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
docstr: Returns the summary of the power law fit.
  
  @param significance: the significance level of the Kolmogorov-Smirnov test
    used to decide whether the input data could have come from the fitted
    distribution
  @return: the summary as a string
docstr_tok: ["Returns","the","summary","of","the","power","law","fit",".","@","param","significance",":","the","significance","level","of","the","Kolmogorov-Smirnov","test","used","to","decide","whether","the","input","data","could","have","come","from","the","fitted","distribution","@","return",":","the","summary","as","a","string"]
code: def summary(self, significance=0.05):
      """Returns the summary of the power law fit.
  
      @param significance: the significance level of the Kolmogorov-Smirnov test
        used to decide whether the input data could have come from the fitted
        distribution
      @return: the summary as a string
      """
      result = [
          "Fitted power-law distribution on %s data"
          % ("discrete", "continuous")[bool(self.continuous)]
      ]
      result.append("")
      result.append("Exponent (alpha)  = %f" % self.alpha)
      result.append("Cutoff (xmin)     = %f" % self.xmin)
      result.append("")
      result.append("Log-likelihood    = %f" % self.L)
      result.append("")
      result.append("H0: data was drawn from the fitted distribution")
      result.append("")
      result.append("KS test statistic = %f" % self.D)
      result.append("p-value           = %f" % self.p)
      result.append("")
      if self.p < significance:
          result.append(
              "H0 rejected at significance level %g" % significance
          )
      else:
          result.append(
              "H0 could not be rejected at significance "
              "level %g" % significance
          )
  
      return "\n".join(result)
  
imports: import math
label_desc: Use image node_id 4 for calling the FittedPowerLaw obj's underlying member method code with example usage: obj.summary(significance) and returns: str