text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  """FNN for time series from the Ikeda map.
  
  This is a purely deterministic time series, yet we see the second test
  reporting FNNs at large embedding dimensions.  The whole problem is that
  the fraction of FNN strongly depends on the threshold parameters
  used (apart from the metric).
  """
  
  from nolitsa import data, dimension
  import matplotlib.pyplot as plt
  import numpy as np
  
  # Generate data.
  x = data.ikeda(length=5000)[:, 0]
  dim = np.arange(1, 15 + 1)
  
  f1 = dimension.fnn(x, tau=1, dim=dim, window=0, metric='chebyshev')[2]
  f2 = dimension.fnn(x, tau=1, dim=dim, window=0, metric='euclidean')[2]
  f3 = dimension.fnn(x, tau=1, dim=dim, window=0, metric='cityblock')[2]
  
  plt.title(r'FNN for the Ikeda map')
  plt.xlabel(r'Embedding dimension $d$')
  plt.ylabel(r'FNN (%)')
  plt.plot(dim, 100 * f1, 'bo-', label=r'Chebyshev')
  plt.plot(dim, 100 * f2, 'g^-', label=r'Euclidean')
  plt.plot(dim, 100 * f3, 'rs-', label=r'Cityblock')
  plt.legend()
  
  plt.show()
  