.speed = np.r_[3.0 if delays else np.inf]
if integrator is None:
dt = 0.01
integrator = EulerDeterministic(dt=dt)
else:
dt = integrator.dt
sim = Simulator(connectivity=conn, model=mpr, integrator=integrator,
monitors=[Raw()],
simulation_length=0.1) # 10 steps
sim.configure()
if not delays:
self.assertTrue((conn.idelays == 0).all())
buf = sim.history.buffer[...,0]
# kernel has history in reverse order except 1st element 🤕
rbuf = np.concatenate((buf[0:1], buf[1:][::-1]), axis=0)
state = np.transpose(rbuf, (1, 0, 2)).astype('f')
self.assertEqual(state.shape[0], 2)
self.assertEqual(state.shape[2], conn.weights.shape[0])
if isinstance(sim.integrator, IntegratorStochastic):
sim.integrator.noise.reset_random_stream()
if run_sim:
(t,y), = sim.run()
return sim, state, t, y
else:
return sim
def test_shape(self):
# try to avoid introspector picking up this model
Gen2D = copy.deepcopy(models.Generic2dOscillator)
class CouplingShapeTestModel(Gen2D):
def __init__(self, test_case=None, n_node=None, **kwds):
super(CouplingShapeTestModel, self).__init__(**kwds)
self.cvar = numpy.r_[0, 1]
self.n_node = n_node
self.test_case = test_case
def dfun(self, state, coupling, local_coupling):
if self.test_case is not None:
self.test_case.assert_equal((2, self.n_node, 1),
coupling.shape)
return state
conn = connectivity.Connectivity.from_file()
surf = cortex.Cortex.from_file()
surf.region_mapping_data.connectivity = conn
sim = Simulator(model=CouplingShapeTestModel(self,
surf.vertices.shape[0]),
connectivity=conn,
surface=surf)
sim.configure()
for _ in sim(simulation_length=sim.integrator.dt * 2):
pass
def test_experimental_similar_to_default(self):
sim = Simulator()
sim.trait.bound = 'attributes-only'
itree = sim.interface['attributes']
sim2 = Simulator()
sim2.trait.bound = 'attributes-only'
itree_exp = sim2.interface_experimental
self._cmp_attributes_or_options(itree_exp, itree)
def deserialize_simulator(simulator_gid, storage_path):
simulator_in_path = h5.path_for(storage_path, SimulatorH5,
simulator_gid)
simulator_in = Simulator()
with SimulatorH5(simulator_in_path) as simulator_in_h5:
simulator_in_h5.load_into(simulator_in)
connectivity_gid = simulator_in_h5.connectivity.load()
stimulus_gid = simulator_in_h5.stimulus.load()
simulation_state_gid = simulator_in_h5.simulation_state.load()
conn_index = dao.get_datatype_by_gid(connectivity_gid.hex)
conn = h5.load_from_index(conn_index)
simulator_in.connectivity = conn
if simulator_in.surface:
cortex_path = h5.path_for(storage_path, CortexH5,
simulator_in.surface.gid)
with CortexH5(cortex_path) as cortex_h5:
local_conn_gid = cortex_h5.local_connectivity.load()
region_mapping_gid = cortex_h5.region_mapping_data.load()
region_mapping_index = dao.get_datatype_by_gid(
region_mapping_gid.hex)
region_mapping_path = h5.path_for_stored_index(
region_mapping_index)
region_mapping = RegionMapping()
with RegionMappingH5(region_mapping_path) as region_mapping_h5:
region_mapping_h5.load_into(region_mapping)
region_mapping.gid = region_mapping_h5.gid.load()
surf_gid = region_mapping_h5.surface.load()
surf_index = dao.get_datatype_by_gid(surf_gid.hex)
surf_h5 = h5.h5_file_for_index(surf_index)
surf = CorticalSurface()
surf_h5.load_into(surf)
surf_h5.close()
region_mapping.surface = surf
simulator_in.surface.region_mapping_data = region_mapping
if local_conn_gid:
local_conn_index = dao.get_datatype_by_gid(local_conn_gid.hex)
local_conn = h5.load_from_index(local_conn_index)
simulator_in.surface.local_connectivity = local_conn
if stimulus_gid:
stimulus_index = dao.get_datatype_by_gid(stimulus_gid.hex)
stimulus = h5.load_from_index(stimulus_index)
simulator_in.stimulus = stimulus
return simulator_in, simulation_state_gid
def test_models_list(self, mocker):
models_form = SimulatorModelFragment()
simulator = Simulator()
simulator.model = ModelsEnum.EPILEPTOR.instance
models_form.fill_from_trait(simulator)
rendering_rules = SimulatorFragmentRenderingRules(is_model_fragment=True)
soup = self.prepare_simulator_form_for_search(mocker, rendering_rules, form=models_form)
select_field = soup.find_all('select')
assert len(select_field) == 1, 'Number of select inputs is different than 1'
select_field_options = soup.find_all('option')
assert len(select_field_options) == len(ModelsEnum), 'Number of select field options != number of models'
select_field_choice = soup.find_all('option', selected=True)
assert len(select_field_choice) == 1
assert 'Epileptor' in select_field_choice[0].attrs['value']
def test_export_simulator_configuration(self, operation_factory):
"""
Test export of a simulator configuration
"""
operation = operation_factory()
simulator = Simulator()
simulator_index = SimulatorIndex()
simulator_index.fill_from_has_traits(simulator)
simulator_index.fk_from_operation = operation.id
simulator_index = dao.store_entity(simulator_index)
burst_configuration = BurstConfiguration(self.test_project.id,
simulator_index.id)
burst_configuration = dao.store_entity(burst_configuration)
simulator_index.fk_parent_burst = burst_configuration.id
simulator_index = dao.store_entity(simulator_index)
simulator_h5 = h5.path_for_stored_index(simulator_index)
with SimulatorH5(simulator_h5) as h5_file:
h5_file.store(simulator)
export_file = self.export_manager.export_simulator_configuration(
burst_configuration.id)
assert export_file is not None, "Export process should return path to export file"
assert os.path.exists(
export_file
), "Could not find export file: %s on disk." % export_file
assert zipfile.is_zipfile(
export_file), "Generated file is not a valid ZIP file"
def test_models_list(self):
all_models_for_ui = get_ui_name_to_model()
models_form = SimulatorModelFragment()
simulator = Simulator()
simulator.model = ModelsEnum.EPILEPTOR.get_class()()
models_form.fill_from_trait(simulator)
html = str(models_form)
soup = BeautifulSoup(html)
select_field = soup.find_all('select')
assert len(select_field) == 1, 'Number of select inputs is different than 1'
select_field_options = soup.find_all('option')
assert len(select_field_options) == len(all_models_for_ui), 'Number of select field options != number of models'
select_field_choice = soup.find_all('option', selected=True)
assert len(select_field_choice) == 1
assert 'Epileptor' in select_field_choice[0].attrs['value']
def test(dt=0.1, noise_strength=0.001, config=CONFIGURED):
# Select the regions for the fine scale modeling with NEST spiking networks
nest_nodes_ids = [] # the indices of fine scale regions modeled with NEST
# In this example, we model parahippocampal cortices (left and right) with NEST
connectivity = Connectivity.from_file(CONFIGURED.DEFAULT_CONNECTIVITY_ZIP)
for id in range(connectivity.region_labels.shape[0]):
if connectivity.region_labels[id].find("hippo") > 0:
nest_nodes_ids.append(id)
connectivity.configure()
# Create a TVB simulator and set all desired inputs
# (connectivity, model, surface, stimuli etc)
# We choose all defaults in this example
simulator = Simulator()
simulator.integrator.dt = dt
simulator.integrator.noise.nsig = np.array([noise_strength])
simulator.model = ReducedWongWangExcIOInhI()
simulator.connectivity = connectivity
mon_raw = Raw(period=simulator.integrator.dt)
simulator.monitors = (mon_raw, )
# Build a NEST network model with the corresponding builder
# Using all default parameters for this example
nest_model_builder = RedWWExcIOInhIMultisynapseBuilder(simulator,
nest_nodes_ids,
config=config)
nest_model_builder.configure()
for prop in [
"min_delay", "tvb_dt", "tvb_model", "tvb_connectivity",
"tvb_weights", "tvb_delays", "number_of_nodes",
"number_of_spiking_nodes", "spiking_nodes_labels",
"number_of_populations", "populations_models", "populations_nodes",
"populations_scales", "populations_sizes", "populations_params",
"populations_connections_labels", "populations_connections_models",
"populations_connections_nodes", "populations_connections_weights",
"populations_connections_delays",
"populations_connections_receptor_types",
"populations_connections_conn_spec", "nodes_connections_labels",
"nodes_connections_models", "nodes_connections_source_nodes",
"nodes_connections_target_nodes", "nodes_connections_weights",
"nodes_connections_delays", "nodes_connections_receptor_types",
"nodes_connections_conn_spec"
]:
print("%s:\n%s\n\n" % (prop, str(getattr(nest_model_builder, prop))))
def _prep_sim(self, coupling) -> Simulator:
"Prepare simulator for testing a coupling function."
con = Connectivity.from_file()
con.weights[:] = 1.0
# con = Connectivity(
# region_labels=np.array(['']),
# weights=con.weights[:5][:,:5],
# tract_lengths=con.tract_lengths[:5][:,:5],
# speed=np.array([10.0]),
# centres=np.array([0.0]))
sim = Simulator(connectivity=con,
model=LinearModel(gamma=np.r_[0.0]),
coupling=coupling,
integrator=Identity(dt=1.0),
monitors=[Raw()],
simulation_length=0.5)
sim.configure()
return sim
def prepare_simulator_form_for_search(self, dict_to_render):
sim_adapter_form = SimulatorAdapterForm(project_id=1)
sim_adapter_form.fill_from_trait(Simulator())
dict_to_render[SimulatorController.FORM_KEY] = sim_adapter_form
html = self.dummy_renderer(dict_to_render)
soup = BeautifulSoup(html)
return soup
class TestsExactPropagation(BaseTestCase):
def build_simulator(self, n=4):
self.conn = numpy.zeros((n, n)) # , numpy.int32)
for i in range(self.conn.shape[0] - 1):
self.conn[i, i + 1] = 1
self.dist = numpy.r_[:n * n].reshape((n, n))
self.dist = numpy.array(self.dist, dtype=float)
self.sim = Simulator(
conduction_speed=1.0,
coupling=IdCoupling(),
surface=None,
stimulus=None,
integrator=Identity(dt=1.0),
initial_conditions=numpy.ones((n * n, 1, n, 1)),
simulation_length=10.0,
connectivity=Connectivity(region_labels=numpy.array(['']),
weights=self.conn,
tract_lengths=self.dist,
speed=numpy.array([1.0]),
centres=numpy.array([0.0])),
model=Sum(),
monitors=(Raw(), ),
)
self.sim.configure()
def test_propagation(self):
n = 4
self.build_simulator(n=n)
# x = numpy.zeros((n, ))
xs = []
for (t, raw), in self.sim(simulation_length=10):
xs.append(raw.flat[:].copy())
xs = numpy.array(xs)
xs_ = numpy.array([[2., 2., 2., 1.], [3., 3., 3.,
1.], [5., 4., 4., 1.],
[8., 5., 5., 1.], [12., 6., 6., 1.],
[17., 7., 7., 1.], [23., 8., 8., 1.],
[30., 10., 9., 1.], [38., 13., 10., 1.],
[48., 17., 11., 1.]])
assert numpy.allclose(xs, xs_)
def test(dt=0.1, noise_strength=0.001, config=CONFIGURED):
# Select the regions for the fine scale modeling with ANNarchy spiking networks
anarchy_nodes_ids = list(
range(10)) # the indices of fine scale regions modeled with ANNarchy
# In this example, we model parahippocampal cortices (left and right) with ANNarchy
connectivity = Connectivity.from_file(CONFIGURED.DEFAULT_CONNECTIVITY_ZIP)
connectivity.configure()
# Create a TVB simulator and set all desired inputs
# (connectivity, model, surface, stimuli etc)
# We choose all defaults in this example
simulator = Simulator()
simulator.integrator.dt = dt
# simulator.integrator.noise.nsig = np.array([noise_strength])
simulator.model = ReducedWongWangExcIOInhI()
simulator.connectivity = connectivity
mon_raw = Raw(period=simulator.integrator.dt)
simulator.monitors = (mon_raw, )
# Build a ANNarchy network model with the corresponding builder
# Using all default parameters for this example
anarchy_model_builder = BasalGangliaIzhikevichBuilder(simulator,
anarchy_nodes_ids,
config=config)
anarchy_model_builder.configure()
for prop in [
"min_delay", "tvb_dt", "tvb_model", "tvb_connectivity",
"tvb_weights", "tvb_delays", "number_of_nodes",
"number_of_spiking_nodes", "spiking_nodes_labels",
"number_of_populations", "populations_models", "populations_nodes",
"populations_scales", "populations_sizes", "populations_params",
"populations_connections_labels", "populations_connections_models",
"populations_connections_nodes", "populations_connections_weights",
"populations_connections_delays",
"populations_connections_receptor_types",
"populations_connections_conn_spec", "nodes_connections_labels",
"nodes_connections_models", "nodes_connections_source_nodes",
"nodes_connections_target_nodes", "nodes_connections_weights",
"nodes_connections_delays", "nodes_connections_receptor_types",
"nodes_connections_conn_spec"
]:
print("%s:\n%s\n\n" %
(prop, str(getattr(anarchy_model_builder, prop))))
def prepare_simulator_form_for_search(self, rendering_rules, form=None):
# type: (SimulatorFragmentRenderingRules, ABCAdapterForm) -> BeautifulSoup
if form is None:
form = SimulatorAdapterForm(project_id=1)
form.fill_from_trait(Simulator())
rendering_rules.form = form
html = self.dummy_renderer(rendering_rules.to_dict())
soup = BeautifulSoup(html)
return soup
def _tvb_sim(self, dt):
import numpy as np
from tvb.simulator.simulator import Simulator, models, connectivity, monitors
sim = Simulator(
connectivity=connectivity.Connectivity.from_file(),
model=models.MontbrioPazoRoxin(I=np.r_[1.0],
Delta=np.r_[1.0],
eta=np.r_[-5.0],
tau=np.r_[100.0],
J=np.r_[15.],
cr=np.r_[0.01],
cv=np.r_[0.0]),
integrator=self._integrator(dt=dt),
monitors=[monitors.Raw()],
).configure()
assert sim.history.buffer.shape == (513, 2, 76, 1)
sim.history.buffer[:, 0] = 0.0
sim.history.buffer[:, 1] = -2.0
sim.current_state[:] = sim.history.buffer[0]
return sim
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 main_example(tvb_sim_model,
connectivity_zip=CONFIGURED.DEFAULT_CONNECTIVITY_ZIP,
dt=0.1,
noise_strength=0.001,
simulation_length=100.0,
config=CONFIGURED):
plotter = Plotter(config)
# --------------------------------------1. Load TVB connectivity----------------------------------------------------
connectivity = Connectivity.from_file(connectivity_zip)
connectivity.configure()
plotter.plot_tvb_connectivity(connectivity)
# ----------------------2. Define a TVB simulator (model, integrator, monitors...)----------------------------------
# Create a TVB simulator and set all desired inputs
# (connectivity, model, surface, stimuli etc)
# We choose all defaults in this example
simulator = Simulator()
simulator.integrator = HeunStochastic(dt=dt)
simulator.integrator.noise.nsig = np.array(ensure_list(noise_strength))
simulator.model = tvb_sim_model
simulator.connectivity = connectivity
mon_raw = Raw(period=simulator.integrator.dt)
simulator.monitors = (mon_raw, )
# -----------------------------------3. Simulate and gather results-------------------------------------------------
# Configure the simulator with the TVB-NEST interface...
# simulator.configure(tvb_nest_interface=tvb_nest_model)
simulator.configure()
# ...and simulate!
t_start = time.time()
results = simulator.run(simulation_length=simulation_length)
print("\nSimulated in %f secs!" % (time.time() - t_start))
# -------------------------------------------6. Plot results--------------------------------------------------------
plot_results(results, simulator, None, "State Variables",
simulator.model.variables_of_interest, plotter)
return connectivity, results
def test_server_fire_simulation(self, mocker, connectivity_factory):
self._mock_user(mocker)
input_folder = self.files_helper.get_project_folder(self.test_project)
sim_dir = os.path.join(input_folder, 'test_sim')
if not os.path.isdir(sim_dir):
os.makedirs(sim_dir)
simulator = Simulator()
simulator.connectivity = connectivity_factory()
sim_serializer = SimulatorSerializer()
sim_serializer.serialize_simulator(simulator, None, sim_dir)
zip_filename = os.path.join(input_folder,
RequestFileKey.SIMULATION_FILE_NAME.value)
FilesHelper().zip_folder(zip_filename, sim_dir)
# Mock flask.request.files to return a dictionary
request_mock = mocker.patch.object(flask, 'request')
fp = open(zip_filename, 'rb')
request_mock.files = {
RequestFileKey.SIMULATION_FILE_KEY.value:
FileStorage(fp, os.path.basename(zip_filename))
}
def launch_sim(self, user_id, project, algorithm, zip_folder_path,
simulator_file):
return Operation('', '', '', {})
# Mock simulation launch and current user
mocker.patch.object(SimulatorService, 'prepare_simulation_on_server',
launch_sim)
operation_gid, status = self.simulation_resource.post(
project_gid=self.test_project.gid)
fp.close()
assert type(operation_gid) is str
assert status == 201
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 transactional_setup_method(self):
self.test_user = TestFactory.create_user(username="******")
self.test_project = TestFactory.create_project(self.test_user, "Test")
zip_path = path.join(path.dirname(tvb_data.__file__), 'connectivity',
'connectivity_66.zip')
TestFactory.import_zip_connectivity(self.test_user, self.test_project,
zip_path, "John")
self.connectivity = TestFactory.get_entity(self.test_project,
ConnectivityIndex)
sim_conf = Simulator(model=ModelsEnum.HOPFIELD.get_class()(),
integrator=HeunStochastic())
self.s_manager = SerializationManager(sim_conf)
self.empty_manager = SerializationManager(None)
def fire_simulation(project_id=1, **kwargs):
project = dao.get_project_by_id(project_id)
# Prepare a Simulator instance with defaults and configure it to use the previously loaded Connectivity
simulator = Simulator(**kwargs)
# Load the SimulatorAdapter algorithm from DB
cached_simulator_algorithm = FlowService(
).get_algorithm_by_module_and_class(IntrospectionRegistry.SIMULATOR_MODULE,
IntrospectionRegistry.SIMULATOR_CLASS)
# Instantiate a SimulatorService and launch the configured simulation
simulator_service = SimulatorService()
launched_operation = simulator_service.async_launch_and_prepare_simulation(
None, project.administrator, project, cached_simulator_algorithm,
simulator, None)
return launched_operation
def prepare_simulator_form_for_search(self, mocker, rendering_rules, form=None):
# type: (MockerFixture, SimulatorFragmentRenderingRules, ABCAdapterForm) -> BeautifulSoup
if form is None:
form = SimulatorAdapterForm()
form.fill_from_trait(Simulator())
rendering_rules.form = form
def _is_online_help_active(self):
return True
def _get_logged_user():
return User('test', 'test', 'test')
mocker.patch('tvb.interfaces.web.controllers.common.get_logged_user', _get_logged_user)
mocker.patch.object(User, 'is_online_help_active', _is_online_help_active)
html = self.dummy_renderer(rendering_rules.to_dict())
soup = BeautifulSoup(html, features="html.parser")
return soup
def build(self, **model_params):
# Load, normalize and configure connectivity
if isinstance(self.connectivity, string_types):
connectivity = Connectivity.from_file(self.connectivity)
else:
connectivity = self.connectivity
if self.scale_connectivity_weights is not None:
if isinstance(self.scale_connectivity_weights, string_types):
connectivity.weights = connectivity.scaled_weights(
mode=self.scale_connectivity_weights)
else:
connectivity.weights /= self.scale_connectivity_weights
if not self.delays_flag:
connectivity.configure() # to set speed
# Given that
# idelays = numpy.rint(delays / dt).astype(numpy.int32)
# and delays = tract_lengths / speed
connectivity.tract_lengths = 0.1 * self.dt * connectivity.speed
connectivity.configure()
# Build model:
model = self.model(**model_params)
# Build integrator
integrator = self.integrator(dt=self.dt)
integrator.noise.nsig = np.array(ensure_list(self.noise_strength))
# Build monitors:
assert Raw in self.monitors
monitors = []
for monitor in self.monitors:
monitors.append(monitor(period=self.dt))
monitors = tuple(monitors)
# Build simulator
simulator = Simulator()
simulator._config = self.config
simulator.connectivity = connectivity
simulator.model = model
simulator.integrator = integrator
simulator.monitors = monitors
return simulator
def test_sim_interface(self):
"""
Test that the interface method returns all attributes required for the UI.
"""
sim = Simulator()
sim.trait.bound = 'attributes-only'
current_dict = sim.interface['attributes']
self.assertFalse(current_dict is None)
attr = self._get_attr_description(current_dict, 'monitors')
self.assertEquals('selectMultiple', attr['type'])
self.assertEqual('TemporalAverage', attr['default'])
attr = self._get_attr_description(current_dict, 'model')
self.assertEquals('select', attr['type'])
self.assertFalse('datatype' in attr)
self.assertTrue('options' in attr)
self.assertTrue(len(attr['options']) >= 5)
for model_attr in attr['options']:
self.assertTrue(model_attr['name'] is not None)
self.assertTrue(model_attr['value'] is not None)
self.assertTrue(model_attr['value'] in model_attr['class'])
self.assertTrue(len(model_attr['attributes']) >= 3)
attr = self._get_attr_description(current_dict, 'connectivity')
self.assertTrue(attr['datatype'])
self.assertEqual(Connectivity.__module__ + "." + Connectivity.__name__,
attr['type'])
attr = self._get_attr_description(current_dict, 'surface')
self.assertTrue(attr['datatype'])
self.assertEqual(
CorticalSurface.__module__ + "." + CorticalSurface.__name__,
attr['type'])
attr = self._get_attr_description(current_dict, 'conduction_speed')
self.assertEquals(attr['type'], 'float')
attr = self._get_attr_description(current_dict, 'simulation_length')
self.assertEquals(attr['type'], 'float')
self.assertEquals(attr['default'], 1000.0)
self._validate_list(current_dict)
def test_sim_interface(self):
"""
Test that the interface method returns all attributes required for the UI.
"""
sim = Simulator()
sim.trait.bound = 'attributes-only'
current_dict = sim.interface['attributes']
assert not current_dict is None
attr = self._get_attr_description(current_dict, 'monitors')
assert 'selectMultiple' == attr['type']
assert 'TemporalAverage' == attr['default']
attr = self._get_attr_description(current_dict, 'model')
assert 'select' == attr['type']
assert not 'datatype' in attr
assert 'options' in attr
assert len(attr['options']) >= 5
for model_attr in attr['options']:
assert model_attr['name'] is not None
assert model_attr['value'] is not None
assert model_attr['value'] in model_attr['class']
assert len(model_attr['attributes']) >= 3
attr = self._get_attr_description(current_dict, 'connectivity')
assert attr['datatype']
assert Connectivity.__module__ + "." + Connectivity.__name__ == attr[
'type']
attr = self._get_attr_description(current_dict, 'surface')
assert attr['datatype']
assert CorticalSurface.__module__ + "." + CorticalSurface.__name__ == attr[
'type']
attr = self._get_attr_description(current_dict, 'conduction_speed')
assert attr['type'] == 'float'
attr = self._get_attr_description(current_dict, 'simulation_length')
assert attr['type'] == 'float'
assert attr['default'] == 1000.0
self._validate_list(current_dict)
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)
### 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.TimeSeriesSEEG: ["SEEG"]}
# 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({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))
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)
def _prepare_simulator_from_view_model(self, view_model):
simulator = Simulator()
simulator.gid = view_model.gid
conn = self.load_traited_by_gid(view_model.connectivity)
simulator.connectivity = conn
simulator.conduction_speed = view_model.conduction_speed
simulator.coupling = view_model.coupling
rm_surface = None
if view_model.surface:
simulator.surface = Cortex()
rm_index = self.load_entity_by_gid(
view_model.surface.region_mapping_data.hex)
rm = h5.load_from_index(rm_index)
rm_surface_index = self.load_entity_by_gid(rm_index.fk_surface_gid)
rm_surface = h5.load_from_index(rm_surface_index, CorticalSurface)
rm.surface = rm_surface
rm.connectivity = conn
simulator.surface.region_mapping_data = rm
if simulator.surface.local_connectivity:
lc = self.load_traited_by_gid(
view_model.surface.local_connectivity)
assert lc.surface.gid == rm_index.fk_surface_gid
lc.surface = rm_surface
simulator.surface.local_connectivity = lc
if view_model.stimulus:
stimulus_index = self.load_entity_by_gid(view_model.stimulus.hex)
stimulus = h5.load_from_index(stimulus_index)
simulator.stimulus = stimulus
if isinstance(stimulus, StimuliSurface):
simulator.stimulus.surface = rm_surface
else:
simulator.stimulus.connectivity = simulator.connectivity
simulator.model = view_model.model
simulator.integrator = view_model.integrator
simulator.initial_conditions = view_model.initial_conditions
simulator.monitors = view_model.monitors
simulator.simulation_length = view_model.simulation_length
# TODO: why not load history here?
# if view_model.history:
# history_index = dao.get_datatype_by_gid(view_model.history.hex)
# history = h5.load_from_index(history_index)
# assert isinstance(history, SimulationHistory)
# history.fill_into(self.algorithm)
return simulator
def get_traited_datatype(self):
return Simulator()
def build(connectivity=None, simulation_length=100):
if not connectivity:
connectivity = connectivity_factory(2)
return Simulator(connectivity=connectivity,
surface=None,
simulation_length=simulation_length)
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
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_input_tree2(self):
sim = Simulator()
sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
result = sim.interface_experimental
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_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)