text: """ Basic functions for manipulating 2d arrays
  
  """
  
  __all__ = ['diag','diagflat','eye','fliplr','flipud','rot90','tri','triu',
             'tril','vander','histogram2d','mask_indices',
             'tril_indices','tril_indices_from','triu_indices','triu_indices_from',
             ]
  
  from numpy.core.numeric import asanyarray, equal, subtract, arange, \
       zeros, greater_equal, multiply, ones, asarray, alltrue, where, \
       empty, diagonal
  
  def fliplr(m):
      """
      Flip array in the left/right direction.
  
      Flip the entries in each row in the left/right direction.
      Columns are preserved, but appear in a different order than before.
  
      Parameters
      ----------
      m : array_like
          Input array.
  
      Returns
      -------
      f : ndarray
          A view of `m` with the columns reversed.  Since a view
          is returned, this operation is :math:`\\mathcal O(1)`.
  
      See Also
      --------
      flipud : Flip array in the up/down direction.
      rot90 : Rotate array counterclockwise.
  
      Notes
      -----
      Equivalent to A[:,::-1]. Does not require the array to be
      two-dimensional.
  
      Examples
      --------
      >>> A = np.diag([1.,2.,3.])
      >>> A
      array([[ 1.,  0.,  0.],
             [ 0.,  2.,  0.],
             [ 0.,  0.,  3.]])
      >>> np.fliplr(A)
      array([[ 0.,  0.,  1.],
             [ 0.,  2.,  0.],
             [ 3.,  0.,  0.]])
  
      >>> A = np.random.randn(2,3,5)
      >>> np.all(np.fliplr(A)==A[:,::-1,...])
      True
  
      """
      m = asanyarray(m)
      if m.ndim < 2:
          raise ValueError("Input must be >= 2-d.")
      return m[:, ::-1]
  
  def flipud(m):
      """
      Flip array in the up/down direction.
  
      Flip the entries in each column in the up/down direction.
      Rows are preserved, but appear in a different order than before.
  
      Parameters
      ----------
      m : array_like
          Input array.
  
      Returns
      -------
      out : array_like
          A view of `m` with the rows reversed.  Since a view is
          returned, this operation is :math:`\\mathcal O(1)`.
  
      See Also
      --------
      fliplr : Flip array in the left/right direction.
      rot90 : Rotate array counterclockwise.
  
      Notes
      -----
      Equivalent to ``A[::-1,...]``.
      Does not require the array to be two-dimensional.
  
      Examples
      --------
      >>> A = np.diag([1.0, 2, 3])
      >>> A
      array([[ 1.,  0.,  0.],
             [ 0.,  2.,  0.],
             [ 0.,  0.,  3.]])
      >>> np.flipud(A)
      array([[ 0.,  0.,  3.],
             [ 0.,  2.,  0.],
             [ 1.,  0.,  0.]])
  
      >>> A = np.random.randn(2,3,5)
      >>> np.all(np.flipud(A)==A[::-1,...])
      True
  
      >>> np.flipud([1,2])
      array([2, 1])
  
      """
      m = asanyarray(m)
      if m.ndim < 1:
          raise ValueError("Input must be >= 1-d.")
      return m[::-1,...]
  
  def rot90(m, k=1):
      """
      Rotate an array by 90 degrees in the counter-clockwise direction.
  
      The first two dimensions are rotated; therefore, the array must be at
      least 2-D.
  
      Parameters
      ----------
      m : array_like
          Array of two or more dimensions.
      k : integer
          Number of times the array is rotated by 90 degrees.
  
      Returns
      -------
      y : ndarray
          Rotated array.
  
      See Also
      --------
      fliplr : Flip an array horizontally.
      flipud : Flip an array vertically.
  
      Examples
      --------
      >>> m = np.array([[1,2],[3,4]], int)
      >>> m
      array([[1, 2],
             [3, 4]])
      >>> np.rot90(m)
      array([[2, 4],
             [1, 3]])
      >>> np.rot90(m, 2)
      array([[4, 3],
             [2, 1]])
  
      """
      m = asanyarray(m)
      if m.ndim < 2:
          raise ValueError("Input must >= 2-d.")
      k = k % 4
      if k == 0:
          return m
      elif k == 1:
          return fliplr(m).swapaxes(0,1)
      elif k == 2:
          return fliplr(flipud(m))
      else:
          # k == 3
          return fliplr(m.swapaxes(0,1))
  
  def eye(N, M=None, k=0, dtype=float):
      """
      Return a 2-D array with ones on the diagonal and zeros elsewhere.
  
      Parameters
      ----------
      N : int
        Number of rows in the output.
      M : int, optional
        Number of columns in the output. If None, defaults to `N`.
      k : int, optional
        Index of the diagonal: 0 (the default) refers to the main diagonal,
        a positive value refers to an upper diagonal, and a negative value
        to a lower diagonal.
      dtype : data-type, optional
        Data-type of the returned array.
  
      Returns
      -------
      I : ndarray of shape (N,M)
        An array where all elements are equal to zero, except for the `k`-th
        diagonal, whose values are equal to one.
  
      See Also
      --------
      identity : (almost) equivalent function
      diag : diagonal 2-D array from a 1-D array specified by the user.
  
      Examples
      --------
      >>> np.eye(2, dtype=int)
      array([[1, 0],
             [0, 1]])
      >>> np.eye(3, k=1)
      array([[ 0.,  1.,  0.],
             [ 0.,  0.,  1.],
             [ 0.,  0.,  0.]])
  
      """
      if M is None:
          M = N
      m = zeros((N, M), dtype=dtype)
      if k >= M:
          return m
      if k >= 0:
          i = k
      else:
          i = (-k) * M
      m[:M-k].flat[i::M+1] = 1
      return m
  
  def diag(v, k=0):
      """
      Extract a diagonal or construct a diagonal array.
  
      See the more detailed documentation for ``numpy.diagonal`` if you use this
      function to extract a diagonal and wish to write to the resulting array;
      whether it returns a copy or a view depends on what version of numpy you
      are using.
  
      Parameters
      ----------
      v : array_like
          If `v` is a 2-D array, return a copy of its `k`-th diagonal.
          If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th
          diagonal.
      k : int, optional
          Diagonal in question. The default is 0. Use `k>0` for diagonals
          above the main diagonal, and `k<0` for diagonals below the main
          diagonal.
  
      Returns
      -------
      out : ndarray
          The extracted diagonal or constructed diagonal array.
  
      See Also
      --------
      diagonal : Return specified diagonals.
      diagflat : Create a 2-D array with the flattened input as a diagonal.
      trace : Sum along diagonals.
      triu : Upper triangle of an array.
      tril : Lower triange of an array.
  
      Examples
      --------
      >>> x = np.arange(9).reshape((3,3))
      >>> x
      array([[0, 1, 2],
             [3, 4, 5],
             [6, 7, 8]])
  
      >>> np.diag(x)
      array([0, 4, 8])
      >>> np.diag(x, k=1)
      array([1, 5])
      >>> np.diag(x, k=-1)
      array([3, 7])
  
      >>> np.diag(np.diag(x))
      array([[0, 0, 0],
             [0, 4, 0],
             [0, 0, 8]])
  
      """
      v = asarray(v)
      s = v.shape
      if len(s) == 1:
          n = s[0]+abs(k)
          res = zeros((n,n), v.dtype)
          if k >= 0:
              i = k
          else:
              i = (-k) * n
          res[:n-k].flat[i::n+1] = v
          return res
      elif len(s) == 2:
          return v.diagonal(k)
      else:
          raise ValueError("Input must be 1- or 2-d.")
  
  def diagflat(v, k=0):
      """
      Create a two-dimensional array with the flattened input as a diagonal.
  
      Parameters
      ----------
      v : array_like
          Input data, which is flattened and set as the `k`-th
          diagonal of the output.
      k : int, optional
          Diagonal to set; 0, the default, corresponds to the "main" diagonal,
          a positive (negative) `k` giving the number of the diagonal above
          (below) the main.
  
      Returns
      -------
      out : ndarray
          The 2-D output array.
  
      See Also
      --------
      diag : MATLAB work-alike for 1-D and 2-D arrays.
      diagonal : Return specified diagonals.
      trace : Sum along diagonals.
  
      Examples
      --------
      >>> np.diagflat([[1,2], [3,4]])
      array([[1, 0, 0, 0],
             [0, 2, 0, 0],
             [0, 0, 3, 0],
             [0, 0, 0, 4]])
  
      >>> np.diagflat([1,2], 1)
      array([[0, 1, 0],
             [0, 0, 2],
             [0, 0, 0]])
  
      """
      try:
          wrap = v.__array_wrap__
      except AttributeError:
          wrap = None
      v = asarray(v).ravel()
      s = len(v)
      n = s + abs(k)
      res = zeros((n,n), v.dtype)
      if (k >= 0):
          i = arange(0,n-k)
          fi = i+k+i*n
      else:
          i = arange(0,n+k)
          fi = i+(i-k)*n
      res.flat[fi] = v
      if not wrap:
          return res
      return wrap(res)
  
  def tri(N, M=None, k=0, dtype=float):
      """
      An array with ones at and below the given diagonal and zeros elsewhere.
  
      Parameters
      ----------
      N : int
          Number of rows in the array.
      M : int, optional
          Number of columns in the array.
          By default, `M` is taken equal to `N`.
      k : int, optional
          The sub-diagonal at and below which the array is filled.
          `k` = 0 is the main diagonal, while `k` < 0 is below it,
          and `k` > 0 is above.  The default is 0.
      dtype : dtype, optional
          Data type of the returned array.  The default is float.
  
      Returns
      -------
      tri : ndarray of shape (N, M)
          Array with its lower triangle filled with ones and zero elsewhere;
          in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise.
  
      Examples
      --------
      >>> np.tri(3, 5, 2, dtype=int)
      array([[1, 1, 1, 0, 0],
             [1, 1, 1, 1, 0],
             [1, 1, 1, 1, 1]])
  
      >>> np.tri(3, 5, -1)
      array([[ 0.,  0.,  0.,  0.,  0.],
             [ 1.,  0.,  0.,  0.,  0.],
             [ 1.,  1.,  0.,  0.,  0.]])
  
      """
      if M is None:
          M = N
      m = greater_equal(subtract.outer(arange(N), arange(M)),-k)
      return m.astype(dtype)
  
  def tril(m, k=0):
      """
      Lower triangle of an array.
  
      Return a copy of an array with elements above the `k`-th diagonal zeroed.
  
      Parameters
      ----------
      m : array_like, shape (M, N)
          Input array.
      k : int, optional
          Diagonal above which to zero elements.  `k = 0` (the default) is the
          main diagonal, `k < 0` is below it and `k > 0` is above.
  
      Returns
      -------
      tril : ndarray, shape (M, N)
          Lower triangle of `m`, of same shape and data-type as `m`.
  
      See Also
      --------
      triu : same thing, only for the upper triangle
  
      Examples
      --------
      >>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
      array([[ 0,  0,  0],
             [ 4,  0,  0],
             [ 7,  8,  0],
             [10, 11, 12]])
  
      """
      m = asanyarray(m)
      out = multiply(tri(m.shape[0], m.shape[1], k=k, dtype=m.dtype),m)
      return out
  
  def triu(m, k=0):
      """
      Upper triangle of an array.
  
      Return a copy of a matrix with the elements below the `k`-th diagonal
      zeroed.
  
      Please refer to the documentation for `tril` for further details.
  
      See Also
      --------
      tril : lower triangle of an array
  
      Examples
      --------
      >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
      array([[ 1,  2,  3],
             [ 4,  5,  6],
             [ 0,  8,  9],
             [ 0,  0, 12]])
  
      """
      m = asanyarray(m)
      out = multiply((1 - tri(m.shape[0], m.shape[1], k - 1, dtype=m.dtype)), m)
      return out
  
  # borrowed from John Hunter and matplotlib
  def vander(x, N=None):
      """
      Generate a Van der Monde matrix.
  
      The columns of the output matrix are decreasing powers of the input
      vector.  Specifically, the `i`-th output column is the input vector
      raised element-wise to the power of ``N - i - 1``.  Such a matrix with
      a geometric progression in each row is named for Alexandre-Theophile
      Vandermonde.
  
      Parameters
      ----------
      x : array_like
          1-D input array.
      N : int, optional
          Order of (number of columns in) the output.  If `N` is not specified,
          a square array is returned (``N = len(x)``).
  
      Returns
      -------
      out : ndarray
          Van der Monde matrix of order `N`.  The first column is ``x^(N-1)``,
          the second ``x^(N-2)`` and so forth.
  
      Examples
      --------
      >>> x = np.array([1, 2, 3, 5])
      >>> N = 3
      >>> np.vander(x, N)
      array([[ 1,  1,  1],
             [ 4,  2,  1],
             [ 9,  3,  1],
             [25,  5,  1]])
  
      >>> np.column_stack([x**(N-1-i) for i in range(N)])
      array([[ 1,  1,  1],
             [ 4,  2,  1],
             [ 9,  3,  1],
             [25,  5,  1]])
  
      >>> x = np.array([1, 2, 3, 5])
      >>> np.vander(x)
      array([[  1,   1,   1,   1],
             [  8,   4,   2,   1],
             [ 27,   9,   3,   1],
             [125,  25,   5,   1]])
  
      The determinant of a square Vandermonde matrix is the product
      of the differences between the values of the input vector:
  
      >>> np.linalg.det(np.vander(x))
      48.000000000000043
      >>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1)
      48
  
      """
      x = asarray(x)
      if N is None:
          N=len(x)
      X = ones( (len(x),N), x.dtype)
      for i in range(N - 1):
          X[:,i] = x**(N - i - 1)
      return X
  
  
  def histogram2d(x, y, bins=10, range=None, normed=False, weights=None):
      """
      Compute the bi-dimensional histogram of two data samples.
  
      Parameters
      ----------
      x : array_like, shape (N,)
          An array containing the x coordinates of the points to be histogrammed.
      y : array_like, shape (N,)
          An array containing the y coordinates of the points to be histogrammed.
      bins : int or [int, int] or array_like or [array, array], optional
          The bin specification:
  
            * If int, the number of bins for the two dimensions (nx=ny=bins).
            * If [int, int], the number of bins in each dimension (nx, ny = bins).
            * If array_like, the bin edges for the two dimensions
              (x_edges=y_edges=bins).
            * If [array, array], the bin edges in each dimension
              (x_edges, y_edges = bins).
  
      range : array_like, shape(2,2), optional
          The leftmost and rightmost edges of the bins along each dimension
          (if not specified explicitly in the `bins` parameters):
          ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
          will be considered outliers and not tallied in the histogram.
      normed : bool, optional
          If False, returns the number of samples in each bin. If True, returns
          the bin density, i.e. the bin count divided by the bin area.
      weights : array_like, shape(N,), optional
          An array of values ``w_i`` weighing each sample ``(x_i, y_i)``. Weights
          are normalized to 1 if `normed` is True. If `normed` is False, the
          values of the returned histogram are equal to the sum of the weights
          belonging to the samples falling into each bin.
  
      Returns
      -------
      H : ndarray, shape(nx, ny)
          The bi-dimensional histogram of samples `x` and `y`. Values in `x`
          are histogrammed along the first dimension and values in `y` are
          histogrammed along the second dimension.
      xedges : ndarray, shape(nx,)
          The bin edges along the first dimension.
      yedges : ndarray, shape(ny,)
          The bin edges along the second dimension.
  
      See Also
      --------
      histogram : 1D histogram
      histogramdd : Multidimensional histogram
  
      Notes
      -----
      When `normed` is True, then the returned histogram is the sample density,
      defined such that:
  
      .. math::
        \\sum_{i=0}^{nx-1} \\sum_{j=0}^{ny-1} H_{i,j} \\Delta x_i \\Delta y_j = 1
  
      where `H` is the histogram array and :math:`\\Delta x_i \\Delta y_i`
      the area of bin ``{i,j}``.
  
      Please note that the histogram does not follow the Cartesian convention
      where `x` values are on the abcissa and `y` values on the ordinate axis.
      Rather, `x` is histogrammed along the first dimension of the array
      (vertical), and `y` along the second dimension of the array (horizontal).
      This ensures compatibility with `histogramdd`.
  
      Examples
      --------
      >>> x, y = np.random.randn(2, 100)
      >>> H, xedges, yedges = np.histogram2d(x, y, bins=(5, 8))
      >>> H.shape, xedges.shape, yedges.shape
      ((5, 8), (6,), (9,))
  
      We can now use the Matplotlib to visualize this 2-dimensional histogram:
  
      >>> extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
      >>> import matplotlib.pyplot as plt
      >>> plt.imshow(H, extent=extent, interpolation='nearest')
      <matplotlib.image.AxesImage object at ...>
      >>> plt.colorbar()
      <matplotlib.colorbar.Colorbar instance at ...>
      >>> plt.show()
  
      """
      from numpy import histogramdd
  
      try:
          N = len(bins)
      except TypeError:
          N = 1
  
      if N != 1 and N != 2:
          xedges = yedges = asarray(bins, float)
          bins = [xedges, yedges]
      hist, edges = histogramdd([x,y], bins, range, normed, weights)
      return hist, edges[0], edges[1]
  
  
  def mask_indices(n, mask_func, k=0):
      """
      Return the indices to access (n, n) arrays, given a masking function.
  
      Assume `mask_func` is a function that, for a square array a of size
      ``(n, n)`` with a possible offset argument `k`, when called as
      ``mask_func(a, k)`` returns a new array with zeros in certain locations
      (functions like `triu` or `tril` do precisely this). Then this function
      returns the indices where the non-zero values would be located.
  
      Parameters
      ----------
      n : int
          The returned indices will be valid to access arrays of shape (n, n).
      mask_func : callable
          A function whose call signature is similar to that of `triu`, `tril`.
          That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`.
          `k` is an optional argument to the function.
      k : scalar
          An optional argument which is passed through to `mask_func`. Functions
          like `triu`, `tril` take a second argument that is interpreted as an
          offset.
  
      Returns
      -------
      indices : tuple of arrays.
          The `n` arrays of indices corresponding to the locations where
          ``mask_func(np.ones((n, n)), k)`` is True.
  
      See Also
      --------
      triu, tril, triu_indices, tril_indices
  
      Notes
      -----
      .. versionadded:: 1.4.0
  
      Examples
      --------
      These are the indices that would allow you to access the upper triangular
      part of any 3x3 array:
  
      >>> iu = np.mask_indices(3, np.triu)
  
      For example, if `a` is a 3x3 array:
  
      >>> a = np.arange(9).reshape(3, 3)
      >>> a
      array([[0, 1, 2],
             [3, 4, 5],
             [6, 7, 8]])
      >>> a[iu]
      array([0, 1, 2, 4, 5, 8])
  
      An offset can be passed also to the masking function.  This gets us the
      indices starting on the first diagonal right of the main one:
  
      >>> iu1 = np.mask_indices(3, np.triu, 1)
  
      with which we now extract only three elements:
  
      >>> a[iu1]
      array([1, 2, 5])
  
      """
      m = ones((n,n), int)
      a = mask_func(m, k)
      return where(a != 0)
  
  
  def tril_indices(n, k=0):
      """
      Return the indices for the lower-triangle of an (n, n) array.
  
      Parameters
      ----------
      n : int
          The row dimension of the square arrays for which the returned
          indices will be valid.
      k : int, optional
          Diagonal offset (see `tril` for details).
  
      Returns
      -------
      inds : tuple of arrays
          The indices for the triangle. The returned tuple contains two arrays,
          each with the indices along one dimension of the array.
  
      See also
      --------
      triu_indices : similar function, for upper-triangular.
      mask_indices : generic function accepting an arbitrary mask function.
      tril, triu
  
      Notes
      -----
      .. versionadded:: 1.4.0
  
      Examples
      --------
      Compute two different sets of indices to access 4x4 arrays, one for the
      lower triangular part starting at the main diagonal, and one starting two
      diagonals further right:
  
      >>> il1 = np.tril_indices(4)
      >>> il2 = np.tril_indices(4, 2)
  
      Here is how they can be used with a sample array:
  
      >>> a = np.arange(16).reshape(4, 4)
      >>> a
      array([[ 0,  1,  2,  3],
             [ 4,  5,  6,  7],
             [ 8,  9, 10, 11],
             [12, 13, 14, 15]])
  
      Both for indexing:
  
      >>> a[il1]
      array([ 0,  4,  5,  8,  9, 10, 12, 13, 14, 15])
  
      And for assigning values:
  
      >>> a[il1] = -1
      >>> a
      array([[-1,  1,  2,  3],
             [-1, -1,  6,  7],
             [-1, -1, -1, 11],
             [-1, -1, -1, -1]])
  
      These cover almost the whole array (two diagonals right of the main one):
  
      >>> a[il2] = -10
      >>> a
      array([[-10, -10, -10,   3],
             [-10, -10, -10, -10],
             [-10, -10, -10, -10],
             [-10, -10, -10, -10]])
  
      """
      return mask_indices(n, tril, k)
  
  
  def tril_indices_from(arr, k=0):
      """
      Return the indices for the lower-triangle of arr.
  
      See `tril_indices` for full details.
  
      Parameters
      ----------
      arr : array_like
          The indices will be valid for square arrays whose dimensions are
          the same as arr.
      k : int, optional
          Diagonal offset (see `tril` for details).
  
      See Also
      --------
      tril_indices, tril
  
      Notes
      -----
      .. versionadded:: 1.4.0
  
      """
      if not (arr.ndim == 2 and arr.shape[0] == arr.shape[1]):
          raise ValueError("input array must be 2-d and square")
      return tril_indices(arr.shape[0], k)
  
  
  def triu_indices(n, k=0):
      """
      Return the indices for the upper-triangle of an (n, n) array.
  
      Parameters
      ----------
      n : int
          The size of the arrays for which the returned indices will
          be valid.
      k : int, optional
          Diagonal offset (see `triu` for details).
  
      Returns
      -------
      inds : tuple, shape(2) of ndarrays, shape(`n`)
          The indices for the triangle. The returned tuple contains two arrays,
          each with the indices along one dimension of the array.  Can be used
          to slice a ndarray of shape(`n`, `n`).
  
      See also
      --------
      tril_indices : similar function, for lower-triangular.
      mask_indices : generic function accepting an arbitrary mask function.
      triu, tril
  
      Notes
      -----
      .. versionadded:: 1.4.0
  
      Examples
      --------
      Compute two different sets of indices to access 4x4 arrays, one for the
      upper triangular part starting at the main diagonal, and one starting two
      diagonals further right:
  
      >>> iu1 = np.triu_indices(4)
      >>> iu2 = np.triu_indices(4, 2)
  
      Here is how they can be used with a sample array:
  
      >>> a = np.arange(16).reshape(4, 4)
      >>> a
      array([[ 0,  1,  2,  3],
             [ 4,  5,  6,  7],
             [ 8,  9, 10, 11],
             [12, 13, 14, 15]])
  
      Both for indexing:
  
      >>> a[iu1]
      array([ 0,  1,  2,  3,  5,  6,  7, 10, 11, 15])
  
      And for assigning values:
  
      >>> a[iu1] = -1
      >>> a
      array([[-1, -1, -1, -1],
             [ 4, -1, -1, -1],
             [ 8,  9, -1, -1],
             [12, 13, 14, -1]])
  
      These cover only a small part of the whole array (two diagonals right
      of the main one):
  
      >>> a[iu2] = -10
      >>> a
      array([[ -1,  -1, -10, -10],
             [  4,  -1,  -1, -10],
             [  8,   9,  -1,  -1],
             [ 12,  13,  14,  -1]])
  
      """
      return mask_indices(n, triu, k)
  
  
  def triu_indices_from(arr, k=0):
      """
      Return the indices for the upper-triangle of a (N, N) array.
  
      See `triu_indices` for full details.
  
      Parameters
      ----------
      arr : ndarray, shape(N, N)
          The indices will be valid for square arrays.
      k : int, optional
          Diagonal offset (see `triu` for details).
  
      Returns
      -------
      triu_indices_from : tuple, shape(2) of ndarray, shape(N)
          Indices for the upper-triangle of `arr`.
  
      See Also
      --------
      triu_indices, triu
  
      Notes
      -----
      .. versionadded:: 1.4.0
  
      """
      if not (arr.ndim == 2 and arr.shape[0] == arr.shape[1]):
          raise ValueError("input array must be 2-d and square")
      return triu_indices(arr.shape[0],k)
  