class Simulator(SimulatorTVB):
tvb_spikeNet_interface = None
configure_spiking_simulator = None
run_spiking_simulator = None
model = Attr(
field_type=models.Model,
label="Local dynamic model",
default=ReducedWongWangExcIOInhI(),
required=True,
doc="""A tvb.simulator.Model object which describes the local dynamic
equations, their parameters, and, to some extent, where connectivity
(local and long-range) enters and which state-variables the Monitors
monitor. By default the 'ReducedWongWang' model is used. Read the
Scientific documentation to learn more about this model.""")
monitors = List(
of=monitors.Monitor,
label="Monitor(s)",
default=(monitors.Raw(), ),
doc="""A tvb.simulator.Monitor or a list of tvb.simulator.Monitor
objects that 'know' how to record relevant data from the simulation. Two
main types exist: 1) simple, spatial and temporal, reductions (subsets
or averages); 2) physiological measurements, such as EEG, MEG and fMRI.
By default the Model's specified variables_of_interest are returned,
temporally downsampled from the raw integration rate to a sample rate of
1024Hz.""")
integrator = Attr(field_type=integrators.Integrator,
label="Integration scheme",
default=integrators.HeunStochastic(dt=0.001),
required=True,
doc="""A tvb.simulator.Integrator object which is
an integration scheme with supporting attributes such as
integration step size and noise specification for stochastic
methods. It is used to compute the time courses of the model state
variables.""")
connectivity = Attr(
field_type=connectivity.Connectivity,
label="Long-range connectivity",
default=CONFIGURED.DEFAULT_SUBJECT["connectivity"],
required=True,
doc="""A tvb.datatypes.Connectivity object which contains the
structural long-range connectivity data (i.e., white-matter tracts). In
combination with the ``Long-range coupling function`` it defines the inter-regional
connections. These couplings undergo a time delay via signal propagation
with a propagation speed of ``Conduction Speed``""")
@property
def config(self):
try:
return self.tvb_spikeNet_interface.config
except:
return CONFIGURED
def _configure_integrator_noise(self):
"""
This enables having noise to be state variable specific and/or to enter
only via specific brain structures, for example it we only want to
consider noise as an external input entering the brain via appropriate
thalamic nuclei.
Support 3 possible shapes:
1) number_of_nodes;
2) number_of_state_variables; and
3) (number_of_state_variables, number_of_nodes).
"""
if self.integrator.noise.ntau > 0.0:
LOG.warning(
"Colored noise is currently not supported for tvb-spikeNet simulations!\n"
+
"Setting integrator.noise.ntau = 0.0 and configuring white noise!"
)
self.integrator.noise.ntau = 0.0
super(Simulator, self)._configure_integrator_noise()
def bound_and_clamp(self, state):
# If there is a state boundary...
if self.integrator.state_variable_boundaries is not None:
# ...use the integrator's bound_state
self.integrator.bound_state(state)
# If there is a state clamping...
if self.integrator.clamped_state_variable_values is not None:
# ...use the integrator's clamp_state
self.integrator.clamp_state(state)
def _update_and_bound_history(self, history):
self.bound_and_clamp(history)
# If there are non-state variables, they need to be updated for history:
try:
# Assuming that node_coupling can have a maximum number of dimensions equal to the state variables,
# in the extreme case where all state variables are cvars as well, we set:
node_coupling = numpy.zeros(
(history.shape[0], 1, history.shape[2], 1))
for i_time in range(history.shape[1]):
self.model.update_non_state_variables(history[:, i_time],
node_coupling[:, 0], 0.0)
self.bound_and_clamp(history)
except:
pass
def _configure_history(self, initial_conditions):
"""
Set initial conditions for the simulation using either the provided
initial_conditions or, if none are provided, the model's initial()
method. This method is called durin the Simulator's __init__().
Any initial_conditions that are provided as an argument are expected
to have dimensions 1, 2, and 3 with shapse corresponding to the number
of state_variables, nodes and modes, respectively. If the provided
inital_conditions are shorter in time (dim=0) than the required history
the model's initial() method is called to make up the difference.
"""
rng = numpy.random
if hasattr(self.integrator, 'noise'):
rng = self.integrator.noise.random_stream
# Default initial conditions
if initial_conditions is None:
n_time, n_svar, n_node, n_mode = self.good_history_shape
LOG.info(
'Preparing initial history of shape %r using model.initial()',
self.good_history_shape)
if self.surface is not None:
n_node = self.number_of_nodes
history = self.model.initial(self.integrator.dt,
(n_time, n_svar, n_node, n_mode), rng)
# ICs provided
else:
# history should be [timepoints, state_variables, nodes, modes]
LOG.info('Using provided initial history of shape %r',
initial_conditions.shape)
n_time, n_svar, n_node, n_mode = ic_shape = initial_conditions.shape
nr = self.connectivity.number_of_regions
if self.surface is not None and n_node == nr:
initial_conditions = initial_conditions[:, :, self._regmap]
return self._configure_history(initial_conditions)
elif ic_shape[1:] != self.good_history_shape[1:]:
raise_value_error(
"Incorrect history sample shape %s, expected %s" %
ic_shape[1:], self.good_history_shape[1:])
else:
if ic_shape[0] >= self.horizon:
LOG.debug("Using last %d time-steps for history.",
self.horizon)
history = initial_conditions[
-self.horizon:, :, :, :].copy()
else:
LOG.debug('Padding initial conditions with model.initial')
history = self.model.initial(self.integrator.dt,
self.good_history_shape, rng)
shift = self.current_step % self.horizon
history = numpy.roll(history, -shift, axis=0)
history[:ic_shape[0], :, :, :] = initial_conditions
history = numpy.roll(history, shift, axis=0)
self.current_step += ic_shape[0] - 1
# Make sure that history values are bounded,
# and any possible non-state variables are initialized
# based on state variable ones (but with no coupling yet...)
self._update_and_bound_history(numpy.swapaxes(history, 0, 1))
LOG.info('Final initial history shape is %r', history.shape)
# create initial state from history
self.current_state = history[self.current_step % self.horizon].copy()
LOG.debug('initial state has shape %r' % (self.current_state.shape, ))
if self.surface is not None and history.shape[
2] > self.connectivity.number_of_regions:
n_reg = self.connectivity.number_of_regions
(nt, ns, _, nm), ax = history.shape, (2, 0, 1, 3)
region_history = numpy.zeros((nt, ns, n_reg, nm))
numpy_add_at(region_history.transpose(ax), self._regmap,
history.transpose(ax))
region_history /= numpy.bincount(self._regmap).reshape((-1, 1))
history = region_history
# create history query implementation
self.history = SparseHistory(self.connectivity.weights,
self.connectivity.idelays,
self.model.cvar,
self.model.number_of_modes)
# initialize its buffer
self.history.initialize(history)
def configure(self, tvb_spikeNet_interface, full_configure=True):
"""Configure simulator and its components.
The first step of configuration is to run the configure methods of all
the Simulator's components, ie its traited attributes.
Configuration of a Simulator primarily consists of calculating the
attributes, etc, which depend on the combinations of the Simulator's
traited attributes (keyword args).
Converts delays from physical time units into integration steps
and updates attributes that depend on combinations of the 6 inputs.
Returns
-------
sim: Simulator
The configured Simulator instance.
"""
if full_configure:
# When run from GUI, preconfigure is run separately, and we want to avoid running that part twice
self.preconfigure()
# Make sure spatialised model parameters have the right shape (number_of_nodes, 1)
self.tvb_spikeNet_interface = tvb_spikeNet_interface
# TODO: decide if this is really necessary...
if self.integrator.dt >= 2 * self.tvb_spikeNet_interface.spikeNet_min_delay:
self.integrator.dt = int(numpy.round(self.integrator.dt /
self.tvb_spikeNet_interface.spikeNet_min_delay)) * \
self.tvb_spikeNet_interface.spikeNet_min_delay
else:
raise_value_error(
"TVB integration time step dt=%f "
"is not equal or greater than twice the Spiking Network minimum delay min_delay=%f!"
% (self.integrator.dt,
self.tvb_spikeNet_interface.spikeNet_min_delay))
excluded_params = ("state_variable_range", "state_variable_boundaries",
"variables_of_interest", "noise", "psi_table",
"nerf_table", "gid")
spatial_reshape = self.model.spatial_param_reshape
for param in type(self.model).declarative_attrs:
if param in excluded_params:
continue
# If it's a surface sim and model parameters were provided at the region level
region_parameters = getattr(self.model, param)
if self.surface is not None:
if region_parameters.size == self.connectivity.number_of_regions:
new_parameters = region_parameters[
self.surface.region_mapping].reshape(spatial_reshape)
setattr(self.model, param, new_parameters)
region_parameters = getattr(self.model, param)
if hasattr(
region_parameters,
"size") and region_parameters.size == self.number_of_nodes:
new_parameters = region_parameters.reshape(spatial_reshape)
setattr(self.model, param, new_parameters)
# Configure spatial component of any stimuli
self._configure_stimuli()
# Set delays, provided in physical units, in integration steps.
self.connectivity.set_idelays(self.integrator.dt)
self.horizon = self.connectivity.idelays.max() + 1
# Reshape integrator.noise.nsig, if necessary.
if isinstance(self.integrator, integrators.IntegratorStochastic):
self._configure_integrator_noise()
# Configure Monitors to work with selected Model, etc...
self._configure_monitors()
# TODO: find out why the model instance is different in simulator and interface...
self.tvb_spikeNet_interface.configure(self.model)
dummy = -numpy.ones((self.connectivity.number_of_regions, ))
dummy[self.tvb_spikeNet_interface.spiking_nodes_ids] = 0.0
# Create TVB model parameter for SpikeNet to target
for param in self.tvb_spikeNet_interface.spikeNet_to_tvb_params:
setattr(self.model, param, dummy)
# Setup Spiking Simulator configure() and Run() method
self.configure_spiking_simulator = self.tvb_spikeNet_interface.spiking_network.configure
self.run_spiking_simulator = self.tvb_spikeNet_interface.spiking_network.Run
# If there are Spiking nodes and are represented exclusively in Spiking Network...
if self.tvb_spikeNet_interface.exclusive_nodes and len(
self.tvb_spikeNet_interface.spiking_nodes_ids) > 0:
# ...zero coupling interface_weights among Spiking nodes:
self.connectivity.weights[self.tvb_spikeNet_interface.spiking_nodes_ids] \
[:, self.tvb_spikeNet_interface.spiking_nodes_ids] = 0.0
# Setup history
# TODO: Reflect upon the idea to allow SpikeNet initialization and history setting via TVB
self._configure_history(self.initial_conditions)
# TODO: Shall we implement a parallel implentation for multiple modes for SpikeNet as well?!
if self.current_state.shape[2] > 1:
raise_value_error(
"Multiple modes' simulation not supported for TVB multiscale simulations!\n"
"Current modes number is %d." %
self.initial_conditions.shape[3])
# Estimate of memory usage.
self._census_memory_requirement()
return self
# TODO: update all those functions below to compute the fine scale requirements as well, ...if you can! :)
# used by simulator adaptor
def memory_requirement(self):
# TODO: calculate SpikeNet memory requirement as well!
"""
Return an estimated of the memory requirements (Bytes) for this
simulator's current configuration.
"""
return super(Simulator, self).memory_requirement()
# appears to be unused
def runtime(self, simulation_length):
# TODO: calculate SpikeNet runtime as well!
"""
Return an estimated run time (seconds) for the simulator's current
configuration and a specified simulation length.
"""
return super(Simulator, self).runtime(simulation_length)
# used by simulator adaptor
def storage_requirement(self, simulation_length):
# TODO: calculate SpikeNet storage requirement as well!
"""
Return an estimated storage requirement (Bytes) for the simulator's
current configuration and a specified simulation length.
"""
return super(Simulator, self).storage_requirement(simulation_length)
def update_state(self, state, node_coupling, local_coupling=0.0):
# If there are non-state variables, they need to be updated for the initial condition:
try:
self.model.update_non_state_variables(state, node_coupling,
local_coupling)
self.bound_and_clamp(state)
except:
# If not, the kwarg will fail and nothing will happen
pass
def __call__(self, simulation_length=None, random_state=None):
"""
Return an iterator which steps through simulation time, generating monitor outputs.
See the run method for a convenient way to collect all output in one call.
:param simulation_length: Length of the simulation to perform in ms.
:param random_state: State of NumPy RNG to use for stochastic integration.
:return: Iterator over monitor outputs.
"""
self.calls += 1
if simulation_length is not None:
self.simulation_length = simulation_length
# Intialization
self._guesstimate_runtime()
self._calculate_storage_requirement()
self._handle_random_state(random_state)
n_reg = self.connectivity.number_of_regions
local_coupling = self._prepare_local_coupling()
stimulus = self._prepare_stimulus()
state = self.current_state
# Do for initial condition:
step = self.current_step + 1 # the first step in the loop
node_coupling = self._loop_compute_node_coupling(step)
self._loop_update_stimulus(step, stimulus)
# This is not necessary in most cases
# if update_non_state_variables=True in the model dfun by default
self.update_state(state, node_coupling, local_coupling)
# spikeNet simulation preparation:
self.configure_spiking_simulator()
# A flag to skip unnecessary steps when Spiking Simulator does NOT update TVB state
updateTVBstateFromSpikeNet = len(
self.tvb_spikeNet_interface.spikeNet_to_tvb_sv_interfaces_ids) > 0
# integration loop
n_steps = int(math.ceil(self.simulation_length / self.integrator.dt))
tic = time.time()
tic_ratio = 0.1
tic_point = tic_ratio * n_steps
for step in range(self.current_step + 1,
self.current_step + n_steps + 1):
# TVB state -> SpikeNet (state or parameter)
# Communicate TVB state to some SpikeNet device (TVB proxy) or TVB coupling to SpikeNet nodes,
# including any necessary conversions from TVB state to SpikeNet variables,
# in a model specific manner
# TODO: find what is the general treatment of local coupling, if any!
# Is this addition correct in all cases for all builders?
self.tvb_spikeNet_interface.tvb_state_to_spikeNet(
state, node_coupling + local_coupling, stimulus, self.model)
# SpikeNet state -> TVB model parameter
# Couple the SpikeNet state to some TVB model parameter,
# including any necessary conversions in a model specific manner
self.model = self.tvb_spikeNet_interface.spikeNet_state_to_tvb_parameter(
self.model)
# Integrate TVB to get the new TVB state
state = self.integrator.scheme(state, self.model.dfun,
node_coupling, local_coupling,
stimulus)
if numpy.any(numpy.isnan(state)) or numpy.any(numpy.isinf(state)):
raise ValueError(
"NaN or Inf values detected in simulator state!:\n%s" %
str(state))
# Integrate Spiking Network to get the new Spiking Network state
self.run_spiking_simulator(self.integrator.dt)
if updateTVBstateFromSpikeNet:
# SpikeNet state -> TVB state
# Update the new TVB state variable with the new SpikeNet state,
# including any necessary conversions from SpikeNet variables to TVB state,
# in a model specific manner
state = self.tvb_spikeNet_interface.spikeNet_state_to_tvb_state(
state)
self.bound_and_clamp(state)
# Prepare coupling and stimulus for next time step
# and, therefore, for the new TVB state:
node_coupling = self._loop_compute_node_coupling(step)
self._loop_update_stimulus(step, stimulus)
# Update any non-state variables and apply any boundaries again to the new state:
self.update_state(state, node_coupling, local_coupling)
# Now direct the new state to history buffer and monitors
self._loop_update_history(step, n_reg, state)
output = self._loop_monitor_output(step, state)
if output is not None:
yield output
if step - self.current_step >= tic_point:
toc = time.time() - tic
if toc > 600:
if toc > 7200:
time_string = "%0.1f hours" % (toc / 3600)
else:
time_string = "%0.1f min" % (toc / 60)
else:
time_string = "%0.1f sec" % toc
print_this = "\r...%0.1f%% done in %s" % \
(100.0 * (step - self.current_step) / n_steps, time_string)
sys.stdout.write(print_this)
sys.stdout.flush()
tic_point += tic_ratio * n_steps
self.current_state = state
self.current_step = self.current_step + n_steps - 1 # -1 : don't repeat last point
def _configure_history(self, initial_conditions):
"""
Set initial conditions for the simulation using either the provided
initial_conditions or, if none are provided, the model's initial()
method. This method is called durin the Simulator's __init__().
Any initial_conditions that are provided as an argument are expected
to have dimensions 1, 2, and 3 with shapse corresponding to the number
of state_variables, nodes and modes, respectively. If the provided
inital_conditions are shorter in time (dim=0) than the required history
the model's initial() method is called to make up the difference.
"""
rng = numpy.random
if hasattr(self.integrator, 'noise'):
rng = self.integrator.noise.random_stream
# Default initial conditions
if initial_conditions is None:
n_time, n_svar, n_node, n_mode = self.good_history_shape
LOG.info(
'Preparing initial history of shape %r using model.initial()',
self.good_history_shape)
if self.surface is not None:
n_node = self.number_of_nodes
history = self.model.initial(self.integrator.dt,
(n_time, n_svar, n_node, n_mode), rng)
# ICs provided
else:
# history should be [timepoints, state_variables, nodes, modes]
LOG.info('Using provided initial history of shape %r',
initial_conditions.shape)
n_time, n_svar, n_node, n_mode = ic_shape = initial_conditions.shape
nr = self.connectivity.number_of_regions
if self.surface is not None and n_node == nr:
initial_conditions = initial_conditions[:, :, self._regmap]
return self._configure_history(initial_conditions)
elif ic_shape[1:] != self.good_history_shape[1:]:
raise_value_error(
"Incorrect history sample shape %s, expected %s" %
ic_shape[1:], self.good_history_shape[1:])
else:
if ic_shape[0] >= self.horizon:
LOG.debug("Using last %d time-steps for history.",
self.horizon)
history = initial_conditions[
-self.horizon:, :, :, :].copy()
else:
LOG.debug('Padding initial conditions with model.initial')
history = self.model.initial(self.integrator.dt,
self.good_history_shape, rng)
shift = self.current_step % self.horizon
history = numpy.roll(history, -shift, axis=0)
history[:ic_shape[0], :, :, :] = initial_conditions
history = numpy.roll(history, shift, axis=0)
self.current_step += ic_shape[0] - 1
# Make sure that history values are bounded,
# and any possible non-state variables are initialized
# based on state variable ones (but with no coupling yet...)
self._update_and_bound_history(numpy.swapaxes(history, 0, 1))
LOG.info('Final initial history shape is %r', history.shape)
# create initial state from history
self.current_state = history[self.current_step % self.horizon].copy()
LOG.debug('initial state has shape %r' % (self.current_state.shape, ))
if self.surface is not None and history.shape[
2] > self.connectivity.number_of_regions:
n_reg = self.connectivity.number_of_regions
(nt, ns, _, nm), ax = history.shape, (2, 0, 1, 3)
region_history = numpy.zeros((nt, ns, n_reg, nm))
numpy_add_at(region_history.transpose(ax), self._regmap,
history.transpose(ax))
region_history /= numpy.bincount(self._regmap).reshape((-1, 1))
history = region_history
# create history query implementation
self.history = SparseHistory(self.connectivity.weights,
self.connectivity.idelays,
self.model.cvar,
self.model.number_of_modes)
# initialize its buffer
self.history.initialize(history)
class CouplingTest(BaseTestCase):
"""
Define test cases for coupling:
- initialise each class
- check functionality
"""
weights = numpy.array([[0, 1], [1, 0]])
state_1sv = numpy.array([[[1], [2]]]) # (state_variables, nodes, modes)
state_2sv = numpy.array([[[1], [2]], [[1], [2]]])
history_1sv = SparseHistory(weights, weights * 0, numpy.r_[0], 1)
history_2sv = SparseHistory(weights, weights * 0, numpy.r_[0, 1], 1)
history_1sv.update(0, state_1sv)
history_2sv.update(0, state_2sv)
def _apply_coupling(self, k):
k.configure()
return k(0, self.history_1sv)
def _apply_coupling_2sv(self, k):
k.configure()
return k(0, self.history_2sv)
def test_difference_coupling(self):
k = coupling.Difference()
self.assertEqual(k.a, 0.1)
result = self._apply_coupling(k)
self.assertEqual(result.shape,
(1, 2, 1)) # One state variable, two nodes, one mode
result = result[0, :, 0].tolist()
expected_result = [
k.a * (self.weights[0, 0] *
(self.state_1sv[0, 0, 0] - self.state_1sv[0, 0, 0]) +
self.weights[0, 1] *
(self.state_1sv[0, 1, 0] - self.state_1sv[0, 0, 0])),
k.a * (self.weights[1, 0] *
(self.state_1sv[0, 0, 0] - self.state_1sv[0, 1, 0]) +
self.weights[1, 1] *
(self.state_1sv[0, 1, 0] - self.state_1sv[0, 1, 0]))
]
self.assertAlmostEqual(result, expected_result)
def test_hyperbolic_coupling(self):
k = coupling.HyperbolicTangent()
self.assertEqual(k.a, 1)
self.assertEqual(k.b, 1)
self.assertEqual(k.midpoint, 0)
self.assertEqual(k.sigma, 1)
self._apply_coupling(k)
def test_kuramoto_coupling(self):
k = coupling.Kuramoto()
self.assertEqual(k.a, 1)
self._apply_coupling(k)
def test_linear_coupling(self):
k = coupling.Linear()
self.assertEqual(k.a, 0.00390625)
self.assertEqual(k.b, 0.0)
self._apply_coupling(k)
def test_pre_sigmoidal_coupling(self):
k = coupling.PreSigmoidal()
self.assertEqual(k.H, 0.5)
self.assertEqual(k.Q, 1.)
self.assertEqual(k.G, 60.)
self.assertEqual(k.P, 1.)
self.assertEqual(k.theta, 0.5)
self.assertEqual(k.dynamic, True)
self.assertEqual(k.globalT, False)
self._apply_coupling_2sv(k)
def test_scaling_coupling(self):
k = coupling.Scaling()
# Check scaling -factor
self.assertEqual(k.a, 0.00390625)
self._apply_coupling(k)
def test_sigmoidal_coupling(self):
k = coupling.Sigmoidal()
self.assertEqual(k.cmin, -1.0)
self.assertEqual(k.cmax, 1.0)
self.assertEqual(k.midpoint, 0.0)
self.assertEqual(k.sigma, 230.)
self.assertEqual(k.a, 1.0)
self._apply_coupling(k)
def test_sigmoidal_jr_coupling(self):
k = coupling.SigmoidalJansenRit()
self.assertEqual(k.cmin, 0.0)
self.assertEqual(k.cmax, 2.0 * 0.0025)
self.assertEqual(k.midpoint, 6.0)
self.assertEqual(k.r, 1.0)
self.assertEqual(k.a, 0.56)
self._apply_coupling_2sv(k)
class TestCoupling(BaseTestCase):
"""
Define test cases for coupling:
- initialise each class
- check functionality
"""
weights = numpy.array([[0, 1], [1, 0]])
state_1sv = numpy.array([[[1], [2]]]) # (state_variables, nodes, modes)
state_2sv = numpy.array([[[1], [2]], [[1], [2]]])
history_1sv = SparseHistory(1,
weights=weights,
delays=weights * 0,
cvars=numpy.r_[0])
history_2sv = SparseHistory(1,
weights=weights,
delays=weights * 0,
cvars=numpy.r_[0, 1])
history_1sv.update(0, state_1sv)
history_2sv.update(0, state_2sv)
def _apply_coupling(self, k):
k.configure()
return k(0, self.history_1sv)
def _apply_coupling_2sv(self, k):
k.configure()
return k(0, self.history_2sv)
def test_difference_coupling(self):
k = coupling.Difference()
assert k.a, 0.1
result = self._apply_coupling(k)
assert result.shape, (1, 2 == 1
) # One state variable, two nodes, one mode
result = result[0, :, 0].tolist()
expected_result = [
k.a * (self.weights[0, 0] *
(self.state_1sv[0, 0, 0] - self.state_1sv[0, 0, 0]) +
self.weights[0, 1] *
(self.state_1sv[0, 1, 0] - self.state_1sv[0, 0, 0])),
k.a * (self.weights[1, 0] *
(self.state_1sv[0, 0, 0] - self.state_1sv[0, 1, 0]) +
self.weights[1, 1] *
(self.state_1sv[0, 1, 0] - self.state_1sv[0, 1, 0]))
]
assert self.almost_equal(result, expected_result)
def test_hyperbolic_coupling(self):
k = coupling.HyperbolicTangent()
assert k.a == 1
assert k.b == 1
assert k.midpoint == 0
assert k.sigma == 1
self._apply_coupling(k)
def test_kuramoto_coupling(self):
k = coupling.Kuramoto()
assert k.a == 1
self._apply_coupling(k)
def test_linear_coupling(self):
k = coupling.Linear()
assert k.a == 0.00390625
assert k.b == 0.0
self._apply_coupling(k)
def test_pre_sigmoidal_coupling(self):
k = coupling.PreSigmoidal()
assert k.H == 0.5
assert k.Q == 1.
assert k.G == 60.
assert k.P == 1.
assert k.theta == 0.5
assert k.dynamic is True
assert k.globalT is False
self._apply_coupling_2sv(k)
def test_scaling_coupling(self):
k = coupling.Scaling()
# Check scaling -factor
assert k.a == 0.00390625
self._apply_coupling(k)
def test_sigmoidal_coupling(self):
k = coupling.Sigmoidal()
assert k.cmin == -1.0
assert k.cmax == 1.0
assert k.midpoint == 0.0
assert k.sigma == 230.
assert k.a == 1.0
self._apply_coupling(k)
def test_sigmoidal_jr_coupling(self):
k = coupling.SigmoidalJansenRit()
assert k.cmin == 0.0
assert k.cmax == 2.0 * 0.0025
assert k.midpoint == 6.0
assert k.r == 1.0
assert k.a == 0.56
self._apply_coupling_2sv(k)
class CouplingTest(BaseTestCase):
"""
Define test cases for coupling:
- initialise each class
- check functionality
"""
weights = numpy.array([[0, 1], [1, 0]])
weights = weights[:, numpy.newaxis, :,
numpy.newaxis] # nodes, ncvar, nodes, modes
state_1sv = numpy.array([[[1], [2]]]) # (state_variables, nodes, modes)
state_2sv = numpy.array([[[1], [2]], [[1], [2]]])
delayed_state_1sv = numpy.ones(
(2, 1, 2, 1)) # nodes, state_variables, nodes, modes
delayed_state_2sv = numpy.ones((2, 2, 2, 1))
weights2d = weights[:, 0, :, 0]
history_1sv = SparseHistory(weights2d, weights2d * 0, numpy.r_[0], 1)
history_2sv = SparseHistory(weights2d, weights2d * 0, numpy.r_[0, 1], 1)
def _apply_coupling(self, k):
k.configure()
k(0, self.history_1sv)
def _apply_coupling_2sv(self, k):
k.configure()
k(0, self.history_2sv)
def test_difference_coupling(self):
k = coupling.Difference()
self.assertEqual(k.a, 0.1)
self._apply_coupling(k)
def test_hyperbolic_coupling(self):
k = coupling.HyperbolicTangent()
self.assertEqual(k.a, 1)
self.assertEqual(k.b, 1)
self.assertEqual(k.midpoint, 0)
self.assertEqual(k.sigma, 1)
self._apply_coupling(k)
def test_kuramoto_coupling(self):
k = coupling.Kuramoto()
self.assertEqual(k.a, 1)
self._apply_coupling(k)
def test_linear_coupling(self):
k = coupling.Linear()
self.assertEqual(k.a, 0.00390625)
self.assertEqual(k.b, 0.0)
self._apply_coupling(k)
def test_pre_sigmoidal_coupling(self):
k = coupling.PreSigmoidal()
self.assertEqual(k.H, 0.5)
self.assertEqual(k.Q, 1.)
self.assertEqual(k.G, 60.)
self.assertEqual(k.P, 1.)
self.assertEqual(k.theta, 0.5)
self.assertEqual(k.dynamic, True)
self.assertEqual(k.globalT, False)
self._apply_coupling_2sv(k)
def test_scaling_coupling(self):
k = coupling.Scaling()
# Check scaling -factor
self.assertEqual(k.a, 0.00390625)
self._apply_coupling(k)
def test_sigmoidal_coupling(self):
k = coupling.Sigmoidal()
self.assertEqual(k.cmin, -1.0)
self.assertEqual(k.cmax, 1.0)
self.assertEqual(k.midpoint, 0.0)
self.assertEqual(k.sigma, 230.)
self.assertEqual(k.a, 1.0)
self._apply_coupling(k)
def test_sigmoidal_jr_coupling(self):
k = coupling.SigmoidalJansenRit()
self.assertEqual(k.cmin, 0.0)
self.assertEqual(k.cmax, 2.0 * 0.0025)
self.assertEqual(k.midpoint, 6.0)
self.assertEqual(k.r, 1.0)
self.assertEqual(k.a, 0.56)
self._apply_coupling_2sv(k)