Source code for brian2.stateupdaters.exact

Exact integration for linear equations.

import itertools

import sympy as sp
from sympy import Wild, Symbol, I, re, im

from brian2.equations.codestrings import is_constant_over_dt
from brian2.parsing.sympytools import sympy_to_str, str_to_sympy
from brian2.stateupdaters.base import (StateUpdateMethod,
from brian2.utils.logger import get_logger

__all__ = ['linear', 'exact', 'independent']

logger = get_logger(__name__)

[docs]def get_linear_system(eqs, variables): """ Convert equations into a linear system using sympy. Parameters ---------- eqs : `Equations` The model equations. Returns ------- (diff_eq_names, coefficients, constants) : (list of str, `sympy.Matrix`, `sympy.Matrix`) A tuple containing the variable names (`diff_eq_names`) corresponding to the rows of the matrix `coefficients` and the vector `constants`, representing the system of equations in the form M * X + B Raises ------ ValueError If the equations cannot be converted into an M * X + B form. """ diff_eqs = eqs.get_substituted_expressions(variables) diff_eq_names = [name for name, _ in diff_eqs] symbols = [Symbol(name, real=True) for name in diff_eq_names] coefficients = sp.zeros(len(diff_eq_names)) constants = sp.zeros(len(diff_eq_names), 1) for row_idx, (name, expr) in enumerate(diff_eqs): s_expr = str_to_sympy(expr.code, variables).expand() current_s_expr = s_expr for col_idx, symbol in enumerate(symbols): current_s_expr = current_s_expr.collect(symbol) constant_wildcard = Wild('c', exclude=[symbol]) factor_wildcard = Wild(f"c_{name}", exclude=symbols) one_pattern = factor_wildcard*symbol + constant_wildcard matches = current_s_expr.match(one_pattern) if matches is None: raise UnsupportedEquationsException(f"The expression '{expr}', " f"defining the variable " f"'{name}', could not be " f"separated into linear " f"components.") coefficients[row_idx, col_idx] = matches[factor_wildcard] current_s_expr = matches[constant_wildcard] # The remaining constant should be a true constant constants[row_idx] = current_s_expr return (diff_eq_names, coefficients, constants)
[docs]class IndependentStateUpdater(StateUpdateMethod): """ A state update for equations that do not depend on other state variables, i.e. 1-dimensional differential equations. The individual equations are solved by sympy. .. deprecated:: 2.1 This method might be removed from future versions of Brian. """
[docs] def __call__(self, equations, variables=None, method_options=None): logger.warn("The 'independent' state updater is deprecated and might be " "removed in future versions of Brian.", 'deprecated_independent', once=True) extract_method_options(method_options, {}) if equations.is_stochastic: raise UnsupportedEquationsException("Cannot solve stochastic " "equations with this state " "updater") if variables is None: variables = {} diff_eqs = equations.get_substituted_expressions(variables) t = Symbol('t', real=True, positive=True) dt = Symbol('dt', real=True, positive=True) t0 = Symbol('t0', real=True, positive=True) code = [] for name, expression in diff_eqs: rhs = str_to_sympy(expression.code, variables) # We have to be careful and use the real=True assumption as well, # otherwise sympy doesn't consider the symbol a match to the content # of the equation var = Symbol(name, real=True) f = sp.Function(name) rhs = rhs.subs(var, f(t)) derivative = sp.Derivative(f(t), t) diff_eq = sp.Eq(derivative, rhs) # TODO: simplify=True sometimes fails with 0.7.4, see: # try: general_solution = sp.dsolve(diff_eq, f(t), simplify=True) except RuntimeError: general_solution = sp.dsolve(diff_eq, f(t), simplify=False) # Check whether this is an explicit solution if not getattr(general_solution, 'lhs', None) == f(t): raise UnsupportedEquationsException(f"Cannot explicitly solve: {str(diff_eq)}") # Solve for C1 (assuming "var" as the initial value and "t0" as time) if general_solution.has(Symbol('C1')): if general_solution.has(Symbol('C2')): raise UnsupportedEquationsException(f'Too many constants in solution: {str(general_solution)}') constant_solution = sp.solve(general_solution, Symbol('C1')) if len(constant_solution) != 1: raise UnsupportedEquationsException(("Couldn't solve for the constant " "C1 in : %s ") % str(general_solution)) constant = constant_solution[0].subs(t, t0).subs(f(t0), var) solution = general_solution.rhs.subs('C1', constant) else: solution = general_solution.rhs.subs(t, t0).subs(f(t0), var) # Evaluate the expression for one timestep solution = solution.subs(t, t + dt).subs(t0, t) # only try symplifying it -- it sometimes raises an error try: solution = solution.simplify() except ValueError: pass code.append(f"{name} = {sympy_to_str(solution)}") return '\n'.join(code)
[docs]class LinearStateUpdater(StateUpdateMethod): """ A state updater for linear equations. Derives a state updater step from the analytical solution given by sympy. Uses the matrix exponential (which is only implemented for diagonalizable matrices in sympy). """
[docs] def __call__(self, equations, variables=None, method_options=None): method_options = extract_method_options(method_options, {'simplify': True}) if equations.is_stochastic: raise UnsupportedEquationsException("Cannot solve stochastic " "equations with this state " "updater.") if variables is None: variables = {} # Get a representation of the ODE system in the form of # dX/dt = M*X + B varnames, matrix, constants = get_linear_system(equations, variables) # No differential equations, nothing to do (this occurs sometimes in the # test suite where the whole model is nothing more than something like # 'v : 1') if matrix.shape == (0, 0): return '' # Make sure that the matrix M is constant, i.e. it only contains # external variables or constant variables # Check for time dependence dt_value = variables['dt'].get_value()[0] if 'dt' in variables else None # This will raise an error if we meet the symbol "t" anywhere # except as an argument of a locally constant function for entry in itertools.chain(matrix, constants): if not is_constant_over_dt(entry, variables, dt_value): raise UnsupportedEquationsException( f"Expression '{sympy_to_str(entry)}' is not guaranteed to be " f"constant over a time step.") symbols = [Symbol(variable, real=True) for variable in varnames] solution = sp.solve_linear_system(matrix.row_join(constants), *symbols) if solution is None or set(symbols) != set(solution.keys()): raise UnsupportedEquationsException("Cannot solve the given " "equations with this " "stateupdater.") b = sp.ImmutableMatrix([solution[symbol] for symbol in symbols]) # Solve the system dt = Symbol('dt', real=True, positive=True) try: A = (matrix * dt).exp() except NotImplementedError: raise UnsupportedEquationsException("Cannot solve the given " "equations with this " "stateupdater.") if method_options['simplify']: A = A.applyfunc(lambda x: sp.factor_terms(sp.cancel(sp.signsimp(x)))) C = sp.ImmutableMatrix(A * b) - b _S = sp.MatrixSymbol('_S', len(varnames), 1) updates = A * _S + C updates = updates.as_explicit() # The solution contains _S[0, 0], _S[1, 0] etc. for the state variables, # replace them with the state variable names abstract_code = [] for idx, (variable, update) in enumerate(zip(varnames, updates)): rhs = update if rhs.has(I, re, im): raise UnsupportedEquationsException("The solution to the linear system " "contains complex values " "which is currently not implemented.") for row_idx, varname in enumerate(varnames): rhs = rhs.subs(_S[row_idx, 0], varname) # Do not overwrite the real state variables yet, the update step # of other state variables might still need the original values abstract_code.append(f"_{variable} = {sympy_to_str(rhs)}") # Update the state variables for variable in varnames: abstract_code.append(f"{variable} = _{variable}") return '\n'.join(abstract_code)
def __repr__(self): return f'{self.__class__.__name__}()'
independent = IndependentStateUpdater() linear = LinearStateUpdater() exact = linear