def build_tvb_model(model_configuration, zmode=numpy.array("lin")):
# We use the opposite sign for K with respect to all epileptor models
K = -model_configuration.K
model_instance = Epileptor(x0=model_configuration.x0, Iext=model_configuration.Iext1, Iext2=model_configuration.Iext2,
Ks=K, c=model_configuration.yc,
a=model_configuration.a, b=model_configuration.b, d=model_configuration.d,
aa=model_configuration.s)
return model_instance
def build_tvb_model(model_configuration):
# We use the opposite sign for K with respect to all epileptor models
K = -model_configuration.K
model_instance = Epileptor(x0=model_configuration.x0,
Iext=model_configuration.Iext1,
Iext2=model_configuration.Iext2,
Ks=K,
c=model_configuration.yc,
a=model_configuration.a,
b=model_configuration.b,
d=model_configuration.d,
aa=model_configuration.s,
tt=model_configuration.tau1,
r=1.0 / model_configuration.tau0)
return model_instance
def build_tvb_model(model_configuration,
a=1.0,
b=3.0,
d=5.0,
zmode=numpy.array("lin")):
x0_transformed = calc_rescaled_x0(model_configuration.x0_values,
model_configuration.yc,
model_configuration.Iext1,
a,
b - d,
zmode=zmode)
model_instance = Epileptor(x0=x0_transformed,
Iext=model_configuration.Iext1,
Ks=model_configuration.K,
c=model_configuration.yc)
return model_instance
def test_models_list(self):
all_models_for_ui = get_ui_name_to_model()
models_form = SimulatorModelFragment()
simulator = Simulator()
simulator.model = Epileptor()
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 build_tvb_model(hypothesis, variables_of_interest=["y3 - y0", "y2"], a=1.0, b=3.0, d=5.0, zmode="lin"):
x0_transformed = calc_rescaled_x0(hypothesis.x0, hypothesis.yc, hypothesis.Iext1, a, b - d, zmode=zmode)
model_instance = Epileptor(x0=x0_transformed.flatten(), Iext=hypothesis.Iext1.flatten(),
Ks=hypothesis.K.flatten(), c=hypothesis.yc.flatten(),
variables_of_interest=variables_of_interest)
return model_instance
"EpileptorDP": EpileptorDP._nvar,
"EpileptorDPrealistic": EpileptorDPrealistic._nvar,
"EpileptorDP2D": EpileptorDP2D._nvar
}
EPILEPTOR_MODEL_TAU1 = {
"JavaEpileptor": JavaEpileptor.tt,
"Epileptor": EpileptorDP().tau1,
"EpileptorDP": EpileptorDP().tau1,
"EpileptorDPrealistic": EpileptorDPrealistic().tau1,
"EpileptorDP2D": EpileptorDP2D().tau1
}
EPILEPTOR_MODEL_TAU0 = {
"JavaEpileptor": 1.0 / JavaEpileptor.r,
"Epileptor": 1.0 / Epileptor().r,
"EpileptorDP": EpileptorDP().tau0,
"EpileptorDPrealistic": EpileptorDPrealistic().tau0,
"EpileptorDP2D": EpileptorDP2D().tau0
}
model_noise_intensity_dict = {
"Epileptor":
numpy.array([0., 0., 5e-6, 0.0, 5e-6, 0.]),
"JavaEpileptor":
numpy.array([0., 0., 5e-6, 0.0, 5e-6, 0.]),
"EpileptorDP":
numpy.array([0., 0., 5e-6, 0.0, 5e-6, 0.]),
# x0_t K_t slope_t Iext1_t Iext2_T
"EpileptorDPrealistic":
[2.5e-4, 2.5e-4, 2e-6, 1e-7, 1e-7, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5],
def build_EpileptorDPrealistic(**kwargs):
# We use the opposite sign for K with respect to all epileptor models
model_instance = Epileptor(**kwargs)
return model_instance