# -*- 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 http://www.thevirtualbrain.org
#
# (c) 2012-2023, 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 <http://www.gnu.org/licenses/>.
#
#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as explained here:
# https://www.thevirtualbrain.org/tvb/zwei/neuroscience-publications
#
#
"""
A plain NumPy backend which uses templating to generate simulation
code.
.. moduleauthor:: Marmaduke Woodman <marmaduke.woodman@univ-amu.fr>
"""
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(self.cgdir.name)
[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(self.cgdir.name, 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="np-sim.py.mako"/>'
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),
