class_docstr: Result of fitting a power-law to a vector of samples
  
  Example:
  
      >>> result = power_law_fit([1, 2, 3, 4, 5, 6])
      >>> result                   # doctest:+ELLIPSIS
      FittedPowerLaw(continuous=False, alpha=2.42..., xmin=3.0, L=-7.54..., D=0.21..., p=0.993...)
      >>> print(result)            # doctest:+ELLIPSIS
      Fitted power-law distribution on discrete data
      <BLANKLINE>
      Exponent (alpha)  = 2.42...
      Cutoff (xmin)     = 3.000000
      <BLANKLINE>
      Log-likelihood    = -7.54...
      <BLANKLINE>
      H0: data was drawn from the fitted distribution
      <BLANKLINE>
      KS test statistic = 0.21...
      p-value           = 0.993...
      <BLANKLINE>
      H0 could not be rejected at significance level 0.05
      >>> result.alpha             # doctest:+ELLIPSIS
      2.42...
      >>> result.xmin
      3.0
      >>> result.continuous
      False
code: def __str__(self):
      return self.summary(significance=0.05)
  
imports: import math
label_desc: Use image node_id 3 for calling the FittedPowerLaw obj's underlying member method code with example usage: obj.__str__() and returns: self