parameters: (f, s)
return_statement: return a
docstring: Create a Leslie matrix.
  
      Given the length n array of fecundity coefficients `f` and the length
      n-1 array of survival coefficients `s`, return the associated Leslie matrix.
  
      Parameters
      ----------
      f : (N,) array_like
          The "fecundity" coefficients.
      s : (N-1,) array_like
          The "survival" coefficients, has to be 1-D.  The length of `s`
          must be one less than the length of `f`, and it must be at least 1.
  
      Returns
      -------
      L : (N, N) ndarray
          The array is zero except for the first row,
          which is `f`, and the first sub-diagonal, which is `s`.
          The data-type of the array will be the data-type of ``f[0]+s[0]``.
  
      Notes
      -----
      .. versionadded:: 0.8.0
  
      The Leslie matrix is used to model discrete-time, age-structured
      population growth [1]_ [2]_. In a population with `n` age classes, two sets
      of parameters define a Leslie matrix: the `n` "fecundity coefficients",
      which give the number of offspring per-capita produced by each age
      class, and the `n` - 1 "survival coefficients", which give the
      per-capita survival rate of each age class.
  
      References
      ----------
      .. [1] P. H. Leslie, On the use of matrices in certain population
             mathematics, Biometrika, Vol. 33, No. 3, 183--212 (Nov. 1945)
      .. [2] P. H. Leslie, Some further notes on the use of matrices in
             population mathematics, Biometrika, Vol. 35, No. 3/4, 213--245
             (Dec. 1948)
  
      Examples
      --------
      >>> from scipy.linalg import leslie
      >>> leslie([0.1, 2.0, 1.0, 0.1], [0.2, 0.8, 0.7])
      array([[ 0.1,  2. ,  1. ,  0.1],
             [ 0.2,  0. ,  0. ,  0. ],
             [ 0. ,  0.8,  0. ,  0. ],
             [ 0. ,  0. ,  0.7,  0. ]])
docstring_summary: Create a Leslie matrix.
docstring_tokens: Create a Leslie matrix .
function: def leslie(f, s):
      """
      Create a Leslie matrix.
  
      Given the length n array of fecundity coefficients `f` and the length
      n-1 array of survival coefficients `s`, return the associated Leslie matrix.
  
      Parameters
      ----------
      f : (N,) array_like
          The "fecundity" coefficients.
      s : (N-1,) array_like
          The "survival" coefficients, has to be 1-D.  The length of `s`
          must be one less than the length of `f`, and it must be at least 1.
  
      Returns
      -------
      L : (N, N) ndarray
          The array is zero except for the first row,
          which is `f`, and the first sub-diagonal, which is `s`.
          The data-type of the array will be the data-type of ``f[0]+s[0]``.
  
      Notes
      -----
      .. versionadded:: 0.8.0
  
      The Leslie matrix is used to model discrete-time, age-structured
      population growth [1]_ [2]_. In a population with `n` age classes, two sets
      of parameters define a Leslie matrix: the `n` "fecundity coefficients",
      which give the number of offspring per-capita produced by each age
      class, and the `n` - 1 "survival coefficients", which give the
      per-capita survival rate of each age class.
  
      References
      ----------
      .. [1] P. H. Leslie, On the use of matrices in certain population
             mathematics, Biometrika, Vol. 33, No. 3, 183--212 (Nov. 1945)
      .. [2] P. H. Leslie, Some further notes on the use of matrices in
             population mathematics, Biometrika, Vol. 35, No. 3/4, 213--245
             (Dec. 1948)
  
      Examples
      --------
      >>> from scipy.linalg import leslie
      >>> leslie([0.1, 2.0, 1.0, 0.1], [0.2, 0.8, 0.7])
      array([[ 0.1,  2. ,  1. ,  0.1],
             [ 0.2,  0. ,  0. ,  0. ],
             [ 0. ,  0.8,  0. ,  0. ],
             [ 0. ,  0. ,  0.7,  0. ]])
  
      """
      f = np.atleast_1d(f)
      s = np.atleast_1d(s)
      if f.ndim != 1:
          raise ValueError("Incorrect shape for f.  f must be one-dimensional")
      if s.ndim != 1:
          raise ValueError("Incorrect shape for s.  s must be one-dimensional")
      if f.size != s.size + 1:
          raise ValueError("Incorrect lengths for f and s.  The length"
                           " of s must be one less than the length of f.")
      if s.size == 0:
          raise ValueError("The length of s must be at least 1.")
  
      tmp = f[0] + s[0]
      n = f.size
      a = np.zeros((n, n), dtype=tmp.dtype)
      a[0] = f
      a[list(range(1, n)), list(range(0, n - 1))] = s
      return a
function_tokens: def leslie ( f , s ) : f = np . atleast_1d ( f ) s = np . atleast_1d ( s ) if f . ndim != 1 : raise ValueError ( "Incorrect shape for f.  f must be one-dimensional" ) if s . ndim != 1 : raise ValueError ( "Incorrect shape for s.  s must be one-dimensional" ) if f . size != s . size + 1 : raise ValueError ( "Incorrect lengths for f and s.  The length" " of s must be one less than the length of f." ) if s . size == 0 : raise ValueError ( "The length of s must be at least 1." ) tmp = f [ 0 ] + s [ 0 ] n = f . size a = np . zeros ( ( n , n ) , dtype = tmp . dtype ) a [ 0 ] = f a [ list ( range ( 1 , n ) ) , list ( range ( 0 , n - 1 ) ) ] = s return a