old_contents: """
  
  P4J is a python package for period detection on irregularly sampled and heteroscedastic
  time series based on Information Theoretic objective functions. P4J was
  developed for astronomical light curves, irregularly sampled time series
  of stellar magnitude or flux. The core of this package is a class called periodogram that 
  sweeps an array of periods/frequencies looking for the one that maximizes a given criteria. 
  The main contribution of this work is a criterion for period detection based on the maximization of
  Cauchy-Schwarz Quadratic Mutual Information [1]. Information theoretic criteria incorporate 
  information on the whole probability density function of the process and are more robust than 
  classical second-order statistics based criteria [2, 3, 4]. For comparison P4J also 
  incorporates classical methods for period detection used in astronomy such as
  Phase Dispersion Minimization [5], Lafler-Kinman's string length [6], and the 
  orthogonal multiharmonic AoV periodogram [7].
  
  Contents:
  
  -  Quadratic Mutual Information periodogram for light curves 
  -  Phase Dispersion Minimization, String Length, Orthogonal Multiharmonic AoV
  -  Basic synthetic light curve generator
  
  
  https://github.com/phuijse/P4J
  
  """
  
  __version__ = '0.27'
  
  from .generator import synthetic_light_curve_generator
  from .periodograms import periodogram, MultiBandPeriodogram
  
  
  
new_contents: """
  
  P4J is a python package for period detection on irregularly sampled and heteroscedastic
  time series based on Information Theoretic objective functions. P4J was
  developed for astronomical light curves, irregularly sampled time series
  of stellar magnitude or flux. The core of this package is a class called periodogram that 
  sweeps an array of periods/frequencies looking for the one that maximizes a given criteria. 
  The main contribution of this work is a criterion for period detection based on the maximization of
  Cauchy-Schwarz Quadratic Mutual Information [1]. Information theoretic criteria incorporate 
  information on the whole probability density function of the process and are more robust than 
  classical second-order statistics based criteria [2, 3, 4]. For comparison P4J also 
  incorporates classical methods for period detection used in astronomy such as
  Phase Dispersion Minimization [5], Lafler-Kinman's string length [6], and the 
  orthogonal multiharmonic AoV periodogram [7].
  
  Contents:
  
  -  Quadratic Mutual Information periodogram for light curves 
  -  Phase Dispersion Minimization, String Length, Analysis of Variance and Orthogonal Multiharmonic AoV
  -  Basic synthetic light curve generator
  
  
  https://github.com/phuijse/P4J
  
  """
  
  __version__ = '1.0.0'
  
  from .generator import synthetic_light_curve_generator
  from .periodograms import periodogram, MultiBandPeriodogram
  
  
  
subject: Bump version to 1.0.0, hooray