class_docstr: !
  @brief Model of oscillatory network that is based on the Kuramoto model of synchronization.
  
  @details CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
docstr: !
  @brief Returns result of phase calculation for specified oscillator in the network.
  
  @param[in] teta (double): Phase of the oscillator that is differentiated.
  @param[in] t (double): Current time of simulation.
  @param[in] argv (tuple): Index of the oscillator in the list.
  
  @return (double) New phase for specified oscillator (don't assign here).
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code: def _phase_kuramoto(self, teta, t, argv):
      """!
      @brief Returns result of phase calculation for specified oscillator in the network.
  
      @param[in] teta (double): Phase of the oscillator that is differentiated.
      @param[in] t (double): Current time of simulation.
      @param[in] argv (tuple): Index of the oscillator in the list.
  
      @return (double) New phase for specified oscillator (don't assign here).
  
      """
  
      index = argv
      phase = 0
      for k in range(0, self._num_osc):
          if self.has_connection(index, k) == True:
              phase += math.sin(self._phases[k] - teta)
  
      return self._freq[index] + (phase * self._weight / self._num_osc)
  
imports: import math
  import numpy
  import random
  import matplotlib.pyplot
  import matplotlib.animation
  import pyclustering.core.sync_wrapper
  from pyclustering.core.wrapper import ccore_library
  from scipy.integrate import odeint
  from pyclustering.nnet import network, conn_represent, conn_type, initial_type, solve_type
  from pyclustering.utils import pi, draw_dynamics, draw_dynamics_set, set_ax_param
label_desc: Use image node_id 5 for calling the sync_network obj's underlying member method code with example usage: obj._phase_kuramoto(teta, t, argv) and returns: unknown