class_docstr: !
  @brief Class represents clustering algorithm SyncNet. 
  @details SyncNet is bio-inspired algorithm that is based on oscillatory network that uses modified Kuramoto model. Each attribute of a data object
           is considered as a phase oscillator.
  
  Example:
  @code
      from pyclustering.cluster import cluster_visualizer
      from pyclustering.cluster.syncnet import syncnet, solve_type
      from pyclustering.samples.definitions import SIMPLE_SAMPLES
      from pyclustering.utils import read_sample
  
      # Read sample for clustering from some file.
      sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
  
      # Create oscillatory network with connectivity radius 1.0.
      network = syncnet(sample, 1.0)
  
      # Run cluster analysis and collect output dynamic of the oscillatory network.
      # Network simulation is performed by Runge Kutta 4.
      analyser = network.process(0.998, solve_type.RK4)
  
      # Show oscillatory network.
      network.show_network()
  
      # Obtain clustering results.
      clusters = analyser.allocate_clusters()
  
      # Visualize clustering results.
      visualizer = cluster_visualizer()
      visualizer.append_clusters(clusters, sample)
      visualizer.show()
  @endcode
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docstr: !
  @brief Overrided method for calculation of oscillator phase.
  
  @param[in] teta (double): Current value of phase.
  @param[in] t (double): Time (can be ignored).
  @param[in] argv (uint): Index of oscillator whose phase represented by argument teta.
  
  @return (double) New value of phase of oscillator with index 'argv'.
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code: def _phase_kuramoto(self, teta, t, argv):
      """!
      @brief Overrided method for calculation of oscillator phase.
  
      @param[in] teta (double): Current value of phase.
      @param[in] t (double): Time (can be ignored).
      @param[in] argv (uint): Index of oscillator whose phase represented by argument teta.
  
      @return (double) New value of phase of oscillator with index 'argv'.
  
      """
  
      index = argv  # index of oscillator
      phase = 0.0  # phase of a specified oscillator that will calculated in line with current env. states.
  
      neighbors = self.get_neighbors(index)
      for k in neighbors:
          conn_weight = 1.0
          if self._ena_conn_weight is True:
              conn_weight = self._conn_weight[index][k]
  
          phase += (
              conn_weight
              * self._weight
              * math.sin(self._phases[k] - teta)
          )
  
      divider = len(neighbors)
      if divider == 0:
          divider = 1.0
  
      return self._freq[index] + (phase / divider)
  
imports: import math
  import matplotlib.pyplot
  import matplotlib.animation
  from pyclustering.cluster.encoder import type_encoding
  from pyclustering.cluster import cluster_visualizer
  from pyclustering.core.syncnet_wrapper import syncnet_create_network, syncnet_process, syncnet_destroy_network, syncnet_analyser_destroy
  from pyclustering.core.sync_wrapper import sync_connectivity_matrix
  from pyclustering.core.wrapper import ccore_library
  from pyclustering.nnet.sync import sync_dynamic, sync_network, sync_visualizer
  from pyclustering.nnet import conn_represent, initial_type, conn_type, solve_type
  from pyclustering.utils import euclidean_distance
label_desc: Use image node_id 6 for calling the syncnet obj's underlying member method code with example usage: obj._phase_kuramoto(teta, t, argv) and returns: unknown