The Virtual Brain Project

Source code for

# -*- coding: utf-8 -*-
# TheVirtualBrain-Framework Package. This package holds all Data Management, and 
# Web-UI helpful to run brain-simulations. To use it, you also need do download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also
# (c) 2012-2017, 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 follows:
#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
#   Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
#       The Virtual Brain: a simulator of primate brain network dynamics.
#   Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)

Service for serianlizing a Burst (Simulator) configuration.

.. moduleauthor:: Mihai Andrei <>

import six
from tvb.basic.logger.builder import get_logger
from tvb.basic.traits.parameters_factory import get_traited_instance_for_name
from tvb.core.adapters.abcadapter import ABCAdapter
from tvb.core.entities.model import RANGE_PARAMETER_1, RANGE_PARAMETER_2, PARAMS_MODEL_PATTERN
from import FlowService
from tvb.datatypes import noise_framework
from tvb.simulator.integrators import Integrator
from tvb.simulator.models import Model

MODEL_PARAMETERS = 'model_parameters'
INTEGRATOR_PARAMETERS = 'integrator_parameters'

[docs]class SerializationManager(object): """ Constructs data types based on a burst configuration. Updates the burst configuration. """ def __init__(self, conf): """ :param conf: burst configuration entity """ self.logger = get_logger(__name__) self.flow_service = FlowService() self.conf = conf def _build_simulator_adapter(self): stored_adapter = self.flow_service.get_algorithm_by_module_and_class(SIMULATOR_MODULE, SIMULATOR_CLASS) return ABCAdapter.build_adapter(stored_adapter)
[docs] def has_model_pse_ranges(self): """ Returns True if the burst configuration describes a range on a model parameter """ first_range = self.conf.get_simulation_parameter_value(RANGE_PARAMETER_1) second_range = self.conf.get_simulation_parameter_value(RANGE_PARAMETER_2) first_range_on = first_range is not None and str(first_range).startswith(MODEL_PARAMETERS) second_range_on = second_range is not None and str(second_range).startswith(MODEL_PARAMETERS) return first_range_on or second_range_on
def _get_params_dict(self): """ Convert ui inputs from the configuration to python types """ simulator_adapter = self._build_simulator_adapter() return simulator_adapter.convert_ui_inputs(self.conf.get_all_simulator_values()[0], False) def __make_instance_from_burst_config(self, params_dict, parent_class, class_name_key, params_key): """ This is used internally to create a model or an integrator based on the burst config """ class_name = self.conf.get_simulation_parameter_value(class_name_key) parameters = params_dict[params_key] noise_framework.build_noise(parameters) try: return get_traited_instance_for_name(class_name, parent_class, parameters) except Exception: self.logger.exception("Could not create an instance of %s with the given parameters. " "A new instance will be created with the default values." % class_name) return get_traited_instance_for_name(class_name, parent_class, {}) def __make_shallow_model(self): """ Creates a model of the type present in the config without setting any parameters on it """ class_name = self.conf.get_simulation_parameter_value(PARAM_MODEL) return get_traited_instance_for_name(class_name, Model, {})
[docs] def make_model_and_integrator(self): """ :return: A model and an integrator. :rtype: Model, Integrator """ params_dict = self._get_params_dict() model = self.__make_instance_from_burst_config(params_dict, Model, PARAM_MODEL, MODEL_PARAMETERS) integrator = self.__make_instance_from_burst_config(params_dict, Integrator, PARAM_INTEGRATOR, INTEGRATOR_PARAMETERS) return model, integrator
[docs] def get_connectivity(self): """ Prepare Connectivity """ connectivity_gid = self.conf.get_simulation_parameter_value(PARAM_CONNECTIVITY) return ABCAdapter.load_entity_by_gid(connectivity_gid)
[docs] def get_surface(self): """ Prepare Surface """ surface_gid = self.conf.get_simulation_parameter_value(PARAM_SURFACE) if surface_gid: return ABCAdapter.load_entity_by_gid(surface_gid) return None
[docs] def group_parameter_values_by_name(model_parameters_list): """ @:param model_parameters_list: Given a list of model parameters like this: [{"a": 2.0, 'b': 1.0}, {"a": 3.0, 'b': 7.0}]) @:return: This method will group them by param name to get: {'a': [2.0, 3.0], 'b': [1.0, 7.0]} """ ret = {} for model_parameters in model_parameters_list: for param_name, param_val in six.iteritems(model_parameters): if param_name not in ret: ret[param_name] = [] ret[param_name].append(param_val) return ret
[docs] def write_model_parameters(self, model_name, model_parameters_list): """ Update model parameters in burst config. :param model_name: This model will be selected in burst :param model_parameters_list: A list of model parameter configurations. One for each connectivity node. Ex. [{'a': 1, 'b': 2}, ...] """ def format_param_vals(vals): # contract constant array if len(set(vals)) == 1: vals = [vals[0]] return str(vals) model_parameters = self.group_parameter_values_by_name(model_parameters_list) # change selected model in burst config self.conf.update_simulation_parameter(PARAM_MODEL, model_name) for param_name, param_vals in six.iteritems(model_parameters): full_name = PARAMS_MODEL_PATTERN % (model_name, param_name) self.conf.update_simulation_parameter(full_name, format_param_vals(param_vals))
[docs] def write_noise_parameters(self, noise_dispersions): """ Set noise dispersions in burst config. It will set all nsig fields it can find in the config (at least 1 per stochastic integrator). :param noise_dispersions: A list of noise dispersions. One for each connectivity node. Ex [{'V': 1, 'W':2}, ...] """ noise_dispersions = self.group_parameter_values_by_name(noise_dispersions) # Flatten the dict to an array of shape (state_vars, nodes) state_vars = self.__make_shallow_model().state_variables noise_arr = [noise_dispersions[sv] for sv in state_vars] simulator_adapter = self._build_simulator_adapter() for param_name in simulator_adapter.noise_configurable_parameters(): self.conf.update_simulation_parameter(param_name, str(noise_arr))