def test_lobatto():
x, w = gauss_lobatto(2, 17)
assert [str(r) for r in x] == ['-1', '1']
assert [str(r) for r in w] == ['1.0000000000000000', '1.0000000000000000']
x, w = gauss_lobatto(3, 17)
assert [str(r) for r in x] == ['-1', '0', '1']
assert [str(r) for r in w] == [
'0.33333333333333333', '1.3333333333333333', '0.33333333333333333'
]
x, w = gauss_lobatto(4, 17)
assert [str(r) for r in x
] == ['-1', '-0.44721359549995794', '0.44721359549995794', '1']
assert [str(r) for r in w] == [
'0.16666666666666667', '0.83333333333333333', '0.83333333333333333',
'0.16666666666666667'
]
x, w = gauss_lobatto(5, 17)
assert [str(r) for r in x] == [
'-1', '-0.65465367070797714', '0', '0.65465367070797714', '1'
]
assert [str(r) for r in w] == [
'0.10000000000000000', '0.54444444444444444', '0.71111111111111111',
'0.54444444444444444', '0.10000000000000000'
]
def test_lobatto_precise():
x, w = gauss_lobatto(3, 40)
assert [str(r) for r in x] == ['-1', '0', '1']
assert [str(r) for r in w] == [
'0.3333333333333333333333333333333333333333',
'1.333333333333333333333333333333333333333',
'0.3333333333333333333333333333333333333333'
]
def create_c_vector(self, s):
c, w = gauss_lobatto(s, s)
c = np.array(c)
c = c + 1
c = c * 0.5
# print("C is")
# print(c)
return c
def test_lobatto_precise():
x, w = gauss_lobatto(3, 40)
assert [str(r) for r in x] == ["-1", "0", "1"]
assert [str(r) for r in w] == [
"0.3333333333333333333333333333333333333333",
"1.333333333333333333333333333333333333333",
"0.3333333333333333333333333333333333333333",
]
def test_lobatto():
x, w = gauss_lobatto(2, 17)
assert [str(r) for r in x] == [
'-1',
'1']
assert [str(r) for r in w] == [
'1.0000000000000000',
'1.0000000000000000']
x, w = gauss_lobatto(3, 17)
assert [str(r) for r in x] == [
'-1',
'0',
'1']
assert [str(r) for r in w] == [
'0.33333333333333333',
'1.3333333333333333',
'0.33333333333333333']
x, w = gauss_lobatto(4, 17)
assert [str(r) for r in x] == [
'-1',
'-0.44721359549995794',
'0.44721359549995794',
'1']
assert [str(r) for r in w] == [
'0.16666666666666667',
'0.83333333333333333',
'0.83333333333333333',
'0.16666666666666667']
x, w = gauss_lobatto(5, 17)
assert [str(r) for r in x] == [
'-1',
'-0.65465367070797714',
'0',
'0.65465367070797714',
'1']
assert [str(r) for r in w] == [
'0.10000000000000000',
'0.54444444444444444',
'0.71111111111111111',
'0.54444444444444444',
'0.10000000000000000']
def test_lobatto_precise():
x, w = gauss_lobatto(3, 40)
assert [str(r) for r in x] == [
'-1',
'0',
'1']
assert [str(r) for r in w] == [
'0.3333333333333333333333333333333333333333',
'1.333333333333333333333333333333333333333',
'0.3333333333333333333333333333333333333333']
def test_lobatto():
x, w = gauss_lobatto(2, 17)
assert [str(r) for r in x] == ["-1", "1"]
assert [str(r) for r in w] == ["1.0000000000000000", "1.0000000000000000"]
x, w = gauss_lobatto(3, 17)
assert [str(r) for r in x] == ["-1", "0", "1"]
assert [str(r) for r in w] == [
"0.33333333333333333",
"1.3333333333333333",
"0.33333333333333333",
]
x, w = gauss_lobatto(4, 17)
assert [str(r) for r in x] == [
"-1",
"-0.44721359549995794",
"0.44721359549995794",
"1",
]
assert [str(r) for r in w] == [
"0.16666666666666667",
"0.83333333333333333",
"0.83333333333333333",
"0.16666666666666667",
]
x, w = gauss_lobatto(5, 17)
assert [str(r) for r in x] == [
"-1",
"-0.65465367070797714",
"0",
"0.65465367070797714",
"1",
]
assert [str(r) for r in w] == [
"0.10000000000000000",
"0.54444444444444444",
"0.71111111111111111",
"0.54444444444444444",
"0.10000000000000000",
]
def _quad_coeffs_hq(M, collocation_type, digits=20):
if M == 1:
x = np.array([0.0])
w = np.array([2.0])
elif collocation_type == "gauss_legendre":
from sympy.integrals.quadrature import gauss_legendre
x, w = gauss_legendre(M, digits)
elif collocation_type == "gauss_lobatto":
from sympy.integrals.quadrature import gauss_lobatto
x, w = gauss_lobatto(M, digits)
elif collocation_type == "gauss_hermite":
from sympy.integrals.quadrature import gauss_hermite
x, w = gauss_hermite(M, 30)
x = np.array(x, dtype=float)
w = np.array(w, dtype=float)
elif collocation_type == "gauss_jacobi":
from sympy.integrals.quadrature import gauss_jacobi
x, w = gauss_jacobi(M, 0, 0, 30)
x = np.array(x, dtype=float)
w = np.array(w, dtype=float)
elif collocation_type == "gauss_chebyshev_u":
from sympy.integrals.quadrature import gauss_chebyshev_u
x, w = gauss_chebyshev_u(M, 30)
x = np.array(x, dtype=float)
w = np.array(w, dtype=float)
elif collocation_type == "gauss_chebyshev_t":
from sympy.integrals.quadrature import gauss_chebyshev_t
x, w = gauss_chebyshev_t(M, 30)
x = np.array(x, dtype=float)
w = np.array(w, dtype=float)
else:
raise Exception("Unknown collocation method '" +
str(collocation_type) + "'")
assert len(x) == M
return x, w
tx = np.zeros(nnp,np.float64)
ty = np.zeros(nnp,np.float64)
# M_prime_el=(hx/2)*np.array([\
# [16/105,957/2240,-3/70,19/840],\
# [957/2240,2619/896,-1161/2240,-327/2240],\
# [-3/70,-1161/2240,27/35,33/280],\
# [19/840,-327/2240,33/280,16/105]])
M_prime_el=np.zeros((4,4),dtype=np.float64)
use_mass_lumping=False
if use_mass_lumping:
degree=4
gleg_points,gleg_weights=gauss_lobatto(degree,20)
else:
degree=14
gleg_points,gleg_weights=np.polynomial.legendre.leggauss(degree)
for iq in range(degree):
jq=0
rq=gleg_points[iq]
sq=-1
weightq=gleg_weights[iq]
NV[0:mV]=NNV(rq,sq)
M_prime_el[0,0] += NV[0]*NV[0]*weightq
M_prime_el[0,1] += NV[0]*NV[1]*weightq
M_prime_el[0,2] += NV[0]*NV[2]*weightq
M_prime_el[0,3] += NV[0]*NV[3]*weightq
def __init__(self,
n_order,
FEM_boundaries,
Mass=1,
Complex_scale=1,
R0_scale=0.0):
"""Constructor method
Builds 1D FEM DVR grid and kinetic energy matrix representation in
normalized FEM DVR basis of shape functions and bridging functions
Finite elements of any sizes determined by FEM_boundaries array, but
all with same order DVR
Args:
n_order (int): The DVR order. More dense takes longer but improves
acuracy
FEM_boundaries (ndarray): An array of 'double' or 'complex' boundaries for the
Finite-Elements.
Complex_scale (int,optional): Flag that starts complex scaling at grid
boundary closest to R0 if .ne. 1. Defaults
to 1
R0_scale (complex,optional): Grid point where the complex tail starts.
Defaults to 0.0
Attributes:
n_order (int): Can access the DVR order later
FEM_boundaries (ndarray): Can access the 'double' or 'complex'
Finite-Element boundaries later
nbas (int): Total number of basis functions in the FEM-DVR grid
x_pts (ndarray): Array of 'double' or 'complex' DVR points
w_pts (ndarray): Array of 'double' or 'complex' DVR weight
KE_mat (ndarray): Kintetic Energy matrix, dimension is
nbas x nbas of 'double' or 'complex' types
i_elem_scale (int): index of last real DVR point
"""
self.n_order = n_order
self.FEM_boundaries = FEM_boundaries
N_elements = len(FEM_boundaries) - 1
print(
"\nFrom FEM_DVR_build: building grid and KE with Gauss Lobatto quadrature of order ",
n_order,
)
#
# Complex the FEM_boundaries if Complex_scale .ne. 1
#
i_elem_scale = 0.0
if Complex_scale != 1:
for i_elem in range(0, N_elements):
if FEM_boundaries[i_elem] >= R0_scale:
R0 = FEM_boundaries[i_elem]
i_elem_scale = i_elem
break
for i_elem in range(i_elem_scale, N_elements + 1):
FEM_boundaries[i_elem] = R0 + Complex_scale * (
FEM_boundaries[i_elem] - R0)
#
for i_elem in range(0, N_elements):
print(
"element ",
i_elem + 1,
" xmin = ",
FEM_boundaries[i_elem],
" xmax = ",
FEM_boundaries[i_elem + 1],
)
#
# sympy gauss_lobatto(n,p) does extended precision, apparently symbolically, to
# produce points and weights on (-1,+1). It is slow for higher orders
n_precision = 18
xlob, wlob = gauss_lobatto(n_order, n_precision)
#
w_lobatto = []
x_lobatto = []
# make the elements of the extended precision points and weights from sympy
# into ordinary floating point numbers for subsequent use
for i in range(0, n_order):
w_lobatto.append(float(wlob[i]))
x_lobatto.append(float(xlob[i]))
# create temporary array to hold kinetic energy before first and last points of grid are removed
# to enforce boundary condition psi = 0 at ends of grid
# all initializations with np.zeros are complex for ECS
full_grid_size = n_order * N_elements - (N_elements - 1)
KE_temp = np.zeros((full_grid_size, full_grid_size), dtype=np.complex)
# Build FEM_DVR and Kinetic Energy matrix in same for loop:
x_pts = np.zeros(full_grid_size, dtype=np.complex)
w_pts = np.zeros(full_grid_size, dtype=np.complex)
for i_elem in range(0, N_elements):
xmin = FEM_boundaries[i_elem]
xmax = FEM_boundaries[i_elem + 1]
x_elem = []
w_elem = []
for i in range(0, n_order):
x_elem.append(
((xmax - xmin) * x_lobatto[i] + (xmax + xmin)) / 2.0)
w_elem.append((xmax - xmin) * w_lobatto[i] / 2.0)
for i in range(0, n_order):
shift = i_elem * n_order
if i_elem > 0:
shift = i_elem * n_order - i_elem
x_pts[i + shift] = x_elem[i]
w_pts[i + shift] = w_pts[i + shift] + w_elem[i]
# DVR().Kinetic_energy_FEM_block gives -1/2 d^x/dx^2 in unnormalized DVR basis
KE_elem = self.Kinetic_Energy_FEM_block(n_order, x_elem, w_elem)
for i in range(0, n_order):
for j in range(0, n_order):
KE_temp[i + (i_elem * (n_order - 1)),
j + (i_elem * (n_order - 1)), ] = (
KE_temp[i + (i_elem * (n_order - 1)), j +
(i_elem *
(n_order - 1)), ] + KE_elem[i, j])
nbas = full_grid_size - 2
KE_mat = np.zeros((nbas, nbas), dtype=np.complex)
# apply normalizations of DVR basis to KE matrix, bridging fcns have w_left + w_right
for i in range(0, nbas):
for j in range(0, nbas):
KE_mat[i, j] = KE_temp[i + 1, j + 1] / np.sqrt(
w_pts[i + 1] * w_pts[j + 1])
# KE matrix complete, return full grid with endpoints and KE_mat which is
# nbas x nbas
self.nbas = nbas
self.x_pts = x_pts
self.w_pts = w_pts
self.KE_mat = KE_mat
self.rescale_kinetic(Mass)
self.i_elem_scale = i_elem_scale
import sys
from math import *
from sympy.integrals.quadrature import gauss_lobatto
precisao = 9
farg = sys.argv[1].replace("^", "**")
variavel = sys.argv[3] if len(sys.argv) >= 4 else 'x'
f = eval("lambda " + variavel + ": " + farg)
n = int(sys.argv[2])
x, w = gauss_lobatto(n, precisao)
xw = zip(x, w)
fxi = [f(xi)*wi for xi, wi in xw]
print(sum(fxi))
