def test_diagonal_symmetrical():
m = PropertiesOnlyMatrix(2, 2, [0, 1, 1, 0])
assert not m.is_diagonal()
assert m.is_symmetric()
assert m.is_symmetric(simplify=False)
m = PropertiesOnlyMatrix(2, 2, [1, 0, 0, 1])
assert m.is_diagonal()
m = PropertiesOnlyMatrix(3, 3, diag(1, 2, 3))
assert m.is_diagonal()
assert m.is_symmetric()
m = PropertiesOnlyMatrix(3, 3, [1, 0, 0, 0, 2, 0, 0, 0, 3])
assert m == diag(1, 2, 3)
m = PropertiesOnlyMatrix(2, 3, zeros(2, 3))
assert not m.is_symmetric()
assert m.is_diagonal()
m = PropertiesOnlyMatrix(((5, 0), (0, 6), (0, 0)))
assert m.is_diagonal()
m = PropertiesOnlyMatrix(((5, 0, 0), (0, 6, 0)))
assert m.is_diagonal()
m = Matrix(3, 3, [1, x**2 + 2*x + 1, y, (x + 1)**2, 2, 0, y, 0, 3])
assert m.is_symmetric()
assert not m.is_symmetric(simplify=False)
assert m.expand().is_symmetric(simplify=False)
def test_diag_make():
diag = SpecialOnlyMatrix.diag
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
assert diag(a, b, b) == Matrix([
[1, 2, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0],
[0, 0, y, 3, 0, 0],
[0, 0, 0, 0, 3, x],
[0, 0, 0, 0, y, 3],
])
assert diag(a, b, c) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0, 0],
[0, 0, y, 3, 0, 0, 0],
[0, 0, 0, 0, 3, x, 3],
[0, 0, 0, 0, y, 3, z],
[0, 0, 0, 0, x, y, z],
])
assert diag(a, c, b) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 3, 0, 0],
[0, 0, y, 3, z, 0, 0],
[0, 0, x, y, z, 0, 0],
[0, 0, 0, 0, 0, 3, x],
[0, 0, 0, 0, 0, y, 3],
])
a = Matrix([x, y, z])
b = Matrix([[1, 2], [3, 4]])
c = Matrix([[5, 6]])
assert diag(a, 7, b, c) == Matrix([
[x, 0, 0, 0, 0, 0],
[y, 0, 0, 0, 0, 0],
[z, 0, 0, 0, 0, 0],
[0, 7, 0, 0, 0, 0],
[0, 0, 1, 2, 0, 0],
[0, 0, 3, 4, 0, 0],
[0, 0, 0, 0, 5, 6],
])
assert SpecialOnlyMatrix.diag([2, 3]) == Matrix([
[2, 0],
[0, 3]])
assert SpecialOnlyMatrix.diag(Matrix([2, 3])) == Matrix([
[2],
[3]])
assert SpecialOnlyMatrix.diag(1, rows=3, cols=2) == Matrix([
[1, 0],
[0, 0],
[0, 0]])
assert type(SpecialOnlyMatrix.diag(1)) == SpecialOnlyMatrix
assert type(SpecialOnlyMatrix.diag(1, cls=Matrix)) == Matrix
def test_get_diag_blocks2():
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
A, B, C, D = diag(a, b, b), diag(a, b, c), diag(a, c, b), diag(c, c, b)
A = ShapingOnlyMatrix(A.rows, A.cols, A)
B = ShapingOnlyMatrix(B.rows, B.cols, B)
C = ShapingOnlyMatrix(C.rows, C.cols, C)
D = ShapingOnlyMatrix(D.rows, D.cols, D)
assert A.get_diag_blocks() == [a, b, b]
assert B.get_diag_blocks() == [a, b, c]
assert C.get_diag_blocks() == [a, c, b]
assert D.get_diag_blocks() == [c, c, b]
def smith_normal_form(m, domain = None):
'''
Return the Smith Normal Form of a matrix `m` over the ring `domain`.
This will only work if the ring is a principal ideal domain.
Examples
========
>>> from sympy.polys.solvers import RawMatrix as Matrix
>>> from sympy.polys.domains import ZZ
>>> from sympy.matrices.normalforms import smith_normal_form
>>> m = Matrix([[12, 6, 4], [3, 9, 6], [2, 16, 14]])
>>> setattr(m, "ring", ZZ)
>>> print(smith_normal_form(m))
Matrix([[1, 0, 0], [0, 10, 0], [0, 0, -30]])
'''
invs = invariant_factors(m, domain=domain)
smf = diag(*invs)
n = len(invs)
if m.rows > n:
smf = smf.row_insert(m.rows, zeros(m.rows-n, m.cols))
elif m.cols > n:
smf = smf.col_insert(m.cols, zeros(m.rows, m.cols-n))
return smf
def _eval_inverse(self, **kwargs):
"""Return the matrix inverse using the method indicated (default
is Gauss elimination).
kwargs
======
method : ('GE', 'LU', or 'ADJ')
iszerofunc
try_block_diag
Notes
=====
According to the ``method`` keyword, it calls the appropriate method:
GE .... inverse_GE(); default
LU .... inverse_LU()
ADJ ... inverse_ADJ()
According to the ``try_block_diag`` keyword, it will try to form block
diagonal matrices using the method get_diag_blocks(), invert these
individually, and then reconstruct the full inverse matrix.
Note, the GE and LU methods may require the matrix to be simplified
before it is inverted in order to properly detect zeros during
pivoting. In difficult cases a custom zero detection function can
be provided by setting the ``iszerosfunc`` argument to a function that
should return True if its argument is zero. The ADJ routine computes
the determinant and uses that to detect singular matrices in addition
to testing for zeros on the diagonal.
See Also
========
inverse_LU
inverse_GE
inverse_ADJ
"""
from sympy.matrices import diag
method = kwargs.get('method', 'GE')
iszerofunc = kwargs.get('iszerofunc', _iszero)
if kwargs.get('try_block_diag', False):
blocks = self.get_diag_blocks()
r = []
for block in blocks:
r.append(block.inv(method=method, iszerofunc=iszerofunc))
return diag(*r)
if method == "GE":
return self.inverse_GE(iszerofunc=iszerofunc)
elif method == "LU":
return self.inverse_LU(iszerofunc=iszerofunc)
elif method == "ADJ":
return self.inverse_ADJ(iszerofunc=iszerofunc)
else:
# make sure to add an invertibility check (as in inverse_LU)
# if a new method is added.
raise ValueError("Inversion method unrecognized")
def scale_matrix(all_elements, mat, inds):
"""
Scales the k or l matrix.
The procedure is the same for each matrix:
(D^x)^(-1) * y * D^(x_i)
Inverse diagonal The matrix to be The diagonal of
of the x where scaled. i.e. the the independent x
x is either the k or l matrix where x is the
species or the species or the
fluxes fluxes
"""
d_all_inv = diag(*all_elements).inv()
d_inds = diag(*inds)
scaled_matrix = d_all_inv * mat * d_inds
return scaled_matrix
def rmat_k2d(d):
d = normalize(d)
if d == k:
rot = eye(3)
elif d == -k:
rot = diag(1,-1,-1)
else:
rot = rquat2rmat(rquat(acos(dot(k, d)), cross(k, d)))
return rot
def zfac2zmat(zfac):
"""
>>> from sympy import *
>>> from symplus.strplus import init_mprinting
>>> init_mprinting()
>>> zfac2zmat([5,2,sqrt(3)])
<BLANKLINE>
[5 0 0]
[0 2 0]
[0 0 sqrt(3)]
"""
return Mat(diag(*zfac))
def eye(n, cls=None):
"""Create square identity matrix n x n
See Also
========
diag
zeros
ones
"""
n = as_int(n)
return diag(*[S.One]*n, **dict(cls=cls))
def ALGO4(As,Bs,Cs,Ds,do_test):
#-------------------STEP 1------------------------------
if not is_row_proper(As):
Us,Ast=row_proper(As)
else:
Us,Ast=eye(As.rows),As
Bst=Us*Bs
Bst=expand(Bst)
r=Ast.cols
#-------------------STEP 2------------------------------
K=simplify(Ast.inv()*Bst) #very important
Ys=zeros(K.shape)
for i,j in product(range(K.rows),range(K.cols)):
Ys[i,j],q=div(numer(K[i,j]),denom(K[i,j]))
B_hat=Bst-Ast*Ys
#-------------------END STEP 2------------------------------
#-------------------STEP 3------------------------------
Psi=diag(*[[s**( mc.row_degrees(Ast,s)[j] -i -1) for i in range( mc.row_degrees(Ast,s)[j])] for j in range(r)]).T
S=diag(*[s**(rho) for rho in mc.row_degrees(Ast,s)])
Ahr=mc.highest_row_degree_matrix(Ast,s)
Help=Ast-S*Ahr
SOL={}
numvar=Psi.rows*Psi.cols
alr=symbols('a0:%d'%numvar)
Alr=Matrix(Psi.cols,Psi.rows,alr)
RHS=Psi*Alr
for i,j in product(range(Help.rows),range(Help.cols)): #diagonal explain later
SOL.update(solve_undetermined_coeffs(Eq(Help[i,j],RHS[i,j]),alr,s))
Alr=Alr.subs(SOL) #substitute(SOL)
Aoc=Matrix(BlockDiagMatrix(*[Matrix(rho, rho, lambda i,j: KroneckerDelta(i+1,j))for rho in mc.row_degrees(Ast,s)]))
Boc=eye(sum(mc.row_degrees(Ast,s)))
Coc=Matrix(BlockDiagMatrix(*[SparseMatrix(1,rho,{(x,0):1 if x==0 else 0 for x in range(rho)}) for rho in mc.row_degrees(Ast,s)]))
A0=Aoc-Alr*Ahr.inv()*Matrix(Coc)
C0=Ahr.inv()*Coc
SOL={}
numvar=Psi.cols*Bst.cols
b0=symbols('b0:%d'%numvar)
B0=Matrix(Psi.cols,Bst.cols,b0)
RHS=Psi*B0
for i,j in product(range(B_hat.rows),range(B_hat.cols)): #diagonal explain later
SOL.update(solve_undetermined_coeffs(Eq(B_hat[i,j],RHS[i,j]),b0,s))
B0=B0.subs(SOL) #substitute(SOL)
LHS_matrix=simplify(Cs*C0) #left hand side of the equation (1)
sI_A=s*eye(A0.cols)- A0
max_degree=mc.find_degree(LHS_matrix,s) #get the degree of the matrix at the LHS
#which is also the maximum degree for the coefficients of Λ(s)
#---------------------------Creating Matrices Λ(s) and C -------------------------------------
Lamda=[]
numvar=((max_degree))*A0.cols
a=symbols('a0:%d'%numvar)
for i in range(A0.cols): # paratirisi den douleuei to prin giat;i otra oxi diagonios
p=sum(a[n +i*(max_degree)]*s**n for n in range(max_degree)) # we want variables one degree lower because we are multiplying by first order monomials
Lamda.append(p)
Lamda=Matrix(Cs.rows,A0.cols,Lamda) #convert the list to Matrix
c=symbols('c0:%d'%(Lamda.rows*Lamda.cols))
C=Matrix(Lamda.rows,Lamda.cols,c)
#-----------------------------------------
RHS_matrix=Lamda*sI_A +C #right hand side of the equation (1)
'''
-----------Converting equation (1) to a system of linear -----------
-----------equations, comparing the coefficients of the -----------
-----------polynomials in both sides of the equation (1) -----------
'''
EQ=[Eq(LHS_matrix[i,j],expand(RHS_matrix[i,j])) for i,j in product(range(LHS_matrix.rows),range(LHS_matrix.cols)) ]
from sympy import Symbol
from sympy.matrices import Matrix, eye, zeros, ones, diag
from sympy import pretty_print
from SymbolicCollisions.core.DiscreteCMTransforms import get_DF
from SymbolicCollisions.core.cm_symbols import Shift_ortho_Straka_d2q5
"""
See
'New Cascaded Thermal Lattice Boltzmann Method for simulations for advection-diffusion and convective heat transfer'
by K.V. Sharma, R. Straka, F.W. Tavares, 2017
"""
# Smat = get_shift_matrix(K_ortho_Straka_d2q5, ex_Straka_d2_q5, ey_Straka_d2_q5)
# pretty_print(Smat)
Smat = Shift_ortho_Straka_d2q5
k = get_DF(q=4, print_symbol='k')
Relax = diag(Symbol('w2'), Symbol('w3'), Symbol('w4'), Symbol('w5')) #
cm_neq = get_DF(q=4, print_symbol='cm_neq')
k = Smat.inv()*Relax*cm_neq
pretty_print(k)
def _eval_inverse(self, **kwargs):
"""Return the matrix inverse using the method indicated (default
is Gauss elimination).
kwargs
======
method : ('GE', 'LU', or 'ADJ')
iszerofunc
try_block_diag
Notes
=====
According to the ``method`` keyword, it calls the appropriate method:
GE .... inverse_GE(); default
LU .... inverse_LU()
ADJ ... inverse_ADJ()
According to the ``try_block_diag`` keyword, it will try to form block
diagonal matrices using the method get_diag_blocks(), invert these
individually, and then reconstruct the full inverse matrix.
Note, the GE and LU methods may require the matrix to be simplified
before it is inverted in order to properly detect zeros during
pivoting. In difficult cases a custom zero detection function can
be provided by setting the ``iszerosfunc`` argument to a function that
should return True if its argument is zero. The ADJ routine computes
the determinant and uses that to detect singular matrices in addition
to testing for zeros on the diagonal.
See Also
========
inverse_LU
inverse_GE
inverse_ADJ
"""
from sympy.matrices import diag
method = kwargs.get('method', 'GE')
iszerofunc = kwargs.get('iszerofunc', _iszero)
if kwargs.get('try_block_diag', False):
blocks = self.get_diag_blocks()
r = []
for block in blocks:
r.append(block.inv(method=method, iszerofunc=iszerofunc))
return diag(*r)
M = self.as_mutable()
if method == "GE":
rv = M.inverse_GE(iszerofunc=iszerofunc)
elif method == "LU":
rv = M.inverse_LU(iszerofunc=iszerofunc)
elif method == "ADJ":
rv = M.inverse_ADJ(iszerofunc=iszerofunc)
else:
# make sure to add an invertibility check (as in inverse_LU)
# if a new method is added.
raise ValueError("Inversion method unrecognized")
return self._new(rv)
def test_diag_make():
diag = SpecialOnlyMatrix.diag
a = Matrix([[1, 2], [2, 3]])
b = Matrix([[3, x], [y, 3]])
c = Matrix([[3, x, 3], [y, 3, z], [x, y, z]])
assert diag(a, b, b) == Matrix([
[1, 2, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0],
[0, 0, y, 3, 0, 0],
[0, 0, 0, 0, 3, x],
[0, 0, 0, 0, y, 3],
])
assert diag(a, b, c) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 0, 0, 0],
[0, 0, y, 3, 0, 0, 0],
[0, 0, 0, 0, 3, x, 3],
[0, 0, 0, 0, y, 3, z],
[0, 0, 0, 0, x, y, z],
])
assert diag(a, c, b) == Matrix([
[1, 2, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[0, 0, 3, x, 3, 0, 0],
[0, 0, y, 3, z, 0, 0],
[0, 0, x, y, z, 0, 0],
[0, 0, 0, 0, 0, 3, x],
[0, 0, 0, 0, 0, y, 3],
])
a = Matrix([x, y, z])
b = Matrix([[1, 2], [3, 4]])
c = Matrix([[5, 6]])
# this "wandering diagonal" is what makes this
# a block diagonal where each block is independent
# of the others
assert diag(a, 7, b, c) == Matrix([[x, 0, 0, 0, 0, 0], [y, 0, 0, 0, 0, 0],
[z, 0, 0, 0, 0, 0], [0, 7, 0, 0, 0, 0],
[0, 0, 1, 2, 0, 0], [0, 0, 3, 4, 0, 0],
[0, 0, 0, 0, 5, 6]])
raises(ValueError, lambda: diag(a, 7, b, c, rows=5))
assert diag(1) == Matrix([[1]])
assert diag(1, rows=2) == Matrix([[1, 0], [0, 0]])
assert diag(1, cols=2) == Matrix([[1, 0], [0, 0]])
assert diag(1, rows=3, cols=2) == Matrix([[1, 0], [0, 0], [0, 0]])
assert diag(*[2, 3]) == Matrix([[2, 0], [0, 3]])
assert diag(Matrix([2, 3])) == Matrix([[2], [3]])
assert diag([1, [2, 3], 4], unpack=False) == \
diag([[1], [2, 3], [4]], unpack=False) == Matrix([
[1, 0],
[2, 3],
[4, 0]])
assert type(diag(1)) == SpecialOnlyMatrix
assert type(diag(1, cls=Matrix)) == Matrix
assert Matrix.diag([1, 2, 3]) == Matrix.diag(1, 2, 3)
assert Matrix.diag([1, 2, 3], unpack=False).shape == (3, 1)
assert Matrix.diag([[1, 2, 3]]).shape == (3, 1)
assert Matrix.diag([[1, 2, 3]], unpack=False).shape == (1, 3)
assert Matrix.diag([[[1, 2, 3]]]).shape == (1, 3)
# kerning can be used to move the starting point
assert Matrix.diag(ones(0, 2), 1, 2) == Matrix([[0, 0, 1, 0], [0, 0, 0,
2]])
assert Matrix.diag(ones(2, 0), 1, 2) == Matrix([[0, 0], [0, 0], [1, 0],
[0, 2]])
ex_D2Q9 = Matrix([0, 1, 0, -1, 0, 1, -1, -1, 1]) ey_D2Q9 = Matrix([0, 0, 1, 0, -1, 1, 1, -1, -1]) e_D2Q9 = ex_D2Q9.col_insert(1, ey_D2Q9) # D3Q7 notation from TCLB ex_D3Q7 = Matrix([0, 1, -1, 0, 0, 0, 0]) ey_D3Q7 = Matrix([0, 0, 0, 1, -1, 0, 0]) ez_D3Q7 = Matrix([0, 0, 0, 0, 0, 1, -1]) # D3Q15 - notation from 'LBM Principles and Practise' Book p. 89 ex_D3Q15 = Matrix([0, 1, -1, 0, 0, 0, 0, 1, -1, 1, -1, 1, -1, -1, 1]) ey_D3Q15 = Matrix([0, 0, 0, 1, -1, 0, 0, 1, -1, 1, -1, -1, 1, 1, -1]) ez_D3Q15 = Matrix([0, 0, 0, 0, 0, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1]) S_relax_ADE_D3Q15 = diag(1, omega_ade, omega_ade, omega_ade, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) # D3Q19 - # as in TCLB or '3D CLBM: Improved implementation and consistent forcing scheme' by L. Fei and Q. li 2018 # (differs from 'LBM Principles and Practise' Book) ex_D3Q19 = Matrix([0, 1, -1, 0, 0, 0, 0, 1, -1, 1, -1, 1, -1, 1, -1, 0, 0, 0, 0]) ey_D3Q19 = Matrix([0, 0, 0, 1, -1, 0, 0, 1, 1, -1, -1, 0, 0, 0, 0, 1, -1, 1, -1]) ez_D3Q19 = Matrix([0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 1, 1, -1, -1, 1, 1, -1, -1]) S_relax_ADE_D3Q19 = diag(1, omega_ade, omega_ade, omega_ade, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) # FROM TCLB # d3q19 = matrix(c( # 0, 1, -1, 0, 0, 0, 0, 1, -1, 1, -1, 1, -1, 1, -1, 0, 0, 0, 0, # 0, 0, 0, 1, -1, 0, 0, 1, 1, -1, -1, 0, 0, 0, 0, 1, -1, 1, -1,
def _inv(M, method=None, iszerofunc=_iszero, try_block_diag=False):
"""
Return the inverse of a matrix using the method indicated. Default for
dense matrices is is Gauss elimination, default for sparse matrices is LDL.
Parameters
==========
method : ('GE', 'LU', 'ADJ', 'CH', 'LDL')
iszerofunc : function, optional
Zero-testing function to use.
try_block_diag : bool, optional
If True then will try to form block diagonal matrices using the
method get_diag_blocks(), invert these individually, and then
reconstruct the full inverse matrix.
Examples
========
>>> from sympy import SparseMatrix, Matrix
>>> A = SparseMatrix([
... [ 2, -1, 0],
... [-1, 2, -1],
... [ 0, 0, 2]])
>>> A.inv('CH')
Matrix([
[2/3, 1/3, 1/6],
[1/3, 2/3, 1/3],
[ 0, 0, 1/2]])
>>> A.inv(method='LDL') # use of 'method=' is optional
Matrix([
[2/3, 1/3, 1/6],
[1/3, 2/3, 1/3],
[ 0, 0, 1/2]])
>>> A * _
Matrix([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
>>> A = Matrix(A)
>>> A.inv('CH')
Matrix([
[2/3, 1/3, 1/6],
[1/3, 2/3, 1/3],
[ 0, 0, 1/2]])
>>> A.inv('ADJ') == A.inv('GE') == A.inv('LU') == A.inv('CH') == A.inv('LDL') == A.inv('QR')
True
Notes
=====
According to the ``method`` keyword, it calls the appropriate method:
GE .... inverse_GE(); default for dense matrices
LU .... inverse_LU()
ADJ ... inverse_ADJ()
CH ... inverse_CH()
LDL ... inverse_LDL(); default for sparse matrices
QR ... inverse_QR()
Note, the GE and LU methods may require the matrix to be simplified
before it is inverted in order to properly detect zeros during
pivoting. In difficult cases a custom zero detection function can
be provided by setting the ``iszerofunc`` argument to a function that
should return True if its argument is zero. The ADJ routine computes
the determinant and uses that to detect singular matrices in addition
to testing for zeros on the diagonal.
See Also
========
inverse_ADJ
inverse_GE
inverse_LU
inverse_CH
inverse_LDL
Raises
======
ValueError
If the determinant of the matrix is zero.
"""
from sympy.matrices import diag, SparseMatrix
if method is None:
method = 'LDL' if isinstance(M, SparseMatrix) else 'GE'
if try_block_diag:
blocks = M.get_diag_blocks()
r = []
for block in blocks:
r.append(block.inv(method=method, iszerofunc=iszerofunc))
return diag(*r)
if method == "GE":
rv = M.inverse_GE(iszerofunc=iszerofunc)
elif method == "LU":
rv = M.inverse_LU(iszerofunc=iszerofunc)
elif method == "ADJ":
rv = M.inverse_ADJ(iszerofunc=iszerofunc)
elif method == "CH":
rv = M.inverse_CH(iszerofunc=iszerofunc)
elif method == "LDL":
rv = M.inverse_LDL(iszerofunc=iszerofunc)
elif method == "QR":
rv = M.inverse_QR(iszerofunc=iszerofunc)
else:
raise ValueError("Inversion method unrecognized")
return M._new(rv)
