Source code for

# -*- coding: utf-8 -*-
# TheVirtualBrain-Scientific Package. This package holds all simulators, and
# analysers necessary to run brain-simulations. You can use it stand alone or
# in conjunction with TheVirtualBrain-Framework Package. See content of the
# documentation-folder for more details. See also
# (c) 2012-2024, Baycrest Centre for Geriatric Care ("Baycrest") and others
# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE.  See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program.  If not, see <>.
# When using The Virtual Brain for scientific publications, please cite it as explained here:

A plain NumPy backend which uses templating to generate simulation

.. moduleauthor:: Marmaduke Woodman <>


import os
import sys
import importlib
import numpy as np
import autopep8
import tempfile

from .templates import MakoUtilMix

from tvb.simulator.lab import *

[docs] class NpBackend(MakoUtilMix): def __init__(self): self.cgdir = tempfile.TemporaryDirectory() sys.path.append(
[docs] def build_py_func(self, template_source, content, name='kernel', print_source=False, modname=None, fname=None): "Build and retrieve one or more Python functions from template." source = self.render_template(template_source, content) source = autopep8.fix_code(source) if print_source: print(self.insert_line_numbers(source)) if fname is not None: fullfname = os.path.join(, fname) with open(fullfname, 'w') as fd: fd.write(source) if modname is not None: return self.eval_module(source, name, modname) else: return self.eval_source(source, name, print_source)
[docs] def eval_source(self, source, name, print_source): globals_ = {} try: exec(source, globals_) except Exception as exc: if not print_source: print(self._insert_line_numbers(source)) raise exc fns = [globals_[n] for n in name.split(',')] return fns[0] if len(fns)==1 else fns
[docs] def eval_module(self, source, name, modname): here = os.path.abspath(os.path.dirname(__file__)) genp = os.path.join(here, 'templates', 'generated') with open(f'{genp}/{modname}.py', 'w') as fd: fd.write(source) fullmodname = f'tvb.simulator.backend.templates.generated.{modname}' mod = importlib.import_module(fullmodname) fns = [getattr(mod,n) for n in name.split(',')] return fns[0] if len(fns)==1 else fns
def _check_choices( self, val, choices): if not isinstance(val, choices): raise NotImplementedError("Unsupported simulator component. Given: {}\nExpected one of: {}".format(val, choices))
[docs] def check_compatibility(self,sim): # monitors if len(sim.monitors) > 1: raise NotImplementedError("Configure with one monitor.") self._check_choices(sim.monitors[0], monitors.Raw) # integrators self._check_choices(sim.integrator, ( integrators.HeunStochastic, integrators.HeunDeterministic, integrators.EulerStochastic, integrators.EulerDeterministic, integrators.Identity, integrators.IdentityStochastic, integrators.RungeKutta4thOrderDeterministic, ) ) # models if sim.model.number_of_modes > 1: # this is a limitation e.g. by how nsig is now handled raise NotImplementedError("Only models with 1 mode are supported") self._check_choices(sim.model, models.MontbrioPazoRoxin) # coupling self._check_choices(sim.coupling, (coupling.Linear, coupling.Sigmoidal)) # surface if sim.surface is not None: raise NotImplementedError("Surface simulation not supported.")
# stimulus evaluated outside the backend, no restrictions
[docs] def run_sim(self, sim, nstep=None, simulation_length=None, print_source=False): assert nstep is not None or simulation_length is not None or sim.simulation_length is not None self.check_compatibility(sim) if nstep is None: if simulation_length is None: simulation_length = sim.simulation_length nstep = int(np.ceil(simulation_length/sim.integrator.dt)) buf = sim.history.buffer[...,0] rbuf = np.concatenate((buf[0:1], buf[1:][::-1]), axis=0) state = np.transpose(rbuf, (1, 0, 2)).astype('f') t = np.arange(1, nstep+1 ) * sim.integrator.dt template = '<%include file=""/>' content = dict(sim=sim, np=np, nstep=nstep) kernel = self.build_py_func(template, content, print_source=print_source) dX = state.copy() n_svar, _, n_node = state.shape state = state.reshape((n_svar, sim.connectivity.horizon, n_node)) weights = sim.connectivity.weights.copy() yh = np.empty((len(t),)+state[:,0].shape) parmat = sim.model.spatial_parameter_matrix args = state, weights, yh, parmat if isinstance(sim.integrator, integrators.IntegratorStochastic): np.random.seed(sim.integrator.noise.noise_seed) if len(sim.integrator.noise.nsig.shape) > 1: nsig = sim.integrator.noise.nsig[:,0] # no modes for now else: nsig = sim.integrator.noise.nsig args = args + (nsig,) if sim.connectivity.has_delays: args = args + (sim.connectivity.delay_indices,) kernel(*args) return (t, yh),