Python Matrix.transpose方法代码示例

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import transpose [as 别名]
 def _eval_transpose(self):
     # Flip all the individual matrices
     matrices = [Transpose(matrix) for matrix in self.blocks]
     # Make a copy
     M = Matrix(self.blockshape[0], self.blockshape[1], matrices)
     # Transpose the block structure
     M = M.transpose()
     return BlockMatrix(M)

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import transpose [as 别名]
def evalMat( Mlist,  blist  ):
	M = Matrix(Mlist)
	b = Matrix(blist)
	c = M.LUsolve( b )
	print 
	print " ================= Case: ",b.transpose()
	print 
	print c
	return c

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import transpose [as 别名]
def test_1xN_vecs():
    gl = glsl_code
    for i in range(1,10):
        A = Matrix(range(i))
        assert gl(A.transpose()) == gl(A)
        assert gl(A,mat_transpose=True) == gl(A)
        if i > 1:
            if i <= 4:
                assert gl(A) == 'vec%s(%s)' % (i,', '.join(str(s) for s in range(i)))
            else:
                assert gl(A) == 'float[%s](%s)' % (i,', '.join(str(s) for s in range(i)))

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import transpose [as 别名]
def mc_compute_stationary_sympy(P, tol=1e-10):
    """
    Computes the stationary distribution(s) of Markov matrix P.

    Parameters
    ----------
    P : array_like(float, ndim=2)
        A discrete Markov transition matrix

    tol : scalar(float) optional
        Tolerance level
        Find eigenvectors for eigenvalues > 1 - tol

    Returns
    -------
    vals : list of sympy.core.numbers.Float
        A list of eigenvalues of P > 1 - tol

    ndarray_vects : list of numpy.arrays of sympy.core.numbers.Float
        A list of the corresponding eigenvectors of P

    """
    P = Matrix(P)  # type(P): sympy.matrices.dense.MutableDenseMatrix
    outputs = P.transpose().eigenvects()  # TODO: Raise exception when empty

    vals = []
    vecs = []

    # output = (eigenvalue, algebraic multiplicity, [eigenvectors])
    for output in outputs:
        if output[0] > 1 - tol:
            vals.append(output[0])
            vecs.extend(output[2])

    # type(vec): sympy.matrices.dense.MutableDenseMatrix
    vecs_flattened = \
        np.array([flatten(vec) for vec in vecs])

    return vals, [vec/sum(vec) for vec in vecs_flattened]

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import transpose [as 别名]
def test_Matrices_1x7():
    gl = glsl_code
    A = Matrix([1,2,3,4,5,6,7])
    assert gl(A) == 'float[7](1, 2, 3, 4, 5, 6, 7)'
    assert gl(A.transpose()) == 'float[7](1, 2, 3, 4, 5, 6, 7)'

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import transpose [as 别名]
	wg = symbols('wg', real=True, positive=True)
	omegag_2 = g*g*2*2*pi*pi*k[0]*k[0]*s[1]*s[1]+g*g*2*2*pi*pi*k[1]*k[1]*s[0]*s[0]+f*f*s[0]*s[0]*s[1]*s[1]
	eq_subs[omegag_2] = wg*wg
	eq_subs[-omegag_2] = -wg*wg
	inv_eq_subs[wg] = sqrt(omegag_2)


# replace k0*k0 + k1*k1 by 'K2' symbol
K2 = symbols('K2', real=True, positive=True)
asdf = pi*pi*k[0]*k[0]+pi*pi*k[1]*k[1]
eq_subs[asdf] = K2
inv_eq_subs[K2] = asdf


# Solution expressed with variables in spectral space
U_spec = U_spec.transpose()

#
# Solution given in Cartesian space, but expressed in spectral space
# Remove 2*pi for "2pi-sized" domain
#
U_fromSpec = exp(I*2*pi*(k[0]*x[0]/s[0]+k[1]*x[1]/s[1])) * U_spec


print
print
print("L(U):")
M = getL(U_fromSpec)
pprint(M)

# Remove spectral basis components (This should be possible in general)

# 需要导入模块: from sympy.matrices import Matrix [as 别名]
# 或者: from sympy.matrices.Matrix import transpose [as 别名]
"Coupling lattice Boltzmann model for simulation of thermal flows on stanard lattices" \
"by Q. Li, K. H. Luo, Y.L.He., Y.J. Gao, W.Q Tao, 2012"

phi_x = Symbol("phi_x")
phi_y = Symbol("phi_y")
C_D2Q9 = Matrix([-1 / 9 * phi_x,
                 -phi_x / 36 + phi_y / 4,
                 -phi_x / 36 - phi_y / 4,  # suprisingly the article applies asymmetric correction in x and y direction
                 -phi_x / 36 + phi_y / 4,
                 -phi_x / 36 - phi_y / 4,
                 phi_x / 18,
                 phi_x / 18,
                 phi_x / 18,
                 phi_x / 18, ])

print_as_vector(C_D2Q9.transpose() * Mraw_D2Q9, print_symbol="pop_in_str")

#
# print('\n//population_eq -> cm_eq - from continous definition: \n'
#       'k_mn = integrate(fun, (x, -oo, oo), (y, -oo, oo)) \n'
#       'where fun = fM(rho,u,x,y) *(x-ux)^m (y-uy)^n')
# cm_eq = get_mom_vector_from_continuous_def(get_continuous_Maxwellian_DF, continuous_transformation=get_continuous_cm)
# # cm_eq = get_mom_vector_from_continuous_def(get_continuous_hydro_DF, continuous_transformation=get_continuous_cm)
# print_as_vector(cm_eq, 'cm_eq')


# import re
# re.findall(r'\d+', 'hello 42 I\'m a 32 string 30')
# ['42', '32', '30']
# # This would also match 42 from bla42bla. If you only want numbers delimited by word boundaries (space, period, comma), you can use \b :
#