def test_wrong_params3():
with pytest.raises(
AssertionError,
match=
"Expecting required_lhs to be a collection of variables or derivatives"
):
partial_eval([Eq(Symbol('x'), 2.0)], [3])
def _get_non_linear_state_vars(self):
""" Get and store the non_linear state vars """
derivative_equations = \
set(partial_eval(self._derivative_equations, self._model.y_derivatives, keep_multiple_usages=False))
self._non_linear_state_vars = \
get_non_linear_state_vars(derivative_equations, self._model.membrane_voltage_var,
self._model.state_vars)
def __init__(self, model, file_name, **kwargs):
self._use_data_clamp = kwargs.get('cvode_data_clamp', False) # store if data clamp is needed
self._use_analytic_jacobian = kwargs.get('use_analytic_jacobian', False) # store if jacobians are needed
super().__init__(model, file_name, **kwargs)
self._templates = ['cvode_model.hpp', 'cvode_model.cpp']
if self._use_data_clamp:
self._vars_for_template['base_class'] = 'AbstractCvodeCellWithDataClamp'
else:
self._vars_for_template['base_class'] = 'AbstractCvodeCell'
if self._use_data_clamp:
self._vars_for_template['model_type'] = 'CvodeCellWithDataClamp'
elif self._use_analytic_jacobian:
self._vars_for_template['model_type'] = 'AnalyticCvode'
else:
self._vars_for_template['model_type'] = 'NumericCvode'
self._vars_for_template['vector_decl'] = "N_Vector" # indicate how to declare state vars and values
if self._use_analytic_jacobian:
# get deriv eqs and substitute in all variables other than state vars
self._derivative_equations = \
partial_eval(self._derivative_equations, self._model.y_derivatives, keep_multiple_usages=False)
self._jacobian_equations, self._jacobian_matrix = get_jacobian(self._state_vars, self._derivative_equations)
self._formatted_state_vars = self._update_state_vars()
self._vars_for_template['jacobian_equations'], self._vars_for_template['jacobian_entries'] = \
self._print_jacobian()
else:
self._vars_for_template['jacobian_equations'], self._vars_for_template['jacobian_entries'] = \
[], Matrix()
def test_partial_eval2(fr_model):
derivatives_eqs = fr_model.derivative_equations
lhs_to_keep = fr_model.y_derivatives
assert len(derivatives_eqs) == 163, str(len(derivatives_eqs))
derivatives_eqs = partial_eval(derivatives_eqs,
lhs_to_keep,
keep_multiple_usages=False)
assert len(derivatives_eqs) == 25, str(len(derivatives_eqs))
expected = open(
os.path.join(TESTS_FOLDER, 'test_partial_eval_derivatives_eqs2.txt'),
'r').read()
assert str(derivatives_eqs) == expected, str(derivatives_eqs)
def _pre_print_hook(self):
""" Retreives out linear and non-linear derivatives and the relevant jacobian for it."""
super()._pre_print_hook()
# get deriv eqs and substitute in all variables other than state vars
derivative_equations = \
partial_eval(self._derivative_equations, self._model.y_derivatives, keep_multiple_usages=False)
self._non_linear_state_vars = \
sorted(get_non_linear_state_vars(derivative_equations, self._model.membrane_voltage_var,
self._model.state_vars), key=lambda s: get_variable_name(s, s in self._in_interface))
# Pick the formatted equations that are for non-linear derivatives
self._non_linear_eqs = self._get_non_linear_eqs()
self._jacobian_equations, self._jacobian_matrix = \
get_jacobian(self._non_linear_state_vars,
[d for d in derivative_equations if d.lhs.args[0] in self._non_linear_state_vars])
self._linear_deriv_eqs, self._linear_equations, self._vars_in_one_step = \
self._rearrange_linear_derivs()
def _get_derivative_equations(self):
""" Get partially evaluated equations defining the derivatives including V (self._model.membrane_voltage_var)"""
derivative_equations = partial_eval(
super()._get_derivative_equations(), self._model.y_derivatives)
self._lookup_tables.calc_lookup_tables(derivative_equations)
return derivative_equations
def _get_extended_ionic_vars(self):
""" Get the partially evaluated equations defining the ionic derivatives and all dependant equations"""
extended_ionic_vars = partial_eval(super()._get_extended_ionic_vars(),
self._model.ionic_vars)
self._lookup_tables.calc_lookup_tables(extended_ionic_vars)
return extended_ionic_vars
def _get_stimulus(self):
""" Get the partially evaluated stimulus currents in the model"""
return_stim_eqs = super()._get_stimulus()
return partial_eval(
return_stim_eqs,
self._model.stimulus_params | self._model.modifiable_parameters)
def test_wrong_params():
with pytest.raises(TypeError, match="'NoneType' object is not iterable"):
partial_eval(None, None)
def test_wrong_params2():
with pytest.raises(
AssertionError,
match="Expecting equations to be a collection of equations"):
partial_eval([1], [2])
def _get_jacobian(self):
"""Retrieve jacobian matrix"""
derivative_eqs_for_jacobian = \
partial_eval(self._derivative_equations, self._model.y_derivatives, keep_multiple_usages=False)
return get_jacobian(self._state_vars, derivative_eqs_for_jacobian)