class_docstr: Simulates the velocity of a dissipative soliton (kind of self organized particle) [6]_.
  The equilibrium velocity without noise R=0 for
  $   au>1.0/\kappa_3$ is $\kappa_3 \sqrt{(tau - 1.0/\kappa_3)/Q}.
  Before the drift-bifurcation $      au \le 1.0/\kappa_3$ the velocity is zero.
  
  References
  ----------
  
  .. [6] Andreas Kempa-Liehr (2013, p. 159-170)
      Dynamics of Dissipative Soliton
      Dissipative Solitons in Reaction Diffusion Systems.
      Springer: Berlin
  
  
  >>> ds = velocity(tau=3.5) # Dissipative soliton with equilibrium velocity 1.5e-3
  >>> print(ds.label) # Discriminating before or beyond Drift-Bifurcation
  1
  
  # Equilibrium velocity
  >>> print(ds.deterministic)
  0.0015191090506254991
  
  # Simulated velocity as a time series with 20000 time steps being disturbed by Gaussian white noise
  >>> v = ds.simulate(20000)
docstr: :param tau: Bifurcation parameter determining the intrinsic velocity of the dissipative soliton,
              which is zero for tau<=1.0/kappa_3 and np.sqrt(kappa_3**3/Q * (tau - 1.0/kappa_3)) otherwise
  :type tau: float
  :param kappa_3: Inverse bifurcation point.
  :type kappa_3:
  :param Q: Shape parameter of dissipative soliton
  :type Q: float
  :param R: Noise amplitude
  :type R: float
  :param delta_t: temporal discretization
  :type delta_t: float
code: def __init__(
      self,
      tau=3.8,
      kappa_3=0.3,
      Q=1950.0,
      R=3e-4,
      delta_t=0.05,
      seed=None,
  ):
      """
      :param tau: Bifurcation parameter determining the intrinsic velocity of the dissipative soliton,
                  which is zero for tau<=1.0/kappa_3 and np.sqrt(kappa_3**3/Q * (tau - 1.0/kappa_3)) otherwise
      :type tau: float
      :param kappa_3: Inverse bifurcation point.
      :type kappa_3:
      :param Q: Shape parameter of dissipative soliton
      :type Q: float
      :param R: Noise amplitude
      :type R: float
      :param delta_t: temporal discretization
      :type delta_t: float
      """
      # done: add start seed
  
      self.delta_t = delta_t
      self.kappa_3 = kappa_3
      self.Q = Q
      self.tau = tau
      self.a = self.delta_t * kappa_3**2 * (tau - 1.0 / kappa_3)
      self.b = self.delta_t * Q / kappa_3
      self.label = int(tau > 1.0 / kappa_3)
      self.c = np.sqrt(self.delta_t) * R
      self.delta_t = self.delta_t
  
      if seed is not None:
          np.random.seed(seed)
  
      if tau <= 1.0 / kappa_3:
          self.deterministic = 0.0
      else:
          self.deterministic = kappa_3**1.5 * np.sqrt(
              (tau - 1.0 / kappa_3) / Q
          )
  
file_path: tsfresh/tsfresh/examples/driftbif_simulation.py
imports: import logging
  import numpy
  import pandas
label_desc: Use image node_id 1 to create a new velocity object  with example: obj = velocity(tau, kappa_3, Q, R, delta_t, seed)