code: def init_kernel(self):
          '''
          Initializes the covariance matrix with a guess at
          the GP kernel parameters.
  
          '''
  
          if self.kernel_params is None:
              X = self.apply_mask(self.fpix / self.flux.reshape(-1, 1))
              y = self.apply_mask(self.flux) - np.dot(X, np.linalg.solve(
                  np.dot(X.T, X), np.dot(X.T, self.apply_mask(self.flux))))
              white = np.nanmedian([np.nanstd(c) for c in Chunks(y, 13)])
              amp = self.gp_factor * np.nanstd(y)
              tau = 30.0
              if self.kernel == 'Basic':
                  self.kernel_params = [white, amp, tau]
              elif self.kernel == 'QuasiPeriodic':
                  self.kernel_params = [white, amp, 1., 20.f init_kernel(self):
          '''
          Initializes the covariance matrix with a guess at
          the GP kernel parameters.
  
          '''
  
          if self.kernel_params is None:
              X = self.apply_mask(self.fpix / self.flux.reshape(-1, 1))
              y = self.apply_mask(self.flux) - np.dot(X, np.linalg.solve(
                  np.dot(X.T, X), np.dot(X.T, self.apply_mask(self.flux))))
              white = np.nanmedian([np.nanstd(c) for c in Chunks(y, 13)])
              amp = self.gp_factor * np.nanstd(y)
              tau = 30.0
              if self.kernel == 'Basic':
                  self.kernel_params = [white, amp, tau]
              elif self.kernel == 'QuasiPeriodic':
                  self.kernel_params = [white, amp, 1., 20.]
docs: Initializes the covariance matrix with a guess at
          the GP kernel parameters.