Source code for

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Service for serializing a Burst (Simulator) configuration.

.. moduleauthor:: Mihai Andrei <>
import numpy
from tvb.basic.logger.builder import get_logger
from tvb.simulator import models

[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.conf = conf
[docs] @staticmethod 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 model_parameters.items(): 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 numpy.array(vals) model_parameters = self.group_parameter_values_by_name(model_parameters_list) # change selected model in burst config model_class = getattr(models, model_name) self.conf.model = model_class() for param_name, param_vals in model_parameters.items(): setattr(self.conf.model, param_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.conf.model.state_variables noise_arr = [noise_dispersions[sv] for sv in state_vars] self.conf.integrator.noise.nsig = numpy.array(noise_arr)