#! /usr/bin/env python3
#
def wathen_st ( nx, ny, nz_num, seed ):

#*****************************************************************************80
#
## WATHEN_ST: Wathen matrix stored in sparse triplet format.
#
#  Discussion:
#
#    When dealing with sparse matrices in MATLAB, it can be much more efficient
#    to work first with a triple of I, J, and X vectors, and only once
#    they are complete, convert to MATLAB's sparse format.
#
#    The Wathen matrix is a finite element matrix which is sparse.
#
#    The entries of the matrix depend in part on a physical quantity
#    related to density.  That density is here assigned random values between
#    0 and 100.
#
#    The matrix order N is determined by the input quantities NX and NY,
#    which would usually be the number of elements in the X and Y directions.
#
#    The value of N is
#
#      N = 3*NX*NY + 2*NX + 2*NY + 1,
#
#    The matrix is the consistent mass matrix for a regular NX by NY grid
#    of 8 node serendipity elements.
#
#    The local element numbering is
#
#      3--2--1
#      |     |
#      4     8
#      |     |
#      5--6--7
#
#    Here is an illustration for NX = 3, NY = 2:
#
#     23-24-25-26-27-28-29
#      |     |     |     |
#     19    20    21    22
#      |     |     |     |
#     12-13-14-15-16-17-18
#      |     |     |     |
#      8     9    10    11
#      |     |     |     |
#      1--2--3--4--5--6--7
#
#    For this example, the total number of nodes is, as expected,
#
#      N = 3 * 3 * 2 + 2 * 2 + 2 * 3 + 1 = 29
#
#    The matrix is symmetric positive definite for any positive values of the
#    density RHO(X,Y).
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    31 August 2014
#
#  Author:
#
#    John Burkardt.
#
#  Reference:
#
#    Nicholas Higham,
#    Algorithm 694: A Collection of Test Matrices in MATLAB,
#    ACM Transactions on Mathematical Software,
#    Volume 17, Number 3, September 1991, pages 289-305.
#
#    Andrew Wathen,
#    Realistic eigenvalue bounds for the Galerkin mass matrix,
#    IMA Journal of Numerical Analysis,
#    Volume 7, Number 4, October 1987, pages 449-457.
#
#  Parameters:
#
#    Input, integer NX, NY, values which determine the size of the matrix.
#
#    Input, integer NZ_NUM, the number of values used to describe the matrix.
#
#    Input/output, integer SEED, the random number seed.
#
#    Output, integer ROW(NZ_NUM), COL(NZ_NUM), the row and column indices 
#    of the nonzero entries.
#
#    Output, real A(NZ_NUM), the nonzero values.
#
  import numpy as np
  from r8_uniform_01 import r8_uniform_01

  em = np.array \
  ( \
    ( ( 6.0, -6.0,  2.0, -8.0,  3.0, -8.0,  2.0, -6.0 ), \
      (-6.0, 32.0, -6.0, 20.0, -8.0, 16.0, -8.0, 20.0 ), \
      ( 2.0, -6.0,  6.0, -6.0,  2.0, -8.0,  3.0, -8.0 ), \
      (-8.0, 20.0, -6.0, 32.0, -6.0, 20.0, -8.0, 16.0 ), \
      ( 3.0, -8.0,  2.0, -6.0,  6.0, -6.0,  2.0, -8.0 ), \
      (-8.0, 16.0, -8.0, 20.0, -6.0, 32.0, -6.0, 20.0 ), \
      ( 2.0, -8.0,  3.0, -8.0,  2.0, -6.0,  6.0, -6.0 ), \
      (-6.0, 20.0, -8.0, 16.0, -8.0, 20.0, -6.0, 32.0 ) )\
  )

  node = np.zeros ( 8, dtype = np.int32 )
  row = np.zeros ( nz_num, dtype = np.int32 )
  col = np.zeros ( nz_num, dtype = np.int32 )
  a = np.zeros ( nz_num )

  k = 0

  for j in range ( 0, ny ):

    for i in range ( 0, nx ):

      node[0] = ( 3 * ( j + 1 )     ) * nx + 2 * ( j + 1 ) + 2 *  ( i + 1 ) + 1 - 1
      node[1] = node[0] - 1
      node[2] = node[0] - 2

      node[3] = ( 3 * ( j + 1 ) - 1 ) * nx + 2 *  ( j + 1 ) + ( i + 1 ) - 1 - 1
      node[7] = node[3] + 1

      node[4] = ( 3 *  ( j + 1 ) - 3 ) * nx + 2 *  ( j + 1 ) + 2 *  ( i + 1 ) - 3 - 1
      node[5] = node[4] + 1
      node[6] = node[4] + 2

      rho, seed = r8_uniform_01 ( seed )
      rho = 100.0 * rho

      for krow in range ( 0, 8 ):
        for kcol in range ( 0, 8 ):
          row[k] = node[krow]
          col[k] = node[kcol]
          a[k] = rho * em[krow,kcol]
          k = k + 1

  return row, col, a, seed