class_docstr: Multi-term Fourier fit to a light curve
  
  Parameters
  ----------
  omega : float
      angular frequency of the fundamental mode
  n_terms : int
      the number of Fourier modes to use in the fit
class_docstr_tok: ["Multi-term","Fourier","fit","to","a","light","curve","Parameters","--","--","--","--","--","omega",":","float","angular","frequency","of","the","fundamental","mode","n_terms",":","int","the","number","of","Fourier","modes","to","use","in","the","fit"]
docstr: Compute the phased fit, and optionally return phased times
  
  Parameters
  ----------
  Nphase : int
      Number of terms to use in the phased fit
  return_phased_times : bool
      If True, then return a phased version of the input times
  adjust_offset : bool
      If true, then shift results so that the minimum value is at phase 0
  
  Returns
  -------
  phase, y_fit : ndarrays
      The phase and y value of the best-fit light curve
  phased_times : ndarray
      The phased version of the training times.  Returned if
      return_phased_times is set to  True.
docstr_tok: ["Compute","the","phased","fit",",","and","optionally","return","phased","times","Parameters","--","--","--","--","--","Nphase",":","int","Number","of","terms","to","use","in","the","phased","fit","return_phased_times",":","bool","If","True",",","then","return","a","phased","version","of","the","input","times","adjust_offset",":","bool","If","true",",","then","shift","results","so","that","the","minimum","value","is","at","phase","0","Returns","--","--","--","-","phase",",","y_fit",":","ndarrays","The","phase","and","y","value","of","the","best-fit","light","curve","phased_times",":","ndarray","The","phased","version","of","the","training","times",".","Returned","if","return_phased_times","is","set","to","True","."]
code: def predict(
      self, Nphase, return_phased_times=False, adjust_offset=True
  ):
      """Compute the phased fit, and optionally return phased times
  
      Parameters
      ----------
      Nphase : int
          Number of terms to use in the phased fit
      return_phased_times : bool
          If True, then return a phased version of the input times
      adjust_offset : bool
          If true, then shift results so that the minimum value is at phase 0
  
      Returns
      -------
      phase, y_fit : ndarrays
          The phase and y value of the best-fit light curve
      phased_times : ndarray
          The phased version of the training times.  Returned if
          return_phased_times is set to  True.
      """
      phase_fit = np.linspace(0, 1, Nphase + 1)[:-1]
  
      X_fit = self._make_X(2 * np.pi * phase_fit / self.omega)
      y_fit = np.dot(X_fit, self.w_)
      i_offset = np.argmin(y_fit)
  
      if adjust_offset:
          y_fit = np.concatenate([y_fit[i_offset:], y_fit[:i_offset]])
  
      if return_phased_times:
          if adjust_offset:
              offset = phase_fit[i_offset]
          else:
              offset = 0
          phased_times = (
              self.t_ * self.omega * 0.5 / np.pi - offset
          ) % 1
  
          return phase_fit, y_fit, phased_times
  
      else:
          return phase_fit, y_fit
  
file_path: astroML/astroML/time_series/periodogram.py
imports: import numpy
  from sklearn.utils import check_random_state
  from ..utils.decorators import deprecated
  from ..utils.exceptions import AstroMLDeprecationWarning
label_desc: Use image node_id 4 for calling the MultiTermFit obj's underlying member method code with example usage: obj.predict(Nphase, return_phased_times, adjust_offset) and returns: phase_fit,  y_fit,  phased_times, phase_fit,  y_fit