parameters: (
          self, y, t=None, A=None, U=None, V=None, kernel=None, check_sorted=True
      )
return_statement: return self.solver.dot(
              kernel.jitter,
              alpha_real,
              beta_real,
              alpha_complex_real,
              alpha_complex_imag,
              beta_complex_real,
              beta_complex_imag,
              A,
              U,
              V,
              t,
              np.ascontiguousarray(y, dtype=float),
          )
docstring: Dot the covariance matrix into a vector or matrix
  
          Compute ``K.y`` where ``K`` is the covariance matrix of the GP without
          the white noise or ``yerr`` values on the diagonal.
  
          Args:
              y (array[n] or array[n, nrhs]): The vector or matrix ``y``
                  described above.
              kernel (Optional[terms.Term]): A different kernel can optionally
                  be provided to compute the matrix ``K`` from a different
                  kernel than the ``kernel`` property on this object.
  
          Returns:
              array[n] or array[n, nrhs]: The dot product ``K.y`` as described
              above. This will have the same shape as ``y``.
  
          Raises:
              ValueError: For mismatched dimensions.
docstring_summary: Dot the covariance matrix into a vector or matrix
docstring_tokens: Dot the covariance matrix into a vector or matrix
function: def dot(
          self, y, t=None, A=None, U=None, V=None, kernel=None, check_sorted=True
      ):
          """
          Dot the covariance matrix into a vector or matrix
  
          Compute ``K.y`` where ``K`` is the covariance matrix of the GP without
          the white noise or ``yerr`` values on the diagonal.
  
          Args:
              y (array[n] or array[n, nrhs]): The vector or matrix ``y``
                  described above.
              kernel (Optional[terms.Term]): A different kernel can optionally
                  be provided to compute the matrix ``K`` from a different
                  kernel than the ``kernel`` property on this object.
  
          Returns:
              array[n] or array[n, nrhs]: The dot product ``K.y`` as described
              above. This will have the same shape as ``y``.
  
          Raises:
              ValueError: For mismatched dimensions.
  
          """
          if kernel is None:
              kernel = self.kernel
  
          if t is not None:
              t = np.atleast_1d(t)
              if check_sorted and np.any(np.diff(t) < 0.0):
                  raise ValueError("the input coordinates must be sorted")
              if check_sorted and len(t.shape) > 1:
                  raise ValueError("dimension mismatch")
  
              A = np.empty(0) if A is None else A
              U = np.empty((0, 0)) if U is None else U
              V = np.empty((0, 0)) if V is None else V
          else:
              if not self.computed:
                  raise RuntimeError("you must call 'compute' first")
              t = self._t
              A = self._A
              U = self._U
              V = self._V
  
          (
              alpha_real,
              beta_real,
              alpha_complex_real,
              alpha_complex_imag,
              beta_complex_real,
              beta_complex_imag,
          ) = kernel.coefficients
  
          return self.solver.dot(
              kernel.jitter,
              alpha_real,
              beta_real,
              alpha_complex_real,
              alpha_complex_imag,
              beta_complex_real,
              beta_complex_imag,
              A,
              U,
              V,
              t,
              np.ascontiguousarray(y, dtype=float),
          )
function_tokens: def dot ( self , y , t = None , A = None , U = None , V = None , kernel = None , check_sorted = True ) : if kernel is None : kernel = self . kernel if t is not None : t = np . atleast_1d ( t ) if check_sorted and np . any ( np . diff ( t ) < 0.0 ) : raise ValueError ( "the input coordinates must be sorted" ) if check_sorted and len ( t . shape ) > 1 : raise ValueError ( "dimension mismatch" ) A = np . empty ( 0 ) if A is None else A U = np . empty ( ( 0 , 0 ) ) if U is None else U V = np . empty ( ( 0 , 0 ) ) if V is None else V else : if not self . computed : raise RuntimeError ( "you must call 'compute' first" ) t = self . _t A = self . _A U = self . _U V = self . _V ( alpha_real , beta_real , alpha_complex_real , alpha_complex_imag , beta_complex_real , beta_complex_imag , ) = kernel . coefficients return self . solver . dot ( kernel . jitter , alpha_real , beta_real , alpha_complex_real , alpha_complex_imag , beta_complex_real , beta_complex_imag , A , U , V , t , np . ascontiguousarray ( y , dtype = float ) , )