def maxij_plu(n):
# *****************************************************************************80
#
## MAXIJ_PLU returns the PLU factors of the MAXIJ matrix.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 23 March 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
#
# Output, real P(N,N), L(N,N), U(N,N), the PLU factors.
#
import numpy as np
from i4_wrap import i4_wrap
p = np.zeros((n, n))
for j in range(0, n):
for i in range(0, n):
if i4_wrap(j - i, 1, n) == 1:
p[i, j] = 1.0
l = np.zeros((n, n))
l[0, 0] = 1.0
j = 0
for i in range(1, n):
l[i, j] = float(i) / float(n)
for j in range(1, n):
l[j, j] = 1.0
u = np.zeros((n, n))
for j in range(0, n):
u[0, j] = float(n)
for i in range(1, n):
for j in range(i, n):
u[i, j] = float(j + 1 - i)
return p, l, u
def maxij_plu(n):
#*****************************************************************************80
#
## MAXIJ_PLU returns the PLU factors of the MAXIJ matrix.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 23 March 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
#
# Output, real P(N,N), L(N,N), U(N,N), the PLU factors.
#
import numpy as np
from i4_wrap import i4_wrap
p = np.zeros((n, n))
for j in range(0, n):
for i in range(0, n):
if (i4_wrap(j - i, 1, n) == 1):
p[i, j] = 1.0
l = np.zeros((n, n))
l[0, 0] = 1.0
j = 0
for i in range(1, n):
l[i, j] = float(i) / float(n)
for j in range(1, n):
l[j, j] = 1.0
u = np.zeros((n, n))
for j in range(0, n):
u[0, j] = float(n)
for i in range(1, n):
for j in range(i, n):
u[i, j] = float(j + 1 - i)
return p, l, u
def daub8(n):
#*****************************************************************************80
#
## DAUB8 returns the DAUB8 matrix.
#
# Discussion:
#
# The DAUB8 matrix is the Daubechies wavelet transformation matrix
# with 8 coefficients.
#
# Properties:
#
# The family of matrices is nested as a function of N.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 12 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
# N must be at least 8 and a multiple of 2.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from math import sqrt
from i4_wrap import i4_wrap
from sys import exit
if (n < 8 or (n % 2) != 0):
print ''
print 'DAUB8 - Fatal error!'
print ' N must be at least 8 and a multiple of 2.'
exit('DAUB8 - Fatal error!')
a = np.zeros([n, n])
c0 = 0.2303778133088964
c1 = 0.7148465705529154
c2 = 0.6308807679298587
c3 = -0.0279837694168599
c4 = -0.1870348117190931
c5 = 0.0308413818355607
c6 = 0.0328830116668852
c7 = -0.0105974017850690
for i in range(0, n - 1, 2):
a[i, i] = c0
a[i, i + 1] = c1
a[i, i4_wrap(i + 2, 0, n - 1)] = c2
a[i, i4_wrap(i + 3, 0, n - 1)] = c3
a[i, i4_wrap(i + 4, 0, n - 1)] = c4
a[i, i4_wrap(i + 5, 0, n - 1)] = c5
a[i, i4_wrap(i + 6, 0, n - 1)] = c6
a[i, i4_wrap(i + 7, 0, n - 1)] = c7
a[i + 1, i] = c7
a[i + 1, i + 1] = -c6
a[i + 1, i4_wrap(i + 2, 0, n - 1)] = c5
a[i + 1, i4_wrap(i + 3, 0, n - 1)] = -c4
a[i + 1, i4_wrap(i + 4, 0, n - 1)] = c3
a[i + 1, i4_wrap(i + 5, 0, n - 1)] = -c2
a[i + 1, i4_wrap(i + 6, 0, n - 1)] = c1
a[i + 1, i4_wrap(i + 7, 0, n - 1)] = -c0
return a
def dif2cyclic(n):
#*****************************************************************************80
#
## DIF2CYCLIC returns the cyclic second difference matrix.
#
# Example:
#
# N = 5
#
# 2 -1 . . -1
# -1 2 -1 . .
# . -1 2 -1 .
# . . -1 2 -1
# -1 . . -1 2
#
# Square Properties:
#
# A is symmetric: A' = A.
#
# Because A is symmetric, it is normal.
#
# Because A is normal, it is diagonalizable.
#
# A is persymmetric: A(I,J) = A(N+1-J,N+1-I).
#
# A is centrosymmetric: A(I,J) = A(N+1-I,N+1-J).
#
# A is a circulant: each row is shifted once to get the next row.
#
# A is (weakly) row diagonally dominant.
#
# A is (weakly) column diagonally dominant.
#
# A is singular.
#
# det ( A ) = 0.
#
# (1,1,...,1) is a null vector of A.
#
# A is cyclic tridiagonal.
#
# A is Toeplitz: constant along diagonals.
#
# A has constant row sum = 0.
#
# A has constant column sum = 0.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 22 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of A.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from i4_wrap import i4_wrap
a = np.zeros([n, n])
for i in range(0, n):
im1 = i4_wrap(i - 1, 0, n - 1)
a[i, im1] = -1.0
a[i, i] = 2.0
ip1 = i4_wrap(i + 1, 0, n - 1)
a[i, ip1] = -1.0
return a
def daub6(n):
#*****************************************************************************80
#
## DAUB6 returns the DAUB6 matrix.
#
# Discussion:
#
# The DAUB6 matrix is the Daubechies wavelet transformation matrix
# with 6 coefficients.
#
# Properties:
#
# The family of matrices is nested as a function of N.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 11 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
# N must be at least 6 and a multiple of 2.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from math import sqrt
from i4_wrap import i4_wrap
from sys import exit
if (n < 6 or (n % 2) != 0):
print ''
print 'DAUB6 - Fatal error!'
print ' N must be at least 6 and a multiple of 2.'
exit('DAUB6 - Fatal error!')
a = np.zeros([n, n])
c0 = 1.0 + sqrt(10.0) + sqrt(5.0 + sqrt(40.0))
c1 = 5.0 + sqrt(10.0) + 3.0 * sqrt(5.0 + sqrt(40.0))
c2 = 10.0 - sqrt(40.0) + 2.0 * sqrt(5.0 + sqrt(40.0))
c3 = 10.0 - sqrt(40.0) - 2.0 * sqrt(5.0 + sqrt(40.0))
c4 = 5.0 + sqrt(10.0) - 3.0 * sqrt(5.0 + sqrt(40.0))
c5 = 1.0 + sqrt(10.0) - sqrt(5.0 + sqrt(40.0))
c0 = c0 / sqrt(512.0)
c1 = c1 / sqrt(512.0)
c2 = c2 / sqrt(512.0)
c3 = c3 / sqrt(512.0)
c4 = c4 / sqrt(512.0)
c5 = c5 / sqrt(512.0)
for i in range(0, n - 1, 2):
a[i, i] = c0
a[i, i + 1] = c1
a[i, i4_wrap(i + 2, 0, n - 1)] = c2
a[i, i4_wrap(i + 3, 0, n - 1)] = c3
a[i, i4_wrap(i + 4, 0, n - 1)] = c4
a[i, i4_wrap(i + 5, 0, n - 1)] = c5
a[i + 1, i] = c5
a[i + 1, i + 1] = -c4
a[i + 1, i4_wrap(i + 2, 0, n - 1)] = c3
a[i + 1, i4_wrap(i + 3, 0, n - 1)] = -c2
a[i + 1, i4_wrap(i + 4, 0, n - 1)] = c1
a[i + 1, i4_wrap(i + 5, 0, n - 1)] = -c0
return a
def daub10(n):
#*****************************************************************************80
#
## DAUB10 returns the DAUB10 matrix.
#
# Discussion:
#
# The DAUB10 matrix is the Daubechies wavelet transformation matrix
# with 10 coefficients.
#
# Properties:
#
# The family of matrices is nested as a function of N.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 14 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
# N must be at least 10 and a multiple of 2.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from math import sqrt
from i4_wrap import i4_wrap
from sys import exit
if (n < 10 or (n % 2) != 0):
print ''
print 'DAUB10 - Fatal error!'
print ' N must be at least 10 and a multiple of 2.'
exit('DAUB10 - Fatal error!')
a = np.zeros([n, n])
c = np.array ( [ \
0.1601023979741929, \
0.6038292697971895, \
0.7243085284377726, \
0.1384281459013203, \
-0.2422948870663823, \
-0.0322448695846381, \
0.0775714938400459, \
-0.0062414902127983, \
-0.0125807519990820, \
0.0033357252854738 ] )
for i in range(0, n - 1, 2):
a[i, i] = c[0]
a[i, i + 1] = c[1]
a[i, i4_wrap(i + 2, 0, n - 1)] = c[2]
a[i, i4_wrap(i + 3, 0, n - 1)] = c[3]
a[i, i4_wrap(i + 4, 0, n - 1)] = c[4]
a[i, i4_wrap(i + 5, 0, n - 1)] = c[5]
a[i, i4_wrap(i + 6, 0, n - 1)] = c[6]
a[i, i4_wrap(i + 7, 0, n - 1)] = c[7]
a[i, i4_wrap(i + 8, 0, n - 1)] = c[8]
a[i, i4_wrap(i + 9, 0, n - 1)] = c[9]
a[i + 1, i] = c[9]
a[i + 1, i + 1] = -c[8]
a[i + 1, i4_wrap(i + 2, 0, n - 1)] = c[7]
a[i + 1, i4_wrap(i + 3, 0, n - 1)] = -c[6]
a[i + 1, i4_wrap(i + 4, 0, n - 1)] = c[5]
a[i + 1, i4_wrap(i + 5, 0, n - 1)] = -c[4]
a[i + 1, i4_wrap(i + 6, 0, n - 1)] = c[3]
a[i + 1, i4_wrap(i + 7, 0, n - 1)] = -c[2]
a[i + 1, i4_wrap(i + 8, 0, n - 1)] = c[1]
a[i + 1, i4_wrap(i + 9, 0, n - 1)] = -c[0]
return a
def daub6 ( n ):
#*****************************************************************************80
#
## DAUB6 returns the DAUB6 matrix.
#
# Discussion:
#
# The DAUB6 matrix is the Daubechies wavelet transformation matrix
# with 6 coefficients.
#
# Properties:
#
# The family of matrices is nested as a function of N.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 11 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
# N must be at least 6 and a multiple of 2.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from math import sqrt
from i4_wrap import i4_wrap
from sys import exit
if ( n < 6 or ( n % 2 ) != 0 ):
print ''
print 'DAUB6 - Fatal error!'
print ' N must be at least 6 and a multiple of 2.'
exit ( 'DAUB6 - Fatal error!' )
a = np.zeros ( [ n, n ] )
c0 = 1.0 + sqrt ( 10.0 ) + sqrt ( 5.0 + sqrt ( 40.0 ) )
c1 = 5.0 + sqrt ( 10.0 ) + 3.0 * sqrt ( 5.0 + sqrt ( 40.0 ) )
c2 = 10.0 - sqrt ( 40.0 ) + 2.0 * sqrt ( 5.0 + sqrt ( 40.0 ) )
c3 = 10.0 - sqrt ( 40.0 ) - 2.0 * sqrt ( 5.0 + sqrt ( 40.0 ) )
c4 = 5.0 + sqrt ( 10.0 ) - 3.0 * sqrt ( 5.0 + sqrt ( 40.0 ) )
c5 = 1.0 + sqrt ( 10.0 ) - sqrt ( 5.0 + sqrt ( 40.0 ) )
c0 = c0 / sqrt ( 512.0 )
c1 = c1 / sqrt ( 512.0 )
c2 = c2 / sqrt ( 512.0 )
c3 = c3 / sqrt ( 512.0 )
c4 = c4 / sqrt ( 512.0 )
c5 = c5 / sqrt ( 512.0 )
for i in range ( 0, n - 1, 2 ):
a[i,i] = c0
a[i,i+1] = c1
a[i,i4_wrap(i+2,0,n-1)] = c2
a[i,i4_wrap(i+3,0,n-1)] = c3
a[i,i4_wrap(i+4,0,n-1)] = c4
a[i,i4_wrap(i+5,0,n-1)] = c5
a[i+1,i] = c5
a[i+1,i+1] = - c4
a[i+1,i4_wrap(i+2,0,n-1)] = c3
a[i+1,i4_wrap(i+3,0,n-1)] = - c2
a[i+1,i4_wrap(i+4,0,n-1)] = c1
a[i+1,i4_wrap(i+5,0,n-1)] = - c0
return a
def dif2cyclic ( n ):
#*****************************************************************************80
#
## DIF2CYCLIC returns the cyclic second difference matrix.
#
# Example:
#
# N = 5
#
# 2 -1 . . -1
# -1 2 -1 . .
# . -1 2 -1 .
# . . -1 2 -1
# -1 . . -1 2
#
# Square Properties:
#
# A is symmetric: A' = A.
#
# Because A is symmetric, it is normal.
#
# Because A is normal, it is diagonalizable.
#
# A is persymmetric: A(I,J) = A(N+1-J,N+1-I).
#
# A is centrosymmetric: A(I,J) = A(N+1-I,N+1-J).
#
# A is a circulant: each row is shifted once to get the next row.
#
# A is (weakly) row diagonally dominant.
#
# A is (weakly) column diagonally dominant.
#
# A is singular.
#
# det ( A ) = 0.
#
# (1,1,...,1) is a null vector of A.
#
# A is cyclic tridiagonal.
#
# A is Toeplitz: constant along diagonals.
#
# A has constant row sum = 0.
#
# A has constant column sum = 0.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 22 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of A.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from i4_wrap import i4_wrap
a = np.zeros ( [ n, n ] )
for i in range ( 0, n ):
im1 = i4_wrap ( i - 1, 0, n - 1 )
a[i,im1] = -1.0
a[i,i] = 2.0
ip1 = i4_wrap ( i + 1, 0, n - 1 )
a[i,ip1] = -1.0
return a
def daub10 ( n ):
#*****************************************************************************80
#
## DAUB10 returns the DAUB10 matrix.
#
# Discussion:
#
# The DAUB10 matrix is the Daubechies wavelet transformation matrix
# with 10 coefficients.
#
# Properties:
#
# The family of matrices is nested as a function of N.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 14 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
# N must be at least 10 and a multiple of 2.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from math import sqrt
from i4_wrap import i4_wrap
from sys import exit
if ( n < 10 or ( n % 2 ) != 0 ):
print ''
print 'DAUB10 - Fatal error!'
print ' N must be at least 10 and a multiple of 2.'
exit ( 'DAUB10 - Fatal error!' )
a = np.zeros ( [ n, n ] )
c = np.array ( [ \
0.1601023979741929, \
0.6038292697971895, \
0.7243085284377726, \
0.1384281459013203, \
-0.2422948870663823, \
-0.0322448695846381, \
0.0775714938400459, \
-0.0062414902127983, \
-0.0125807519990820, \
0.0033357252854738 ] )
for i in range ( 0, n - 1, 2 ):
a[i,i] = c[0]
a[i,i+1] = c[1]
a[i,i4_wrap(i+2,0,n-1)] = c[2]
a[i,i4_wrap(i+3,0,n-1)] = c[3]
a[i,i4_wrap(i+4,0,n-1)] = c[4]
a[i,i4_wrap(i+5,0,n-1)] = c[5]
a[i,i4_wrap(i+6,0,n-1)] = c[6]
a[i,i4_wrap(i+7,0,n-1)] = c[7]
a[i,i4_wrap(i+8,0,n-1)] = c[8]
a[i,i4_wrap(i+9,0,n-1)] = c[9]
a[i+1,i] = c[9]
a[i+1,i+1] = - c[8]
a[i+1,i4_wrap(i+2,0,n-1)] = c[7]
a[i+1,i4_wrap(i+3,0,n-1)] = - c[6]
a[i+1,i4_wrap(i+4,0,n-1)] = c[5]
a[i+1,i4_wrap(i+5,0,n-1)] = - c[4]
a[i+1,i4_wrap(i+6,0,n-1)] = c[3]
a[i+1,i4_wrap(i+7,0,n-1)] = - c[2]
a[i+1,i4_wrap(i+8,0,n-1)] = c[1]
a[i+1,i4_wrap(i+9,0,n-1)] = - c[0]
return a
def daub12 ( n ):
#*****************************************************************************80
#
## DAUB12 returns the DAUB12 matrix.
#
# Discussion:
#
# The DAUB12 matrix is the Daubechies wavelet transformation matrix
# with 12 coefficients.
#
# Properties:
#
# The family of matrices is nested as a function of N.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 16 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
# N must be at least 12 and a multiple of 2.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from math import sqrt
from i4_wrap import i4_wrap
from sys import exit
if ( n < 12 or ( n % 2 ) != 0 ):
print ''
print 'DAUB12 - Fatal error!'
print ' N must be at least 12 and a multiple of 2.'
exit ( 'DAUB12 - Fatal error!' )
a = np.zeros ( [ n, n ] )
c = np.array ( [ \
0.1115407433501095, \
0.4946238903984533, \
0.7511339080210959, \
0.3152503517091982, \
-0.2262646939654400, \
-0.1297668675672625, \
0.0975016055873225, \
0.0275228655303053, \
-0.0315820393174862, \
0.0005538422011614, \
0.0047772575109455, \
-0.0010773010853085 ] )
for i in range ( 0, n - 1, 2 ):
a[i,i] = c[0]
a[i,i+1] = c[1]
a[i,i4_wrap(i+2,0,n-1)] = c[2]
a[i,i4_wrap(i+3,0,n-1)] = c[3]
a[i,i4_wrap(i+4,0,n-1)] = c[4]
a[i,i4_wrap(i+5,0,n-1)] = c[5]
a[i,i4_wrap(i+6,0,n-1)] = c[6]
a[i,i4_wrap(i+7,0,n-1)] = c[7]
a[i,i4_wrap(i+8,0,n-1)] = c[8]
a[i,i4_wrap(i+9,0,n-1)] = c[9]
a[i,i4_wrap(i+10,0,n-1)] = c[10]
a[i,i4_wrap(i+11,0,n-1)] = c[11]
a[i+1,i] = c[11]
a[i+1,i+1] = - c[10]
a[i+1,i4_wrap(i+2,0,n-1)] = c[9]
a[i+1,i4_wrap(i+3,0,n-1)] = - c[8]
a[i+1,i4_wrap(i+4,0,n-1)] = c[7]
a[i+1,i4_wrap(i+5,0,n-1)] = - c[6]
a[i+1,i4_wrap(i+6,0,n-1)] = c[5]
a[i+1,i4_wrap(i+7,0,n-1)] = - c[4]
a[i+1,i4_wrap(i+8,0,n-1)] = c[3]
a[i+1,i4_wrap(i+9,0,n-1)] = - c[2]
a[i+1,i4_wrap(i+10,0,n-1)] = c[1]
a[i+1,i4_wrap(i+11,0,n-1)] = - c[0]
return a
def dif1cyclic(n):
#*****************************************************************************80
#
## DIF1CYCLIC returns the cyclic first difference matrix.
#
# Example:
#
# N = 5
#
# 0 +1 . . -1
# -1 0 +1 . .
# . -1 0 +1 .
# . . -1 0 +1
# +1 . . -1 0
#
# Square Properties:
#
# A is integral: int ( A ) = A.
#
# A is Toeplitz: constant along diagonals.
#
# A is antisymmetric: A' = -A.
#
# Because A is antisymmetric, it is normal.
#
# Because A is normal, it is diagonalizable.
#
# A has constant row sum = 0.
#
# A has constant column sum = 0.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 19 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the number of rows and columns of A.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from i4_wrap import i4_wrap
a = np.zeros([n, n])
for i in range(0, n):
im1 = i4_wrap(i - 1, 0, n - 1)
a[i, im1] = -1.0
ip1 = i4_wrap(i + 1, 0, n - 1)
a[i, ip1] = +1.0
return a
def daub8 ( n ):
#*****************************************************************************80
#
## DAUB8 returns the DAUB8 matrix.
#
# Discussion:
#
# The DAUB8 matrix is the Daubechies wavelet transformation matrix
# with 8 coefficients.
#
# Properties:
#
# The family of matrices is nested as a function of N.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 12 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the order of the matrix.
# N must be at least 8 and a multiple of 2.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from math import sqrt
from i4_wrap import i4_wrap
from sys import exit
if ( n < 8 or ( n % 2 ) != 0 ):
print ''
print 'DAUB8 - Fatal error!'
print ' N must be at least 8 and a multiple of 2.'
exit ( 'DAUB8 - Fatal error!' )
a = np.zeros ( [ n, n ] )
c0 = 0.2303778133088964
c1 = 0.7148465705529154
c2 = 0.6308807679298587
c3 = -0.0279837694168599
c4 = -0.1870348117190931
c5 = 0.0308413818355607
c6 = 0.0328830116668852
c7 = -0.0105974017850690
for i in range ( 0, n - 1, 2 ):
a[i,i] = c0
a[i,i+1] = c1
a[i,i4_wrap(i+2,0,n-1)] = c2
a[i,i4_wrap(i+3,0,n-1)] = c3
a[i,i4_wrap(i+4,0,n-1)] = c4
a[i,i4_wrap(i+5,0,n-1)] = c5
a[i,i4_wrap(i+6,0,n-1)] = c6
a[i,i4_wrap(i+7,0,n-1)] = c7
a[i+1,i] = c7
a[i+1,i+1] = - c6
a[i+1,i4_wrap(i+2,0,n-1)] = c5
a[i+1,i4_wrap(i+3,0,n-1)] = - c4
a[i+1,i4_wrap(i+4,0,n-1)] = c3
a[i+1,i4_wrap(i+5,0,n-1)] = - c2
a[i+1,i4_wrap(i+6,0,n-1)] = c1
a[i+1,i4_wrap(i+7,0,n-1)] = - c0
return a
def dif1cyclic ( n ):
#*****************************************************************************80
#
## DIF1CYCLIC returns the cyclic first difference matrix.
#
# Example:
#
# N = 5
#
# 0 +1 . . -1
# -1 0 +1 . .
# . -1 0 +1 .
# . . -1 0 +1
# +1 . . -1 0
#
# Square Properties:
#
# A is integral: int ( A ) = A.
#
# A is Toeplitz: constant along diagonals.
#
# A is antisymmetric: A' = -A.
#
# Because A is antisymmetric, it is normal.
#
# Because A is normal, it is diagonalizable.
#
# A has constant row sum = 0.
#
# A has constant column sum = 0.
#
# Licensing:
#
# This code is distributed under the GNU LGPL license.
#
# Modified:
#
# 19 January 2015
#
# Author:
#
# John Burkardt
#
# Parameters:
#
# Input, integer N, the number of rows and columns of A.
#
# Output, real A(N,N), the matrix.
#
import numpy as np
from i4_wrap import i4_wrap
a = np.zeros ( [ n, n ] )
for i in range ( 0, n ):
im1 = i4_wrap ( i - 1, 0, n - 1 )
a[i,im1] = -1.0
ip1 = i4_wrap ( i + 1, 0, n - 1 )
a[i,ip1] = +1.0
return a
