old_contents: from . import __nnls
  from numpy import asarray_chkfinite, zeros, double
  
  __all__ = ['nnls']
  
  
  def nnls(A, b, maxiter=None):
      """
      Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. This is a wrapper
      for a FORTRAN non-negative least squares solver.
  
      Parameters
      ----------
      A : ndarray
          Matrix ``A`` as shown above.
      b : ndarray
          Right-hand side vector.
      maxiter: int, optional
          Maximum number of iterations, optional.
          Default is ``3 * A.shape[1]``.
  
      Returns
      -------
      x : ndarray
          Solution vector.
      rnorm : float
          The residual, ``|| Ax-b ||_2``.
  
      See Also
      --------
      lsq_linear : Linear least squares with bounds on the variables
  
      Notes
      -----
      The FORTRAN code was published in the book below. The algorithm
      is an active set method. It solves the KKT (Karush-Kuhn-Tucker)
      conditions for the non-negative least squares problem.
  
      References
      ----------
      Lawson C., Hanson R.J., (1987) Solving Least Squares Problems, SIAM
  
       Examples
      --------
      >>> from scipy.optimize import nnls
      ...
      >>> A = np.array([[1, 0], [1, 0], [0, 1]])
      >>> b = np.array([2, 1, 1])
      >>> nnls(A, b)
      (array([1.5, 1. ]), 0.7071067811865475)
  
      >>> b = np.array([-1, -1, -1])
      >>> nnls(A, b)
      (array([0., 0.]), 1.7320508075688772)
  
      """
  
      A, b = map(asarray_chkfinite, (A, b))
  
      if len(A.shape) != 2:
          raise ValueError("Expected a two-dimensional array (matrix)" +
                           ", but the shape of A is %s" % (A.shape, ))
      if len(b.shape) != 1:
          raise ValueError("Expected a one-dimensional array (vector" +
                           ", but the shape of b is %s" % (b.shape, ))
  
      m, n = A.shape
  
      if m != b.shape[0]:
          raise ValueError(
                  "Incompatible dimensions. The first dimension of " +
                  "A is %s, while the shape of b is %s" % (m, (b.shape[0], )))
  
      maxiter = -1 if maxiter is None else int(maxiter)
  
      w = zeros((n,), dtype=double)
      zz = zeros((m,), dtype=double)
      index = zeros((n,), dtype=int)
  
      x, rnorm, mode = __nnls.nnls(A, m, n, b, w, zz, index, maxiter)
      if mode != 1:
          raise RuntimeError("too many iterations")
  
      return x, rnorm
  
new_contents: from . import __nnls
  from numpy import asarray_chkfinite, zeros, double
  
  __all__ = ['nnls']
  
  
  def nnls(A, b, maxiter=None):
      """
      Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. This is a wrapper
      for a FORTRAN non-negative least squares solver.
  
      Parameters
      ----------
      A : ndarray
          Matrix ``A`` as shown above.
      b : ndarray
          Right-hand side vector.
      maxiter: int, optional
          Maximum number of iterations, optional.
          Default is ``3 * A.shape[1]``.
  
      Returns
      -------
      x : ndarray
          Solution vector.
      rnorm : float
          The residual, ``|| Ax-b ||_2``.
  
      See Also
      --------
      lsq_linear : Linear least squares with bounds on the variables
  
      Notes
      -----
      The FORTRAN code was published in the book below. The algorithm
      is an active set method. It solves the KKT (Karush-Kuhn-Tucker)
      conditions for the non-negative least squares problem.
  
      References
      ----------
      Lawson C., Hanson R.J., (1987) Solving Least Squares Problems, SIAM
  
       Examples
      --------
      >>> from scipy.optimize import nnls
      ...
      >>> A = np.array([[1, 0], [1, 0], [0, 1]])
      >>> b = np.array([2, 1, 1])
      >>> nnls(A, b)
      (array([1.5, 1. ]), 0.7071067811865475)
  
      >>> b = np.array([-1, -1, -1])
      >>> nnls(A, b)
      (array([0., 0.]), 1.7320508075688772)
  
      """
  
      A, b = map(asarray_chkfinite, (A, b))
  
      if len(A.shape) != 2:
          raise ValueError("Expected a two-dimensional array (matrix)" +
                           ", but the shape of A is %s" % (A.shape, ))
      if len(b.shape) != 1:
          raise ValueError("Expected a one-dimensional array (vector)" +
                           ", but the shape of b is %s" % (b.shape, ))
  
      m, n = A.shape
  
      if m != b.shape[0]:
          raise ValueError(
                  "Incompatible dimensions. The first dimension of " +
                  "A is %s, while the shape of b is %s" % (m, (b.shape[0], )))
  
      maxiter = -1 if maxiter is None else int(maxiter)
  
      w = zeros((n,), dtype=double)
      zz = zeros((m,), dtype=double)
      index = zeros((n,), dtype=int)
  
      x, rnorm, mode = __nnls.nnls(A, m, n, b, w, zz, index, maxiter)
      if mode != 1:
          raise RuntimeError("too many iterations")
  
      return x, rnorm
  
subject: Update _nnls.py