#!/usr/bin/env python3
# Author: Octavio Castillo Reyes
# Contact: octavio.castillo@bsc.es
"""Define functions for high-order vector finite element method."""
# ---------------------------------------------------------------
# Load python modules
# ---------------------------------------------------------------
import numpy as np
from .common import Print
# ###############################################################
# ################ FUNCTIONS DEFINITION #################
# ###############################################################
[docs]def computeConnectivityDOFS(elemsE, elemsF, Nord):
r"""Compute the degrees of freedom connectivity for a given list of edges, faces and elements.
:param ndarray elemsE: elements-edge connectivity with dimensions = (number_elements, 6)
:param ndarray elemsF: element/faces connectivity with dimensions = (number_elements, 4)
:param int Nord: polynomial order of nedelec basis functions
:return: local/global dofs list for elements, dofs index on edges, dofs index on faces, dofs index on volumes, total number of dofs
:rtype: ndarray and int
.. note:: References:\n
Amor-Martin, A., Garcia-Castillo, L. E., & Garcia-Doñoro, D. D.
(2016). Second-order Nédélec curl-conforming prismatic element
for computational electromagnetics. IEEE Transactions on
Antennas and Propagation, 64(10), 4384-4395.
"""
# Get number of elements
nElems = elemsE.shape[0]
# Get number of edges
nEdges = np.max(elemsE) + 1
# Get number of faces
nFaces = np.max(elemsF) + 1
# Define number of edges and faces per element
num_edges_in_element = 6
num_faces_in_element = 4
# Define number of dofs per entity
num_dof_in_edge = Nord
num_dof_in_face = Nord*(Nord-1)
num_dof_in_volume = np.int(Nord*(Nord-1)*(Nord-2)/2)
# Compute number of dofs per element
num_dof_in_element = np.int(Nord*(Nord+2)*(Nord+3)/2)
# Compute dofs list for edges, faces and volume
# Initialize dof counter
current_dof = 0
# Compute connectivity for dofs on edges
dof_edges = np.zeros((nEdges, num_dof_in_edge), dtype=np.int)
for index_edge in np.arange(nEdges):
dof_edges[index_edge,:] = np.arange(current_dof, current_dof+num_dof_in_edge)
current_dof += num_dof_in_edge
# Compute connectivity for dofs on faces
dof_faces = np.zeros((nFaces, num_dof_in_face), dtype=np.int)
for index_face in np.arange(nFaces):
dof_faces[index_face,:] = np.arange(current_dof, current_dof+num_dof_in_face)
current_dof += num_dof_in_face
# Compute connectivity for dofs on volume
dof_volume = np.zeros((nElems, num_dof_in_volume), dtype=np.int)
for index_ele in np.arange(nElems):
dof_volume[index_ele,:] = np.arange(current_dof, current_dof+num_dof_in_volume)
current_dof += num_dof_in_volume
# Compute local/global dofs connectivity list
# Allocate
dof_connectivity = np.zeros((nElems, num_dof_in_element), dtype=np.int)
# Compute local/global dofs connectivity
for i in np.arange(nElems):
# Initialize dof counter
dof_counter = 0
# Get dofs on edges
for j in np.arange(num_edges_in_element):
dof_connectivity[i, np.arange(dof_counter, dof_counter+num_dof_in_edge)] = dof_edges[elemsE[i,j],:]
dof_counter += num_dof_in_edge
# Get dofs on faces
for j in np.arange(num_faces_in_element):
dof_connectivity[i, np.arange(dof_counter, dof_counter+num_dof_in_face)] = dof_faces[elemsF[i,j],:]
dof_counter += num_dof_in_face
# Get dofs in volume
dof_connectivity[i, np.arange(dof_counter, dof_counter+num_dof_in_volume)] = dof_volume[i,:]
# Get total number of dofs
total_num_dofs = np.max(dof_connectivity) + 1
return dof_connectivity, dof_edges, dof_faces, dof_volume, total_num_dofs
[docs]def computeJacobian(eleNodes):
"""Compute the jacobian and its inverse.
:param ndarray eleNodes: spatial coordinates of the nodes with dimensions = (4,3)
:return: jacobian matrix and its inverse.
:rtype: ndarray
"""
# Allocate
jacobian = np.zeros((3, 3), dtype=np.float)
# Jacobian computation
jacobian[0, :] = eleNodes[1, :] - eleNodes[0, :]
jacobian[1, :] = eleNodes[2, :] - eleNodes[0, :]
jacobian[2, :] = eleNodes[3, :] - eleNodes[0, :]
# Inverse of jacobian
invjacobian = np.linalg.inv(jacobian)
return jacobian, invjacobian
[docs]def computeElementOrientation(edgesEle,nodesEle,edgesNodesEle,globalEdgesInFace):
r"""Compute the orientation for the computation of hierarchical basis functions of high-order (High-order nédélec basis functions).
:param ndarray edgesEle:list of element's edges
:param ndarray nodesEle: list of element's nodes
:param ndarray edgesNodesEle: list of nodes for each edge in edgesEle
:param ndarray globalEdgesInFace: list of edges for each face
:return: orientation for edges and orientation for faces
:rtype: ndarray.
.. note:: References:\n
Amor-Martin, A., Garcia-Castillo, L. E., & Garcia-Doñoro, D. D.
(2016). Second-order Nédélec curl-conforming prismatic element
for computational electromagnetics. IEEE Transactions on
Antennas and Propagation, 64(10), 4384-4395.
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocation (10 = 6 edges per element + 4 faces per element)
orientation = np.zeros(10, dtype=np.int)
# ---------------------------------------------------------------
# Compute orientation for edges
# ---------------------------------------------------------------
NumEdgesInElement = 6
for i in np.arange(NumEdgesInElement):
if i == 0:
localNodes = [0, 1]
elif i == 1:
localNodes = [1, 2]
elif i == 2:
localNodes = [0, 2]
elif i == 3:
localNodes = [0, 3]
elif i == 4:
localNodes = [1, 3]
elif i == 5:
localNodes = [2, 3]
nodesEleForThisEdge = nodesEle[localNodes]
globalNodesInEdge = edgesNodesEle[i, :]
orientationForThisEdge = 0
if ((nodesEleForThisEdge[0] == globalNodesInEdge[1]) and (nodesEleForThisEdge[1] == globalNodesInEdge[0])):
orientationForThisEdge = 1
orientation[i] = orientationForThisEdge
# ---------------------------------------------------------------
# Compute orientation for faces
# ---------------------------------------------------------------
NumFacesInElement = 4
for i in np.arange(NumFacesInElement):
if i == 0:
localEdges = [0, 1, 2]
elif i == 1:
localEdges = [0, 4, 3]
elif i == 2:
localEdges = [1, 5, 4]
elif i == 3:
localEdges = [2, 5, 3]
globalEdgesInElementForThisFace = edgesEle[localEdges]
orientationForThisFace = 0
k1 = 0
k2 = 0
for k in np.arange(3):
if (globalEdgesInElementForThisFace[0] == globalEdgesInFace[i, k]):
k1 = k + 1
if (globalEdgesInElementForThisFace[1] == globalEdgesInFace[i, k]):
k2 = k + 1
selection = k1*10 + k2
if selection == 12:
orientationForThisFace = 0
elif selection == 31:
orientationForThisFace = 1
elif selection == 23:
orientationForThisFace = 2
elif selection == 32:
orientationForThisFace = 3
elif selection == 13:
orientationForThisFace = 4
elif selection == 21:
orientationForThisFace = 5
orientation[6+i] = orientationForThisFace
# Edge orientation (6 edges)
edges_orientation = orientation[0:6]
# Face orientation (4 faces)
faces_orientation = orientation[6:11]
return edges_orientation, faces_orientation
[docs]def computeElementalMatrices(edge_orientation, face_orientation, jacobian, invjacob, Nord, sigmaEle):
r"""Compute the elemental mass matrix and stiffness matrix based ons high-order vector finite element.
:param ndarray edges_orientation: orientation for edges
:param ndarray faces_orientation: orientation for faces
:param ndarray jacobian: jacobian matrix
:param ndarray invjacob: inverse of jacobian matrix
:param int Nord: polynomial order of vector basis functions
:param ndarray sigmaEle: element conductivity with dimensions (1, 2), (horizontal and vertical)
:return: elemental mass matrix and elemental stiffness matrix
:rtype: ndarray
.. note:: References:\n
Fuentes, F., Keith, B., Demkowicz, L., & Nagaraj, S. (2015). Orientation
embedded high order shape functions for the exact sequence elements of
all shapes. Computers & Mathematics with applications, 70(4), 353-458.
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Compute number of dofs per element
num_dof_in_element = np.int(Nord*(Nord+2)*(Nord+3)/2)
# Allocate elemental mass matrix and stiffness matrix
Me = np.zeros((num_dof_in_element, num_dof_in_element), dtype=np.float)
Ke = np.zeros((num_dof_in_element, num_dof_in_element), dtype=np.float)
# Compute gauss points
gauss_order = 2*Nord
gaussPoints, Wi = compute3DGaussPoints(gauss_order)
ngaussP = gaussPoints.shape[0]
# Tensor for integration (Vertical transverse electric permittivity)
# sigmaX = sigmaY = sigmaEle[0]
# sigmaZ = sigmaEle[1]
e_r = np.diag((sigmaEle[0], sigmaEle[0], sigmaEle[1]))
# Tensor for integration (Constant magnetic permittivity)
mu_r = np.ones(3, dtype=np.float)
inv_mu_r = np.diag(mu_r**-1.)
# Compute determinant of jacobian
det_jacob = np.linalg.det(jacobian)
# ---------------------------------------------------------------
# Assembly matrices
# ---------------------------------------------------------------
for i in np.arange(ngaussP):
# Get gauss point coordinates
X = gaussPoints[i, :]
# Polynomial order (6 edges, 4 faces, 1 volume)
Nord_vector = np.ones(11, dtype=np.int)*Nord
# Edge orientation (6 edges)
NoriE = edge_orientation
# Face orientation (4 faces)
NoriF = face_orientation
# Compute basis for iPoint
NrdofE, ShapE, CurlE = shape3DETet(X, Nord_vector, NoriE, NoriF)
# Verify consistency of number of dofs for this point
if (NrdofE != num_dof_in_element):
Print.master(' Number of DOFs is not consistent')
exit(-1)
# Niref=Ni in reference element
Niref = ShapE[0:3, 0:NrdofE]
# Ni=Ni in real element
Ni_real = np.matmul(invjacob, Niref)
# Perform mass matrix integration
for j in np.arange(num_dof_in_element):
for k in np.arange(num_dof_in_element):
Me[j,k] += Wi[i] * np.dot(np.matmul(Ni_real[:,j], e_r), Ni_real[:,k])
# Transform curl on reference element to real element
curl_reference_element = CurlE[0:3, 0:NrdofE]
curl_real_element = np.zeros((num_dof_in_element,3), dtype=np.float)
for j in np.arange(num_dof_in_element):
curl_real_element[j,:] = np.matmul(curl_reference_element[:,j],jacobian)/det_jacob
# Perform stiffness matrix integration
for j in np.arange(num_dof_in_element):
for k in np.arange(num_dof_in_element):
Ke[j,k] += Wi[i] * np.dot(np.matmul(curl_real_element[j,:], inv_mu_r), curl_real_element[k,:])
# Matrix normalization
Me = Me*det_jacob
Ke = Ke*det_jacob
return Me, Ke
[docs]def shape3DETet(X, Nord, NoriE, NoriF):
r"""Compute values of 3D tetrahedron element H(curl) shape functions and their derivatives.
:param ndarray X: master tetrahedron coordinates from (0,1)^3
:param int Nord: polynomial order
:param ndarray NoriE: edge orientation
:param ndarray NoriF: face orientation
:return: number of dof, values of the shape functions at the point, curl of the shape functions
:rtype: ndarray.
.. note:: References:\n
Fuentes, F., Keith, B., Demkowicz, L., & Nagaraj, S. (2015). Orientation
embedded high order shape functions for the exact sequence elements of
all shapes. Computers & Mathematics with applications, 70(4), 353-458.
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
MAXP = 6
MAXtetraE = np.int(MAXP*(MAXP+2)*(MAXP+3)/2)
IdecB = np.array([False, False])
# Spatial dimension
N = 3
# Allocate matrices for shape functions
ShapE = np.zeros((3, MAXtetraE), dtype=np.float)
CurlE = np.zeros((3, MAXtetraE), dtype=np.float)
# Local parameters
minI = 0
minJ = 1
minK = 1
minIJ = minI+minJ
minIJK = minIJ+minK
NumEdgesInElement = 6
NumFacesInElement = 4
# Initialize counter for shape functions
m = 0
# ---------------------------------------------------------------
# Basis computation
# ---------------------------------------------------------------
# Define affine coordinates and gradients
Lam, DLam = AffineTetrahedron(X)
# Edge shape functions
LampE, DLampE, IdecE = ProjectTetE(Lam, DLam)
# Loop over edges
for i in np.arange(NumEdgesInElement):
# Local parameters
nordE = Nord[i]
ndofE = nordE
if (ndofE > 0):
# Local parameters
maxI = nordE-1
# Orient
D = DLampE[i, 0:N, 0:2]
GLampE, GDLampE = OrientE(LampE[i, 0:2], D, NoriE[i], N)
# Construct the shape functions
EE, CurlEE = AncEE(GLampE,GDLampE,nordE,IdecE,N)
for i in np.arange(minI, maxI+1):
ShapE[0:N, m] = EE[0:N, i]
CurlE[0:N, m] = CurlEE[0:N, i]
m += 1
# Face shape functions
LampF, DLampF, IdecF = ProjectTetF(Lam,DLam)
# Loop over faces
for i in np.arange(NumFacesInElement):
# Local parameters
nordF = Nord[6+i]
ndofF = np.int(nordF*(nordF-1)/2)
if (ndofF > 0):
# Local parameters (again)
maxIJ = nordF-1;
# Orient
D = DLampF[i, 0:N, 0:3]
GLampF, GDLampF = OrientTri(LampF[i, 0:3], D, NoriF[i], N)
# Loop over families
famctr = m
for j in np.arange(2):
m = famctr+j-1
abc = np.roll([0,1,2], -j)
# Construct the shape functions
ETri, CurlETri = AncETri(GLampF[abc], GDLampF[0:N,abc], nordF, IdecF, N)
for k in np.arange(minIJ, maxIJ+1):
for r in np.arange(minI, k-minJ+1):
p = k-r
m += 2
ShapE[0:N, m-1] = ETri[0:N, r, p-1]
CurlE[0:N, m-1] = CurlETri[0:N, r, p-1]
# BUBBLE FUNCTIONS
# Local parameters
nordB = Nord[10]
ndofB = np.int(nordB*(nordB-1)*(nordB-2)/6)
# If necessary, create bubbles
if (ndofB > 0):
# Local parameters (again)
IdecB[0] = IdecF
IdecB[1] = True
minbeta = 2*minIJ
maxIJK = nordB-1
maxK = maxIJK-minIJ
# Loop over families
famctr = m
for i in np.arange(3):
m = famctr+i-2
abcd = np.roll([0,1,2,3], -i)
abc = abcd[[0,1,2]]
d = abcd[3]
# Now construct the shape functions (no need to orient)
ETri,CurlETri = AncETri(Lam[abc],DLam[0:N,abc],nordB-minK,IdecB[0],N)
tmp1 = np.array([1-Lam[d],Lam[d]], dtype=np.float)
tmp2 = np.array([-DLam[0:N,d],DLam[0:N,d]], dtype=np.float)
homLbet, DhomLbet = HomIJacobi(tmp1,tmp2,maxK,minbeta,IdecB[1],N)
for j in np.arange(minIJK, maxIJK+1):
for k in np.arange(minIJ, j-minK+1):
for r in np.arange(minI, k-minJ+1):
p = k-r
q = j-k
m += 3
ShapE[0:N, m-1] = ETri[0:N,r,p-1]*homLbet[k-1,q-1]
DhomLbetxETri = np.cross(DhomLbet[0:N,k-1,q-1], ETri[0:N,r,p-1])
CurlE[0:N, m-1] = homLbet[k-1,q-1]*CurlETri[0:N,r,p-1] + DhomLbetxETri
# Get total degrees of freedom
# First order = 6
# Second order = 20
# Third order = 45
# Fourth order = 84
# Fifth order = 140
# Sixth order = 216
NrdofE = m
return NrdofE, ShapE, CurlE
[docs]def AncEE(S, DS, Nord, Idec, N):
r"""Compute edge Hcurl ancillary functions and their curls.
:param ndarray S: affine coordinates associated to edge
:param ndarray DS: derivatives of S in R^N
:param int Nord: polynomial order
:param bool Idec: Binary flag
:param int N: spatial dimension
:return: edge Hcurl ancillary functions, curls of edge Hcurl ancillary functions
:rtype: ndarray
.. note:: References:\n
Idec: = FALSE s0+s1 != 1
= TRUE s0+s1 = 1
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
EE = np.zeros((N, Nord), dtype=np.float)
CurlEE = np.zeros((2*N-3, Nord), dtype=np.float)
# Local parameters
minI = 1
maxI = Nord
Ncurl = 2*N-3
# Extract homogenized Legendre polyomials first
homP = HomLegendre(S,maxI)
# Simplified case
if (Idec):
for i in np.arange(minI, maxI+1):
EE[0:N, i-1] = homP[i-1]*DS[0:N, 1]
# No need to compute Whitney function or curl
CurlEE[0:Ncurl, minI-1: maxI-1] = 0.
# In general
else:
# Lowest order Whitney function and its curl
whiE = S[0]*DS[0:N, 1] - S[1]*DS[0:N, 0]
curlwhiE = np.cross(DS[0:N, 0], DS[0:N, 1])
# Now construct the higher order elements
for i in np.arange(minI, maxI+1):
EE[0:N, i-1] = homP[i-1]*whiE
CurlEE[0:Ncurl, i-1] = (i+1)*homP[i-1]*curlwhiE
return EE, CurlEE
[docs]def AncETri(S, DS, Nord, Idec, N):
"""Compute triangle face Hcurl ancillary functions and their curls.
:param ndarray S: (s0,s1,s2) affine coordinates associated to triangle face
:param ndarray DS: derivatives of S0,S1,S2
:param int Nord: polynomial order
:param boll Idec: Binary flag:
:param int N: spatial dimension
:return: triangle Hcurl ancillary functions and curls of triangle Hcurl ancillary functions
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
ETri = np.zeros((N, Nord-1, Nord-1), dtype=np.float)
CurlETri = np.zeros((2*N-3, Nord-1, Nord-1), dtype=np.float)
DsL = np.zeros((3,2), dtype=np.float)
sL = np.zeros(2, dtype=np.float)
# Local parameters
minI = 0
#maxI = Nord-2
minJ = 1
maxJ = Nord-1
#minIJ = minI+minJ
maxIJ = Nord-1
minalpha = 2*minI+1
Ncurl = 2*N-3
IdecE = False
# get EE - this is never a simplified case (IdecE=0)
EE, curlEE = AncEE(S[0:2], DS[0:N, 0:2], Nord-minJ, IdecE, N)
# get homogenized Integrated Jacobi polynomials, homLal, and gradients
sL[0] = S[0]+S[1]
sL[1] = S[2]
DsL[0:N, 0] = DS[0:N, 0] + DS[0:N, 1]
DsL[0:N, 1] = DS[0:N, 2]
homLal, DhomLal = HomIJacobi(sL, DsL, maxJ, minalpha, Idec, N)
# Simply complete the required information
for i in np.arange(maxIJ+1):
for j in np.arange(minI, i-minJ+1):
k = i-j
ETri[0:N, j, k-1] = EE[0:N,j]*homLal[j,k-1]
DhomLalxEE = np.cross(DhomLal[0:N, j, k-1], EE[0:N,j])
CurlETri[0:Ncurl, j, k-1] = homLal[j,k-1]*curlEE[0:Ncurl,j] + DhomLalxEE
return ETri, CurlETri
[docs]def HomLegendre(S, Nord):
"""Compute values of homogenized Legendre polynomials.
:param ndarray S: affine(like) coordinates
:param int Nord: polynomial order
:return: polynomial values
:rtype: ndarray
"""
# Simply the definition of homogenized polynomials
HomP = PolyLegendre(S[1], S[0]+S[1], Nord)
return HomP
[docs]def HomIJacobi(S, DS, Nord, Minalpha, Idec, N):
"""Compute values of integrated homogenized Jacobi polynomials and their gradients.
Result is half of a matrix with each row associated to a fixed alpha.
Alpha grows by 2 in each row.
:param ndarray S: (s0,s1) affine(like) coordinates
:param ndarray DS: gradients of S in R(N)
:param int Nord: max polynomial order
:param int Minalpha: first row value of alpha (integer)
:param bool Idec: decision flag to compute
:return: polynomial values and derivatives in x (Jacobi polynomials)
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
DHomL = np.zeros((N, Nord, Nord), dtype=np.float)
# clearly (minI,maxI)=(1,Nord), but the syntax is written as it is
# because it reflects how the indexing is called from outside
minI = 1
maxI = minI+Nord-1
if (Idec):
HomL, homP, _ = PolyIJacobi(S[1],1,Nord,Minalpha,Idec)
ni = -1
for i in np.arange(minI, maxI+1):
ni += 1
for j in np.arange(1, Nord-ni+1):
DHomL[0:N, i-1, j-1] = homP[i-1, j-1]*DS[1, 0:N]
else:
# If sum of S different from 1 -> Idec=.FALSE.
HomL, homP, homR = PolyIJacobi(S[1], S[0]+S[1], Nord, Minalpha, Idec)
ni = -1
DS01 = DS[0:N, 0] + DS[0:N, 1]
for i in np.arange(minI, maxI+1):
ni += 1
for j in np.arange(1, Nord-ni+1):
DHomL[0:N, i-1, j-1] = homP[i-1, j-1]*DS[0:N, 1] + homR[i-1, j-1]*DS01
return HomL, DHomL
[docs]def PolyLegendre(X, T, Nord):
"""Compute values of shifted scaled Legendre polynomials.
:param ndarray X: coordinate from [0,1]
:param float T: scaling parameter
:param int Nord: polynomial order
:return: polynomial values
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
P = np.zeros(Nord, dtype=np.float)
# i stands for the order of the polynomial, stored in P(i)
# lowest order case (order 0)
P[0] = 1.
# First order case (order 1) if necessary
if (Nord == 1):
y = 2.*X - T
P = np.hstack((P, y))
# Higher order if necessary - use recurrence formula
if (Nord >= 2):
# Compute second point
y = 2.*X - T
P[1] = y
# Remaining points
tt = T**2
for i in np.arange(1, Nord-1):
P[i+1] = (2.*i+1)*y*P[i] - (i)*tt*P[i-1]
P[i+1] = P[i+1]/np.float(i+1)
return P
[docs]def PolyIJacobi(X, T, Nord, Minalpha, Idec):
"""Compute values of integrated shifted scaled Jacobi polynomials and their derivatives starting with p=1.
Result is 'half' of a matrix with each row associated to a fixed alpha.
Alpha grows by 2 in each row.
:param ndarray X: coordinate from [0,1]
:param ndarray T: scaling parameter
:param int Nord: max polynomial order
:param int Minalpha: first row value of alpha
:param bool Idec: decision flag to compute (= FALSE polynomials with x and t derivatives, = TRUE polynomials with x derivatives only)
:return: polynomial values, derivatives in x (Jacobi polynomials), derivatives in t
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
alpha = np.zeros(Nord, dtype=np.float)
L = np.zeros((Nord, Nord), dtype=np.float)
R = np.zeros((Nord, Nord), dtype=np.float)
# clearly (minI,maxI)=(1,Nord), but the syntax is written as it is
# because it reflects how the indexing is called from outside
minI = 0
maxI = minI+Nord
# Calling Jacobi for required information
ptemp = PolyJacobi(X,T,Nord,Minalpha)
# Define P. Note that even though P is defined at all entries,
# because of the way Jacobi computes ptemp, only the necessary entries,
# and those on the first subdiagonal (which are never used later)
# are actually accurate.
P = ptemp[minI:maxI, 0:Nord]
# Create vector alpha first
for i in np.arange(maxI):
alpha[i] = Minalpha+2*(i-minI)
# Initiate first column (order 1 in L)
L[minI:maxI, 0] = X
# General case; compute R
R[minI:maxI, ] = 0
# Fill the last columns if necessary
if (Nord>=2):
tt = T**2
ni = -1
for i in np.arange(maxI-1):
al = alpha[i]
ni += 1
for j in np.arange(2, Nord-ni+1):
tia = j+j+al
tiam1 = tia-1
tiam2 = tia-2
ai = (j+al)/(tiam1*tia)
bi = (al)/(tiam2*tia)
ci = (j-1)/(tiam2*tiam1)
L[i, j-1] = ai*ptemp[i, j]+bi*T*ptemp[i,j-1]-ci*tt*ptemp[i,j-2]
R[i, j-1] = -(j-1)*(ptemp[i,j-1]+T*ptemp[i,j-2])
R[i, j-1] = R[i, j-1]/tiam2
return L, P, R
[docs]def PolyJacobi(X, T, Nord, Minalpha):
"""Compute values of shifted scaled Jacobi polynomials P**alpha-i.
Result is a half of a matrix with each row associated to a fixed alpha.
Alpha grows by 2 in each row.
:param ndarray X: coordinate from [0,1]
:param float T: scaling parameter
:param int Nord: max polynomial order
:param int Minalpha: first row value of alpha (integer)
:return: polynomial values
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
alpha = np.zeros(Nord+1, dtype=np.float)
P = np.zeros((Nord+1, Nord+1), dtype=np.float)
# Clearly (minI,maxI)=(0,Nord), but the syntax is written as it is
# because it reflects how the indexing is called from outside
minI = 0
maxI = minI+Nord
for i in np.arange(maxI+1):
alpha[i] = Minalpha+2*(i-minI)
# Initiate first column (order 0)
P[minI:maxI+1, 0] = 1.
# Initiate second column (order 1) if necessary
if (Nord>=1):
y = 2*X - T
indx = np.arange(minI, maxI)
P[indx, 1] = y+alpha[indx]*X
# Fill the last columns if necessary
if (Nord >= 2):
tt = T**2
ni = -1
for i in np.arange(maxI-1):
al = alpha[i]
aa = al**2
ni += 1
# Use recursion in order, i, to compute P^alpha_i for i>=2
for j in np.arange(2, Nord-ni+1):
ai = 2*j*(j+al)*(2*j+al-2)
bi = 2*j+al-1
ci = (2*j+al)*(2*j+al-2)
di = 2*(j+al-1)*(j-1)*(2*j+al)
P[i,j] = bi*(ci*y+aa*T)*P[i,j-1]-di*tt*P[i,j-2]
P[i,j] = P[i, j]/ai
return P
[docs]def OrientE(S, DS, Nori, N):
"""Compute the local to global transformations of edges.
:param ndarray S: projection of affine coordinates on edges
:param ndarray DS: projection of gradients of affine coordinates on edges
:param ndarray Nori: edge orientation
:param int N: number of dimensions
:return: global transformation of edges and global transformation of gradients of edges
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
Or = np.zeros((2,2), dtype=np.float)
GS = np.zeros(2, dtype=np.float)
GDS = np.zeros((3,2), dtype=np.float)
# Nori=0 => (s0,s1)->(s0,s1)
Or[0, 0] = 0
Or[0, 1] = 1
# Nori=1 => (s0,s1)->(s1,s0)
Or[1, 0] = 1
Or[1, 1] = 0
# Local-to-global transformation
GS[0] = S[np.int(Or[Nori, 0])]
GS[1] = S[np.int(Or[Nori, 1])]
GDS[0:N, 0] = DS[0:N, np.int(Or[Nori,0])]
GDS[0:N, 1] = DS[0:N, np.int(Or[Nori,1])]
return GS, GDS
[docs]def OrientTri(S, DS, Nori, N):
"""Compute the local to global transformations of edges.
:param ndarray S: projection of affine coordinates on faces
:param ndarray DS: projection of gradients of affine coordinates on faces
:param ndarray Nori: face orientation
:param int N: number of dimensions
:return: global transformation of faces and global transformation of gradients of faces
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
Or = np.zeros((6,3), np.int)
GS = np.zeros(3, np.float)
GDS = np.zeros((3,3), np.float)
# GS(0:2)=S(Or(Nori,0:2))
# Nori=0 => (s0,s1,s2)->(s0,s1,s2)
Or[0, 0] = 0
Or[0, 1] = 1
Or[0, 2] = 2
# Nori=1 => (s0,s1,s2)->(s1,s2,s0)
Or[1, 0] = 1
Or[1, 1] = 2
Or[1, 2] = 0
# Nori=2 => (s0,s1,s2)->(s2,s0,s1)
Or[2, 0] = 2
Or[2, 1] = 0
Or[2, 2] = 1
# Nori=3 => (s0,s1,s2)->(s0,s2,s1)
Or[3, 0] = 0
Or[3, 1] = 2
Or[3, 2] = 1
# Nori=4 => (s0,s1,s2)->(s1,s0,s2)
Or[4, 0] = 1
Or[4, 1] = 0
Or[4, 2] = 2
# Nori=5 => (s0,s1,s2)->(s2,s1,s0)
Or[5, 0] = 2
Or[5, 1] = 1
Or[5, 2] = 0
# Local-to-global transformation
GS[0] = S[Or[Nori,0]]
GS[1] = S[Or[Nori,1]]
GS[2] = S[Or[Nori,2]]
GDS[0:N, 0] = DS[0:N, Or[Nori, 0]]
GDS[0:N, 1] = DS[0:N, Or[Nori, 1]]
GDS[0:N, 2] = DS[0:N, Or[Nori, 2]]
return GS, GDS
[docs]def ProjectTetE(Lam, DLam):
r"""Projection of tetrahedral edges in concordance with numbering of topological entities (vertices, edges, faces).
:param ndarray Lam: affine coordinates
:param ndarray DLam: gradients of affine coordinates
:return: projection of affine coordinates on edges, projection of gradients of affine coordinates on edges
:rtype: ndarray
.. note:: References:\n
Fuentes, F., Keith, B., Demkowicz, L., & Nagaraj, S. (2015). Orientation
embedded high order shape functions for the exact sequence elements of
all shapes. Computers & Mathematics with applications, 70(4), 353-458.
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# 6 edges, each with a locally oriented pair representing a projection
N = 3
num_edges_in_element = 6
LampE = np.zeros((num_edges_in_element, 2), dtype=np.float)
DLampE = np.zeros((num_edges_in_element, N, 2), dtype=np.float)
# ---------------------------------------------------------------
# Compute projection
# ---------------------------------------------------------------
# e=1 --> edge01 with local orientation v0->v1
e = 0
LampE[e, 0] = Lam[0]
LampE[e, 1] = Lam[1]
DLampE[e, 0:N, 0] = DLam[0:N, 0]
DLampE[e, 0:N, 1] = DLam[0:N, 1]
# e=2 --> edge12 with local orientation v1->v2
e = 1
LampE[e, 0] = Lam[1]
LampE[e, 1] = Lam[2]
DLampE[e, 0:N, 0] = DLam[0:N, 1]
DLampE[e, 0:N, 1] = DLam[0:N, 2]
# e=3 --> edge20 with local orientation v0->v2
e = 2
LampE[e, 0] = Lam[0]
LampE[e, 1] = Lam[2]
DLampE[e, 0:N, 0] = DLam[0:N, 0]
DLampE[e, 0:N, 1] = DLam[0:N, 2]
# e=4 --> edge03 with local orientation v0->v3
e = 3
LampE[e, 0] = Lam[0]
LampE[e, 1] = Lam[3]
DLampE[e, 0:N, 0] = DLam[0:N, 0]
DLampE[e, 0:N, 1] = DLam[0:N, 3]
# e=5 --> edge13 with local orientation v1->v3
e = 4
LampE[e, 0] = Lam[1]
LampE[e, 1] = Lam[3]
DLampE[e, 0:N, 0] = DLam[0:N, 1]
DLampE[e, 0:N, 1] = DLam[0:N, 3]
# e=6 --> edge23 with local orientation v2->v3
e = 5
LampE[e, 0] = Lam[2]
LampE[e, 1] = Lam[3]
DLampE[e, 0:N, 0] = DLam[0:N, 2]
DLampE[e, 0:N, 1] = DLam[0:N, 3]
# Projected coordinates are Lam, so IdecE=false for all edges
IdecE = False
return LampE, DLampE, IdecE
[docs]def ProjectTetF(Lam, DLam):
"""Projection of tetrahedral faces in concordance with numbering of topological entities (vertices, edges, faces).
:param ndarray Lam: affine coordinates
:param ndarray DLam: gradients of affine coordinates
:return: projection of affine coordinates on faces, projection of gradients of affine coordinates on faces
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# 4 faces, each with a locally oriented triplet representing
# a projection
# Allocate
N = 3
num_faces_in_element = 4
LampF = np.zeros((num_faces_in_element, N), dtype=np.float)
DLampF = np.zeros((num_faces_in_element, N, N), dtype=np.float)
# ---------------------------------------------------------------
# Compute projection
# ---------------------------------------------------------------
# f=1 --> face012 with local orientation v0->v1->v2
f = 0
LampF[f, 0] = Lam[0]
LampF[f, 1] = Lam[1]
LampF[f, 2] = Lam[2]
DLampF[f, 0:N, 0] = DLam[0:N, 0]
DLampF[f, 0:N, 1] = DLam[0:N, 1]
DLampF[f, 0:N, 2] = DLam[0:N, 2]
# f=2 --> face013 with local orientation v0->v1->v3
f = 1
LampF[f, 0] = Lam[0]
LampF[f, 1] = Lam[1]
LampF[f, 2] = Lam[3]
DLampF[f, 0:N, 0] = DLam[0:N, 0]
DLampF[f, 0:N, 1] = DLam[0:N, 1]
DLampF[f, 0:N, 2] = DLam[0:N, 3]
# f=3 --> face123 with local orientation v1->v2->v3
f = 2
LampF[f, 0] = Lam[1]
LampF[f, 1] = Lam[2]
LampF[f, 2] = Lam[3]
DLampF[f, 0:N, 0] = DLam[0:N, 1]
DLampF[f, 0:N, 1] = DLam[0:N, 2]
DLampF[f, 0:N, 2] = DLam[0:N, 3]
# f=4 --> face023 with local orientation v0->v2->v3
f = 3
LampF[f, 0] = Lam[0]
LampF[f, 1] = Lam[2]
LampF[f, 2] = Lam[3]
DLampF[f, 0:N, 0] = DLam[0:N, 0]
DLampF[f, 0:N, 1] = DLam[0:N, 2]
DLampF[f, 0:N, 2] = DLam[0:N, 3]
# Pprojected coordinates are Lam, so IdecF=false for all faces
IdecF = False
return LampF, DLampF, IdecF
[docs]def AffineTetrahedron(X):
r"""Compute affine coordinates and their gradients.
:param ndarray X: point coordinates
:return: affine coordinates and gradients of affine coordinates
:rtype: ndarray
.. note:: References:\n
Fuentes, F., Keith, B., Demkowicz, L., & Nagaraj, S. (2015). Orientation
embedded high order shape functions for the exact sequence elements of
all shapes. Computers & Mathematics with applications, 70(4), 353-458.
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Allocate
Lam = np.zeros(4, dtype=np.float)
DLam = np.zeros((3,4), dtype=np.float)
# Define affine coordinates
Lam[0] = 1.-X[0]-X[1]-X[2]
Lam[1] = X[0]
Lam[2] = X[1]
Lam[3] = X[2]
# and their gradients
DLam[0, 0] = -1
DLam[0, 1] = 1
DLam[1, 0] = -1
DLam[1, 2] = 1
DLam[2, 0] = -1
DLam[2, 3] = 1
return Lam, DLam
[docs]def compute3DGaussPoints(Nord):
"""Compute 3D gauss points for high-order nédélec elements.
:param int Nord: polynomial order of nedelec basis functions
:return: coordinates of gauss points and its weights
:rtype: ndarray.
"""
# Select gauss points
if Nord == 1:
table = np.array([-0.500000000000000, -0.500000000000000, -0.500000000000000, 1.333333333333333], dtype=np.float)
elif Nord == 2:
table = np.array([[-0.723606797749979, -0.723606797749979, -0.723606797749979, 0.333333333333333],
[ 0.170820393249937, -0.723606797749979, -0.723606797749979, 0.333333333333333],
[-0.723606797749979, 0.170820393249937, -0.723606797749979, 0.333333333333333],
[-0.723606797749979, -0.723606797749979, 0.170820393249937, 0.333333333333333]], dtype=np.float)
elif Nord == 3:
table = np.array([[-0.500000000000000, -0.500000000000000, -0.500000000000000, -1.066666666666667],
[-0.666666666666667, -0.666666666666667, -0.666666666666667, 0.600000000000000],
[-0.666666666666667, -0.666666666666667, 0.000000000000000, 0.600000000000000],
[-0.666666666666667, 0.000000000000000, -0.666666666666667, 0.600000000000000],
[ 0.000000000000000, -0.666666666666667, -0.666666666666667, 0.600000000000000]], dtype=np.float)
elif Nord == 4:
table = np.array([[-0.500000000000000, -0.500000000000000, -0.500000000000000, -0.105244444444444],
[-0.857142857142857, -0.857142857142857, -0.857142857142857, 0.060977777777778],
[-0.857142857142857, -0.857142857142857, 0.571428571428571, 0.060977777777778],
[-0.857142857142857, 0.571428571428571, -0.857142857142857, 0.060977777777778],
[ 0.571428571428571, -0.857142857142857, -0.857142857142857, 0.060977777777778],
[-0.201192847666402, -0.201192847666402, -0.798807152333598, 0.199111111111111],
[-0.201192847666402, -0.798807152333598, -0.201192847666402, 0.199111111111111],
[-0.798807152333598, -0.201192847666402, -0.201192847666402, 0.199111111111111],
[-0.201192847666402, -0.798807152333598, -0.798807152333598, 0.199111111111111],
[-0.798807152333598, -0.201192847666402, -0.798807152333598, 0.199111111111111],
[-0.798807152333598, -0.798807152333598, -0.201192847666402, 0.199111111111111]], dtype=np.float)
elif Nord == 5:
table = np.array([[-0.814529499378218, -0.814529499378218, -0.814529499378218, 0.097990724155149],
[ 0.443588498134653, -0.814529499378218, -0.814529499378218, 0.097990724155149],
[-0.814529499378218, 0.443588498134653, -0.814529499378218, 0.097990724155149],
[-0.814529499378218, -0.814529499378218, 0.443588498134653, 0.097990724155149],
[-0.378228161473399, -0.378228161473399, -0.378228161473399, 0.150250567624021],
[-0.865315515579804, -0.378228161473399, -0.378228161473399, 0.150250567624021],
[-0.378228161473399, -0.865315515579804, -0.378228161473399, 0.150250567624021],
[-0.378228161473399, -0.378228161473399, -0.865315515579804, 0.150250567624021],
[-0.091007408251299, -0.091007408251299, -0.908992591748701, 0.056728027702775],
[-0.091007408251299, -0.908992591748701, -0.091007408251299, 0.056728027702775],
[-0.908992591748701, -0.091007408251299, -0.091007408251299, 0.056728027702775],
[-0.091007408251299, -0.908992591748701, -0.908992591748701, 0.056728027702775],
[-0.908992591748701, -0.091007408251299, -0.908992591748701, 0.056728027702775],
[-0.908992591748701, -0.908992591748701, -0.091007408251299, 0.056728027702775]], dtype=np.float)
elif Nord == 6:
table = np.array([[-0.570794257481696, -0.570794257481696, -0.570794257481696, 0.053230333677557],
[-0.287617227554912, -0.570794257481696, -0.570794257481696, 0.053230333677557],
[-0.570794257481696, -0.287617227554912, -0.570794257481696, 0.053230333677557],
[-0.570794257481696, -0.570794257481696, -0.287617227554912, 0.053230333677557],
[-0.918652082930777, -0.918652082930777, -0.918652082930777, 0.013436281407094],
[ 0.755956248792332, -0.918652082930777, -0.918652082930777, 0.013436281407094],
[-0.918652082930777, 0.755956248792332, -0.918652082930777 ,0.013436281407094],
[-0.918652082930777, -0.918652082930777, 0.755956248792332 ,0.013436281407094],
[-0.355324219715449, -0.355324219715449, -0.355324219715449, 0.073809575391540],
[-0.934027340853653, -0.355324219715449, -0.355324219715449, 0.073809575391540],
[-0.355324219715449, -0.934027340853653, -0.355324219715449, 0.073809575391540],
[-0.355324219715449, -0.355324219715449, -0.934027340853653, 0.073809575391540],
[-0.872677996249965, -0.872677996249965, -0.460655337083368, 0.064285714285714],
[-0.872677996249965, -0.460655337083368, -0.872677996249965, 0.064285714285714],
[-0.872677996249965, -0.872677996249965, 0.206011329583298, 0.064285714285714],
[-0.872677996249965, 0.206011329583298, -0.872677996249965, 0.064285714285714],
[-0.872677996249965, -0.460655337083368, 0.206011329583298, 0.064285714285714],
[-0.872677996249965, 0.206011329583298, -0.460655337083368, 0.064285714285714],
[-0.460655337083368, -0.872677996249965, -0.872677996249965, 0.064285714285714],
[-0.460655337083368, -0.872677996249965, 0.206011329583298, 0.064285714285714],
[-0.460655337083368, 0.206011329583298, -0.872677996249965, 0.064285714285714],
[ 0.206011329583298, -0.872677996249965, -0.460655337083368, 0.064285714285714],
[ 0.206011329583298, -0.872677996249965, -0.872677996249965, 0.064285714285714],
[ 0.206011329583298, -0.460655337083368, -0.872677996249965, 0.064285714285714]], dtype=np.float)
elif Nord == 7:
table = np.array([[0.000000000000000, 0.000000000000000, -1.000000000000000, 0.007760141093474],
[ 0.000000000000000, -1.000000000000000, 0.000000000000000, 0.007760141093474],
[-1.000000000000000, 0.000000000000000, 0.000000000000000, 0.007760141093474],
[-1.000000000000000, -1.000000000000000, 0.000000000000000, 0.007760141093474],
[-1.000000000000000, 0.000000000000000, -1.000000000000000, 0.007760141093474],
[ 0.000000000000000, -1.000000000000000, -1.000000000000000, 0.007760141093474],
[-0.500000000000000, -0.500000000000000, -0.500000000000000, 0.146113787728871],
[-0.843573615339364, -0.843573615339364, -0.843573615339364, 0.084799532195309],
[-0.843573615339364, -0.843573615339364, 0.530720846018092, 0.084799532195309],
[-0.843573615339364, 0.530720846018092, -0.843573615339364, 0.084799532195309],
[ 0.530720846018092, -0.843573615339364, -0.843573615339364, 0.084799532195309],
[-0.756313566672190, -0.756313566672190, -0.756313566672190, -0.500141920914655],
[-0.756313566672190, -0.756313566672190, 0.268940700016569, -0.500141920914655],
[-0.756313566672190, 0.268940700016569, -0.756313566672190, -0.500141920914655],
[ 0.268940700016569, -0.756313566672190, -0.756313566672190, -0.500141920914655],
[-0.334921671107159, -0.334921671107159, -0.334921671107159, 0.039131402104588],
[-0.334921671107159, -0.334921671107159, -0.995234986678524, 0.039131402104588],
[-0.334921671107159, -0.995234986678524, -0.334921671107159, 0.039131402104588],
[-0.995234986678524, -0.334921671107159, -0.334921671107159, 0.039131402104588],
[-0.800000000000000, -0.800000000000000, -0.600000000000000, 0.220458553791887],
[-0.800000000000000, -0.600000000000000, -0.800000000000000, 0.220458553791887],
[-0.800000000000000, -0.800000000000000, 0.200000000000000, 0.220458553791887],
[-0.800000000000000, 0.200000000000000, -0.800000000000000, 0.220458553791887],
[-0.800000000000000, -0.600000000000000, 0.200000000000000, 0.220458553791887],
[-0.800000000000000, 0.200000000000000, -0.600000000000000, 0.220458553791887],
[-0.600000000000000, -0.800000000000000, -0.800000000000000, 0.220458553791887],
[-0.600000000000000, -0.800000000000000, 0.200000000000000, 0.220458553791887],
[-0.600000000000000, 0.200000000000000, -0.800000000000000, 0.220458553791887],
[ 0.200000000000000, -0.800000000000000, -0.600000000000000, 0.220458553791887],
[ 0.200000000000000, -0.800000000000000, -0.800000000000000, 0.220458553791887],
[ 0.200000000000000, -0.600000000000000, -0.800000000000000, 0.220458553791887]], dtype=np.float)
elif Nord == 8:
table = np.array([[-0.500000000000000, -0.500000000000000, -0.500000000000000, -0.164001509269119],
[-0.586340136778654, -0.586340136778654, -0.586340136778654, 0.114002446582935],
[-0.586340136778654, -0.586340136778654, -0.240979589664039, 0.114002446582935],
[-0.586340136778654, -0.240979589664039, -0.586340136778654, 0.114002446582935],
[-0.240979589664039, -0.586340136778654, -0.586340136778654, 0.114002446582935],
[-0.835792823378907, -0.835792823378907, -0.835792823378907, 0.015736266505071],
[-0.835792823378907, -0.835792823378907, 0.507378470136720, 0.015736266505071],
[-0.835792823378907, 0.507378470136720, -0.835792823378907, 0.015736266505071],
[ 0.507378470136720, -0.835792823378907, -0.835792823378907, 0.015736266505071],
[-0.988436098989604, -0.988436098989604, -0.988436098989604, 0.001358672872743],
[-0.988436098989604, -0.988436098989604, 0.965308296968812, 0.001358672872743],
[-0.988436098989604, 0.965308296968812, -0.988436098989604, 0.001358672872743],
[ 0.965308296968812, -0.988436098989604, -0.988436098989604, 0.001358672872743],
[-0.898934519962212, -0.898934519962212, -0.101065480037788, 0.036637470595738],
[-0.898934519962212, -0.101065480037788, -0.898934519962212, 0.036637470595738],
[-0.101065480037788, -0.898934519962212, -0.898934519962212, 0.036637470595738],
[-0.898934519962212, -0.101065480037788, -0.101065480037788, 0.036637470595738],
[-0.101065480037788, -0.898934519962212, -0.101065480037788, 0.036637470595738],
[-0.101065480037788, -0.101065480037788, -0.898934519962212, 0.036637470595738],
[-0.541866927766378, -0.541866927766378, -0.928720834422932, 0.045635886469455],
[-0.541866927766378, -0.928720834422932, -0.541866927766378, 0.045635886469455],
[-0.541866927766378, -0.541866927766378, 0.012454689955687, 0.045635886469455],
[-0.541866927766378, 0.012454689955687, -0.541866927766378, 0.045635886469455],
[-0.541866927766378, -0.928720834422932, 0.012454689955687, 0.045635886469455],
[-0.541866927766378, 0.012454689955687, -0.928720834422932, 0.045635886469455],
[-0.928720834422932, -0.541866927766378, -0.541866927766378, 0.045635886469455],
[-0.928720834422932, -0.541866927766378, 0.012454689955687, 0.045635886469455],
[-0.928720834422932, 0.012454689955687, -0.541866927766378, 0.045635886469455],
[ 0.012454689955687, -0.541866927766378, -0.928720834422932, 0.045635886469455],
[ 0.012454689955687, -0.541866927766378, -0.541866927766378, 0.045635886469455],
[ 0.012454689955687, -0.928720834422932, -0.541866927766378, 0.045635886469455],
[-0.926784500893605, -0.926784500893605, -0.619027916130733, 0.017124153129297],
[-0.926784500893605, -0.619027916130733, -0.926784500893605, 0.017124153129297],
[-0.926784500893605, -0.926784500893605, 0.472596917917943, 0.017124153129297],
[-0.926784500893605, 0.472596917917943, -0.926784500893605, 0.017124153129297],
[-0.926784500893605, -0.619027916130733, 0.472596917917943, 0.017124153129297],
[-0.926784500893605, 0.472596917917943, -0.619027916130733, 0.017124153129297],
[-0.619027916130733, -0.926784500893605, -0.926784500893605, 0.017124153129297],
[-0.619027916130733, -0.926784500893605, 0.472596917917943, 0.017124153129297],
[-0.619027916130733, 0.472596917917943, -0.926784500893605, 0.017124153129297],
[ 0.472596917917943, -0.926784500893605, -0.619027916130733, 0.017124153129297],
[ 0.472596917917943, -0.926784500893605, -0.926784500893605, 0.017124153129297],
[ 0.472596917917943, -0.619027916130733, -0.926784500893605, 0.017124153129297]], dtype=np.float)
elif Nord == 9:
table = np.array([[-0.500000000000000, -0.500000000000000, -0.500000000000000, -1.102392306608869],
[-0.903297922900526, -0.903297922900526, -0.903297922900526, 0.014922692552682],
[-0.903297922900526, -0.903297922900526, 0.709893768701580, 0.014922692552682],
[-0.903297922900526, 0.709893768701580, -0.903297922900526, 0.014922692552682],
[ 0.709893768701580, -0.903297922900526, -0.903297922900526, 0.014922692552682],
[-0.350841439764235, -0.350841439764235, -0.350841439764235, 0.034475391755947],
[-0.350841439764235, -0.350841439764235, -0.947475680707294, 0.034475391755947],
[-0.350841439764235, -0.947475680707294, -0.350841439764235, 0.034475391755947],
[-0.947475680707294, -0.350841439764235, -0.350841439764235, 0.034475391755947],
[-0.770766919552010, -0.770766919552010, -0.770766919552010, -0.721478131849612],
[-0.770766919552010, -0.770766919552010, 0.312300758656029, -0.721478131849612],
[-0.770766919552010, 0.312300758656029, -0.770766919552010, -0.721478131849612],
[ 0.312300758656029, -0.770766919552010, -0.770766919552010, -0.721478131849612],
[-0.549020096176972, -0.549020096176972, -0.549020096176972, 0.357380609620092],
[-0.549020096176972, -0.549020096176972, -0.352939711469084, 0.357380609620092],
[-0.549020096176972, -0.352939711469084, -0.549020096176972, 0.357380609620092],
[-0.352939711469084, -0.549020096176972, -0.549020096176972, 0.357380609620092],
[-0.736744381506260, -0.736744381506260, -0.832670596765630, 0.277603247076406],
[-0.736744381506260, -0.832670596765630, -0.736744381506260, 0.277603247076406],
[-0.736744381506260, -0.736744381506260, 0.306159359778151, 0.277603247076406],
[-0.736744381506260, 0.306159359778151, -0.736744381506260, 0.277603247076406],
[-0.736744381506260, -0.832670596765630, 0.306159359778151, 0.277603247076406],
[-0.736744381506260, 0.306159359778151, -0.832670596765630, 0.277603247076406],
[-0.832670596765630, -0.736744381506260, -0.736744381506260, 0.277603247076406],
[-0.832670596765630, -0.736744381506260, 0.306159359778151, 0.277603247076406],
[-0.832670596765630, 0.306159359778151, -0.736744381506260, 0.277603247076406],
[ 0.306159359778151, -0.736744381506260, -0.832670596765630, 0.277603247076406],
[ 0.306159359778151, -0.736744381506260, -0.736744381506260, 0.277603247076406],
[ 0.306159359778151, -0.832670596765630, -0.736744381506260, 0.277603247076406],
[-0.132097077177186, -0.132097077177186, -0.784460280901143, 0.026820671221285],
[-0.132097077177186, -0.784460280901143, -0.132097077177186, 0.026820671221285],
[-0.132097077177186, -0.132097077177186, -0.951345564744484, 0.026820671221285],
[-0.132097077177186, -0.951345564744484, -0.132097077177186, 0.026820671221285],
[-0.132097077177186, -0.784460280901143, -0.951345564744484, 0.026820671221285],
[-0.132097077177186, -0.951345564744484, -0.784460280901143, 0.026820671221285],
[-0.784460280901143, -0.132097077177186, -0.132097077177186, 0.026820671221285],
[-0.784460280901143, -0.132097077177186, -0.951345564744484, 0.026820671221285],
[-0.784460280901143, -0.951345564744484, -0.132097077177186, 0.026820671221285],
[-0.951345564744484, -0.132097077177186, -0.784460280901143, 0.026820671221285],
[-0.951345564744484, -0.132097077177186, -0.132097077177186, 0.026820671221285],
[-0.951345564744484, -0.784460280901143, -0.132097077177186, 0.026820671221285],
[-1.002752554636276, -1.002752554636276, -0.446893054726385, 0.003453031004456],
[-1.002752554636276, -0.446893054726385, -1.002752554636276, 0.003453031004456],
[-1.002752554636276, -1.002752554636276, 0.452398163998938, 0.003453031004456],
[-1.002752554636276, 0.452398163998938, -1.002752554636276, 0.003453031004456],
[-1.002752554636276, -0.446893054726385, 0.452398163998938, 0.003453031004456],
[-1.002752554636276, 0.452398163998938, -0.446893054726385, 0.003453031004456],
[-0.446893054726385, -1.002752554636276, -1.002752554636276, 0.003453031004456],
[-0.446893054726385, -1.002752554636276, 0.452398163998938, 0.003453031004456],
[-0.446893054726385, 0.452398163998938, -1.002752554636276, 0.003453031004456],
[ 0.452398163998938, -1.002752554636276, -0.446893054726385, 0.003453031004456],
[ 0.452398163998938, -1.002752554636276, -1.002752554636276, 0.003453031004456],
[ 0.452398163998938, -0.446893054726385, -1.002752554636276, 0.003453031004456]], dtype=np.float)
elif Nord == 10 or Nord == 11:
table = np.array([[-0.857142857142857, -0.857142857142857, 0.571428571428571 , 0.362902592520648],
[-0.857142857142857, -0.571428571428571, 0.285714285714286, 0.362902592520648],
[-0.857142857142857, -0.285714285714286, 0.000000000000000, 0.362902592520648],
[-0.857142857142857, 0.000000000000000, -0.285714285714286, 0.362902592520648],
[-0.857142857142857, 0.285714285714286, -0.571428571428571, 0.362902592520648],
[-0.857142857142857, 0.571428571428571, -0.857142857142857, 0.362902592520648],
[-0.571428571428571, -0.857142857142857, 0.285714285714286, 0.362902592520648],
[-0.571428571428571, -0.571428571428571, 0.000000000000000, 0.362902592520648],
[-0.571428571428571, -0.285714285714286, -0.285714285714286, 0.362902592520648],
[-0.571428571428571, 0.000000000000000, -0.571428571428571, 0.362902592520648],
[-0.571428571428571, 0.285714285714286, -0.857142857142857, 0.362902592520648],
[-0.285714285714286, -0.857142857142857, 0.000000000000000, 0.362902592520648],
[-0.285714285714286, -0.571428571428571, -0.285714285714286, 0.362902592520648],
[-0.285714285714286, -0.285714285714286, -0.571428571428571, 0.362902592520648],
[-0.285714285714286, 0.000000000000000, -0.857142857142857, 0.362902592520648],
[ 0.000000000000000, -0.857142857142857, -0.285714285714286, 0.362902592520648],
[ 0.000000000000000, -0.571428571428571, -0.571428571428571, 0.362902592520648],
[ 0.000000000000000, -0.285714285714286, -0.857142857142857, 0.362902592520648],
[ 0.285714285714286, -0.857142857142857, -0.571428571428571, 0.362902592520648],
[ 0.285714285714286, -0.571428571428571, -0.857142857142857, 0.362902592520648],
[ 0.571428571428571, -0.857142857142857, -0.857142857142857, 0.362902592520648],
[-0.857142857142857, -0.857142857142857, 0.285714285714286, 0.362902592520648],
[-0.857142857142857, -0.571428571428571, 0.000000000000000, 0.362902592520648],
[-0.857142857142857, -0.285714285714286, -0.285714285714286, 0.362902592520648],
[-0.857142857142857, 0.000000000000000, -0.571428571428571, 0.362902592520648],
[-0.857142857142857, 0.285714285714286, -0.857142857142857, 0.362902592520648],
[-0.571428571428571, -0.857142857142857, 0.000000000000000, 0.362902592520648],
[-0.571428571428571, -0.571428571428571, -0.285714285714286, 0.362902592520648],
[-0.571428571428571, -0.285714285714286, -0.571428571428571, 0.362902592520648],
[-0.571428571428571, 0.000000000000000, -0.857142857142857, 0.362902592520648],
[-0.285714285714286, -0.857142857142857, -0.285714285714286, 0.362902592520648],
[-0.285714285714286, -0.571428571428571, -0.571428571428571, 0.362902592520648],
[-0.285714285714286, -0.285714285714286, -0.857142857142857, 0.362902592520648],
[ 0.000000000000000, -0.857142857142857, -0.571428571428571, 0.362902592520648],
[ 0.000000000000000, -0.571428571428571, -0.857142857142857, 0.362902592520648],
[ 0.285714285714286, -0.857142857142857, -0.857142857142857, 0.362902592520648],
[-0.857142857142857, -0.857142857142857, 0.000000000000000, 0.362902592520648],
[-0.857142857142857, -0.571428571428571, -0.285714285714286, 0.362902592520648],
[-0.857142857142857, -0.285714285714286, -0.571428571428571, 0.362902592520648],
[-0.857142857142857, 0.000000000000000, -0.857142857142857, 0.362902592520648],
[-0.571428571428571, -0.857142857142857, -0.285714285714286, 0.362902592520648],
[-0.571428571428571, -0.571428571428571, -0.571428571428571, 0.362902592520648],
[-0.571428571428571, -0.285714285714286, -0.857142857142857, 0.362902592520648],
[-0.285714285714286, -0.857142857142857, -0.571428571428571, 0.362902592520648],
[-0.285714285714286, -0.571428571428571, -0.857142857142857, 0.362902592520648],
[ 0.000000000000000, -0.857142857142857, -0.857142857142857, 0.362902592520648],
[-0.857142857142857, -0.857142857142857, -0.285714285714286, 0.362902592520648],
[-0.857142857142857, -0.571428571428571, -0.571428571428571, 0.362902592520648],
[-0.857142857142857, -0.285714285714286, -0.857142857142857, 0.362902592520648],
[-0.571428571428571, -0.857142857142857, -0.571428571428571, 0.362902592520648],
[-0.571428571428571, -0.571428571428571, -0.857142857142857, 0.362902592520648],
[-0.285714285714286, -0.857142857142857, -0.857142857142857, 0.362902592520648],
[-0.857142857142857, -0.857142857142857, -0.571428571428571, 0.362902592520648],
[-0.857142857142857, -0.571428571428571, -0.857142857142857, 0.362902592520648],
[-0.571428571428571, -0.857142857142857, -0.857142857142857, 0.362902592520648],
[-0.857142857142857, -0.857142857142857, -0.857142857142857, 0.362902592520648],
[-0.833333333333333, -0.833333333333333, 0.500000000000000, -0.932187812187812],
[-0.833333333333333, -0.500000000000000, 0.166666666666667, -0.932187812187812],
[-0.833333333333333, -0.166666666666667, -0.166666666666667, -0.932187812187812],
[-0.833333333333333, 0.166666666666667, -0.500000000000000, -0.932187812187812],
[-0.833333333333333, 0.500000000000000, -0.833333333333333, -0.932187812187812],
[-0.500000000000000, -0.833333333333333, 0.166666666666667, -0.932187812187812],
[-0.500000000000000, -0.500000000000000, -0.166666666666667, -0.932187812187812],
[-0.500000000000000, -0.166666666666667, -0.500000000000000, -0.932187812187812],
[-0.500000000000000, 0.166666666666667, -0.833333333333333, -0.932187812187812],
[-0.166666666666667, -0.833333333333333, -0.166666666666667, -0.932187812187812],
[-0.166666666666667, -0.500000000000000, -0.500000000000000, -0.932187812187812],
[-0.166666666666667, -0.166666666666667, -0.833333333333333, -0.932187812187812],
[ 0.166666666666667, -0.833333333333333, -0.500000000000000, -0.932187812187812],
[ 0.166666666666667, -0.500000000000000, -0.833333333333333, -0.932187812187812],
[ 0.500000000000000, -0.833333333333333, -0.833333333333333, -0.932187812187812],
[-0.833333333333333, -0.833333333333333, 0.166666666666667, -0.932187812187812],
[-0.833333333333333, -0.500000000000000, -0.166666666666667, -0.932187812187812],
[-0.833333333333333, -0.166666666666667, -0.500000000000000, -0.932187812187812],
[-0.833333333333333, 0.166666666666667, -0.833333333333333, -0.932187812187812],
[-0.500000000000000, -0.833333333333333, -0.166666666666667, -0.932187812187812],
[-0.500000000000000, -0.500000000000000, -0.500000000000000, -0.932187812187812],
[-0.500000000000000, -0.166666666666667, -0.833333333333333, -0.932187812187812],
[-0.166666666666667, -0.833333333333333, -0.500000000000000, -0.932187812187812],
[-0.166666666666667, -0.500000000000000, -0.833333333333333, -0.932187812187812],
[ 0.166666666666667, -0.833333333333333, -0.833333333333333, -0.932187812187812],
[-0.833333333333333, -0.833333333333333, -0.166666666666667, -0.932187812187812],
[-0.833333333333333, -0.500000000000000, -0.500000000000000, -0.932187812187812],
[-0.833333333333333, -0.166666666666667, -0.833333333333333, -0.932187812187812],
[-0.500000000000000, -0.833333333333333, -0.500000000000000, -0.932187812187812],
[-0.500000000000000, -0.500000000000000, -0.833333333333333, -0.932187812187812],
[-0.166666666666667, -0.833333333333333, -0.833333333333333, -0.932187812187812],
[-0.833333333333333, -0.833333333333333, -0.500000000000000, -0.932187812187812],
[-0.833333333333333, -0.500000000000000, -0.833333333333333, -0.932187812187812],
[-0.500000000000000, -0.833333333333333, -0.833333333333333, -0.932187812187812],
[-0.833333333333333, -0.833333333333333, -0.833333333333333, -0.932187812187812],
[-0.800000000000000, -0.800000000000000, 0.400000000000000, 0.815498319838598],
[-0.800000000000000, -0.400000000000000, 0.000000000000000, 0.815498319838598],
[-0.800000000000000, 0.000000000000000, -0.400000000000000, 0.815498319838598],
[-0.800000000000000, 0.400000000000000, -0.800000000000000, 0.815498319838598],
[-0.400000000000000, -0.800000000000000, 0.000000000000000, 0.815498319838598],
[-0.400000000000000, -0.400000000000000, -0.400000000000000, 0.815498319838598],
[-0.400000000000000, 0.000000000000000, -0.800000000000000, 0.815498319838598],
[ 0.000000000000000, -0.800000000000000, -0.400000000000000, 0.815498319838598],
[ 0.000000000000000, -0.400000000000000, -0.800000000000000, 0.815498319838598],
[ 0.400000000000000, -0.800000000000000, -0.800000000000000, 0.815498319838598],
[-0.800000000000000, -0.800000000000000, 0.000000000000000, 0.815498319838598],
[-0.800000000000000, -0.400000000000000, -0.400000000000000, 0.815498319838598],
[-0.800000000000000, 0.000000000000000, -0.800000000000000, 0.815498319838598],
[-0.400000000000000, -0.800000000000000, -0.400000000000000, 0.815498319838598],
[-0.400000000000000, -0.400000000000000, -0.800000000000000, 0.815498319838598],
[ 0.000000000000000, -0.800000000000000, -0.800000000000000, 0.815498319838598],
[-0.800000000000000, -0.800000000000000, -0.400000000000000, 0.815498319838598],
[-0.800000000000000, -0.400000000000000, -0.800000000000000, 0.815498319838598],
[-0.400000000000000, -0.800000000000000, -0.800000000000000, 0.815498319838598],
[-0.800000000000000, -0.800000000000000, -0.800000000000000, 0.815498319838598],
[-0.750000000000000, -0.750000000000000, 0.250000000000000, -0.280203089091978],
[-0.750000000000000, -0.250000000000000, -0.250000000000000, -0.280203089091978],
[-0.750000000000000, 0.250000000000000, -0.750000000000000, -0.280203089091978],
[-0.250000000000000, -0.750000000000000, -0.250000000000000, -0.280203089091978],
[-0.250000000000000, -0.250000000000000, -0.750000000000000, -0.280203089091978],
[ 0.250000000000000, -0.750000000000000, -0.750000000000000, -0.280203089091978],
[-0.750000000000000, -0.750000000000000, -0.250000000000000, -0.280203089091978],
[-0.750000000000000, -0.250000000000000, -0.750000000000000, -0.280203089091978],
[-0.250000000000000, -0.750000000000000, -0.750000000000000, -0.280203089091978],
[-0.750000000000000, -0.750000000000000, -0.750000000000000, -0.280203089091978],
[-0.666666666666667, -0.666666666666667, 0.000000000000000, 0.032544642857143],
[-0.666666666666667, 0.000000000000000, -0.666666666666667, 0.032544642857143],
[ 0.000000000000000, -0.666666666666667, -0.666666666666667, 0.032544642857143],
[-0.666666666666667, -0.666666666666667, -0.666666666666667, 0.032544642857143],
[-0.500000000000000, -0.500000000000000, -0.500000000000000, -0.000752498530276]], dtype=np.float)
elif Nord == 12:
table = np.array([[-0.875000000000000, -0.875000000000000, 0.625000000000000, 0.420407272132140],
[-0.875000000000000, -0.625000000000000, 0.375000000000000, 0.420407272132140],
[-0.875000000000000, -0.375000000000000, 0.125000000000000, 0.420407272132140],
[-0.875000000000000, -0.125000000000000, -0.125000000000000, 0.420407272132140],
[-0.875000000000000, 0.125000000000000, -0.375000000000000, 0.420407272132140],
[-0.875000000000000, 0.375000000000000, -0.625000000000000, 0.420407272132140],
[-0.875000000000000, 0.625000000000000, -0.875000000000000, 0.420407272132140],
[-0.625000000000000, -0.875000000000000, 0.375000000000000, 0.420407272132140],
[-0.625000000000000, -0.625000000000000, 0.125000000000000, 0.420407272132140],
[-0.625000000000000, -0.375000000000000, -0.125000000000000, 0.420407272132140],
[-0.625000000000000, -0.125000000000000, -0.375000000000000, 0.420407272132140],
[-0.625000000000000, 0.125000000000000, -0.625000000000000, 0.420407272132140],
[-0.625000000000000, 0.375000000000000, -0.875000000000000, 0.420407272132140],
[-0.375000000000000, -0.875000000000000, 0.125000000000000, 0.420407272132140],
[-0.375000000000000, -0.625000000000000, -0.125000000000000, 0.420407272132140],
[-0.375000000000000, -0.375000000000000, -0.375000000000000, 0.420407272132140],
[-0.375000000000000, -0.125000000000000, -0.625000000000000, 0.420407272132140],
[-0.375000000000000, 0.125000000000000, -0.875000000000000, 0.420407272132140],
[-0.125000000000000, -0.875000000000000, -0.125000000000000, 0.420407272132140],
[-0.125000000000000, -0.625000000000000, -0.375000000000000, 0.420407272132140],
[-0.125000000000000, -0.375000000000000, -0.625000000000000, 0.420407272132140],
[-0.125000000000000, -0.125000000000000, -0.875000000000000, 0.420407272132140],
[ 0.125000000000000, -0.875000000000000, -0.375000000000000, 0.420407272132140],
[ 0.125000000000000, -0.625000000000000, -0.625000000000000, 0.420407272132140],
[ 0.125000000000000, -0.375000000000000, -0.875000000000000, 0.420407272132140],
[ 0.375000000000000, -0.875000000000000, -0.625000000000000, 0.420407272132140],
[ 0.375000000000000, -0.625000000000000, -0.875000000000000, 0.420407272132140],
[ 0.625000000000000, -0.875000000000000, -0.875000000000000, 0.420407272132140],
[-0.875000000000000, -0.875000000000000, 0.375000000000000, 0.420407272132140],
[-0.875000000000000, -0.625000000000000, 0.125000000000000, 0.420407272132140],
[-0.875000000000000, -0.375000000000000, -0.125000000000000, 0.420407272132140],
[-0.875000000000000, -0.125000000000000, -0.375000000000000, 0.420407272132140],
[-0.875000000000000, 0.125000000000000, -0.625000000000000, 0.420407272132140],
[-0.875000000000000, 0.375000000000000, -0.875000000000000, 0.420407272132140],
[-0.625000000000000, -0.875000000000000, 0.125000000000000, 0.420407272132140],
[-0.625000000000000, -0.625000000000000, -0.125000000000000, 0.420407272132140],
[-0.625000000000000, -0.375000000000000, -0.375000000000000, 0.420407272132140],
[-0.625000000000000, -0.125000000000000, -0.625000000000000, 0.420407272132140],
[-0.625000000000000, 0.125000000000000, -0.875000000000000, 0.420407272132140],
[-0.375000000000000, -0.875000000000000, -0.125000000000000, 0.420407272132140],
[-0.375000000000000, -0.625000000000000, -0.375000000000000, 0.420407272132140],
[-0.375000000000000, -0.375000000000000, -0.625000000000000, 0.420407272132140],
[-0.375000000000000, -0.125000000000000, -0.875000000000000, 0.420407272132140],
[-0.125000000000000, -0.875000000000000, -0.375000000000000, 0.420407272132140],
[-0.125000000000000, -0.625000000000000, -0.625000000000000, 0.420407272132140],
[-0.125000000000000, -0.375000000000000, -0.875000000000000, 0.420407272132140],
[ 0.125000000000000, -0.875000000000000, -0.625000000000000, 0.420407272132140],
[ 0.125000000000000, -0.625000000000000, -0.875000000000000, 0.420407272132140],
[ 0.375000000000000, -0.875000000000000, -0.875000000000000, 0.420407272132140],
[-0.875000000000000, -0.875000000000000, 0.125000000000000, 0.420407272132140],
[-0.875000000000000, -0.625000000000000, -0.125000000000000, 0.420407272132140],
[-0.875000000000000, -0.375000000000000, -0.375000000000000, 0.420407272132140],
[-0.875000000000000, -0.125000000000000, -0.625000000000000, 0.420407272132140],
[-0.875000000000000, 0.125000000000000, -0.875000000000000, 0.420407272132140],
[-0.625000000000000, -0.875000000000000, -0.125000000000000, 0.420407272132140],
[-0.625000000000000, -0.625000000000000, -0.375000000000000, 0.420407272132140],
[-0.625000000000000, -0.375000000000000, -0.625000000000000, 0.420407272132140],
[-0.625000000000000, -0.125000000000000, -0.875000000000000, 0.420407272132140],
[-0.375000000000000, -0.875000000000000, -0.375000000000000, 0.420407272132140],
[-0.375000000000000, -0.625000000000000, -0.625000000000000, 0.420407272132140],
[-0.375000000000000, -0.375000000000000, -0.875000000000000, 0.420407272132140],
[-0.125000000000000, -0.875000000000000, -0.625000000000000, 0.420407272132140],
[-0.125000000000000, -0.625000000000000, -0.875000000000000, 0.420407272132140],
[ 0.125000000000000, -0.875000000000000, -0.875000000000000, 0.420407272132140],
[-0.875000000000000, -0.875000000000000, -0.125000000000000, 0.420407272132140],
[-0.875000000000000, -0.625000000000000, -0.375000000000000, 0.420407272132140],
[-0.875000000000000, -0.375000000000000, -0.625000000000000, 0.420407272132140],
[-0.875000000000000, -0.125000000000000, -0.875000000000000, 0.420407272132140],
[-0.625000000000000, -0.875000000000000, -0.375000000000000, 0.420407272132140],
[-0.625000000000000, -0.625000000000000, -0.625000000000000, 0.420407272132140],
[-0.625000000000000, -0.375000000000000, -0.875000000000000, 0.420407272132140],
[-0.375000000000000, -0.875000000000000, -0.625000000000000, 0.420407272132140],
[-0.375000000000000, -0.625000000000000, -0.875000000000000, 0.420407272132140],
[-0.125000000000000, -0.875000000000000, -0.875000000000000, 0.420407272132140],
[-0.875000000000000, -0.875000000000000, -0.375000000000000, 0.420407272132140],
[-0.875000000000000, -0.625000000000000, -0.625000000000000, 0.420407272132140],
[-0.875000000000000, -0.375000000000000, -0.875000000000000, 0.420407272132140],
[-0.625000000000000, -0.875000000000000, -0.625000000000000, 0.420407272132140],
[-0.625000000000000, -0.625000000000000, -0.875000000000000, 0.420407272132140],
[-0.375000000000000, -0.875000000000000, -0.875000000000000, 0.420407272132140],
[-0.875000000000000, -0.875000000000000, -0.625000000000000, 0.420407272132140],
[-0.875000000000000, -0.625000000000000, -0.875000000000000, 0.420407272132140],
[-0.625000000000000, -0.875000000000000, -0.875000000000000, 0.420407272132140],
[-0.875000000000000, -0.875000000000000, -0.875000000000000, 0.420407272132140],
[-0.857142857142857, -0.857142857142857, 0.571428571428571, -1.185481802234117],
[-0.857142857142857, -0.571428571428571, 0.285714285714286, -1.185481802234117],
[-0.857142857142857, -0.285714285714286, 0.000000000000000, -1.185481802234117],
[-0.857142857142857, 0.000000000000000, -0.285714285714286, -1.185481802234117],
[-0.857142857142857, 0.285714285714286, -0.571428571428571, -1.185481802234117],
[-0.857142857142857, 0.571428571428571, -0.857142857142857, -1.185481802234117],
[-0.571428571428571, -0.857142857142857, 0.285714285714286, -1.185481802234117],
[-0.571428571428571, -0.571428571428571, 0.000000000000000, -1.185481802234117],
[-0.571428571428571, -0.285714285714286, -0.285714285714286, -1.185481802234117],
[-0.571428571428571, 0.000000000000000, -0.571428571428571, -1.185481802234117],
[-0.571428571428571, 0.285714285714286, -0.857142857142857, -1.185481802234117],
[-0.285714285714286, -0.857142857142857, 0.000000000000000, -1.185481802234117],
[-0.285714285714286, -0.571428571428571, -0.285714285714286, -1.185481802234117],
[-0.285714285714286, -0.285714285714286, -0.571428571428571, -1.185481802234117],
[-0.285714285714286, 0.000000000000000, -0.857142857142857, -1.185481802234117],
[ 0.000000000000000, -0.857142857142857, -0.285714285714286, -1.185481802234117],
[ 0.000000000000000, -0.571428571428571, -0.571428571428571, -1.185481802234117],
[ 0.000000000000000, -0.285714285714286, -0.857142857142857, -1.185481802234117],
[ 0.285714285714286, -0.857142857142857, -0.571428571428571, -1.185481802234117],
[ 0.285714285714286, -0.571428571428571, -0.857142857142857, -1.185481802234117],
[ 0.571428571428571, -0.857142857142857, -0.857142857142857, -1.185481802234117],
[-0.857142857142857, -0.857142857142857, 0.285714285714286, -1.185481802234117],
[-0.857142857142857, -0.571428571428571, 0.000000000000000, -1.185481802234117],
[-0.857142857142857, -0.285714285714286, -0.285714285714286, -1.185481802234117],
[-0.857142857142857, 0.000000000000000, -0.571428571428571, -1.185481802234117],
[-0.857142857142857, 0.285714285714286, -0.857142857142857, -1.185481802234117],
[-0.571428571428571, -0.857142857142857, 0.000000000000000, -1.185481802234117],
[-0.571428571428571, -0.571428571428571, -0.285714285714286, -1.185481802234117],
[-0.571428571428571, -0.285714285714286, -0.571428571428571, -1.185481802234117],
[-0.571428571428571, 0.000000000000000, -0.857142857142857, -1.185481802234117],
[-0.285714285714286, -0.857142857142857, -0.285714285714286, -1.185481802234117],
[-0.285714285714286, -0.571428571428571, -0.571428571428571, -1.185481802234117],
[-0.285714285714286, -0.285714285714286, -0.857142857142857, -1.185481802234117],
[ 0.000000000000000, -0.857142857142857, -0.571428571428571, -1.185481802234117],
[ 0.000000000000000, -0.571428571428571, -0.857142857142857, -1.185481802234117],
[ 0.285714285714286, -0.857142857142857, -0.857142857142857, -1.185481802234117],
[-0.857142857142857, -0.857142857142857, 0.000000000000000, -1.185481802234117],
[-0.857142857142857, -0.571428571428571, -0.285714285714286, -1.185481802234117],
[-0.857142857142857, -0.285714285714286, -0.571428571428571, -1.185481802234117],
[-0.857142857142857, 0.000000000000000, -0.857142857142857, -1.185481802234117],
[-0.571428571428571, -0.857142857142857, -0.285714285714286, -1.185481802234117],
[-0.571428571428571, -0.571428571428571, -0.571428571428571, -1.185481802234117],
[-0.571428571428571, -0.285714285714286, -0.857142857142857, -1.185481802234117],
[-0.285714285714286, -0.857142857142857, -0.571428571428571, -1.185481802234117],
[-0.285714285714286, -0.571428571428571, -0.857142857142857, -1.185481802234117],
[ 0.000000000000000, -0.857142857142857, -0.857142857142857, -1.185481802234117],
[-0.857142857142857, -0.857142857142857, -0.285714285714286, -1.185481802234117],
[-0.857142857142857, -0.571428571428571, -0.571428571428571, -1.185481802234117],
[-0.857142857142857, -0.285714285714286, -0.857142857142857, -1.185481802234117],
[-0.571428571428571, -0.857142857142857, -0.571428571428571, -1.185481802234117],
[-0.571428571428571, -0.571428571428571, -0.857142857142857, -1.185481802234117],
[-0.285714285714286, -0.857142857142857, -0.857142857142857, -1.185481802234117],
[-0.857142857142857, -0.857142857142857, -0.571428571428571, -1.185481802234117],
[-0.857142857142857, -0.571428571428571, -0.857142857142857, -1.185481802234117],
[-0.571428571428571, -0.857142857142857, -0.857142857142857, -1.185481802234117],
[-0.857142857142857, -0.857142857142857, -0.857142857142857, -1.185481802234117],
[-0.833333333333333, -0.833333333333333, 0.500000000000000, 1.198527187098616],
[-0.833333333333333, -0.500000000000000, 0.166666666666667, 1.198527187098616],
[-0.833333333333333, -0.166666666666667, -0.166666666666667, 1.198527187098616],
[-0.833333333333333, 0.166666666666667, -0.500000000000000, 1.198527187098616],
[-0.833333333333333, 0.500000000000000, -0.833333333333333, 1.198527187098616],
[-0.500000000000000, -0.833333333333333, 0.166666666666667, 1.198527187098616],
[-0.500000000000000, -0.500000000000000, -0.166666666666667, 1.198527187098616],
[-0.500000000000000, -0.166666666666667, -0.500000000000000, 1.198527187098616],
[-0.500000000000000, 0.166666666666667, -0.833333333333333, 1.198527187098616],
[-0.166666666666667, -0.833333333333333, -0.166666666666667, 1.198527187098616],
[-0.166666666666667, -0.500000000000000, -0.500000000000000, 1.198527187098616],
[-0.166666666666667, -0.166666666666667, -0.833333333333333, 1.198527187098616],
[ 0.166666666666667, -0.833333333333333, -0.500000000000000, 1.198527187098616],
[ 0.166666666666667, -0.500000000000000, -0.833333333333333, 1.198527187098616],
[ 0.500000000000000, -0.833333333333333, -0.833333333333333, 1.198527187098616],
[-0.833333333333333, -0.833333333333333, 0.166666666666667, 1.198527187098616],
[-0.833333333333333, -0.500000000000000, -0.166666666666667, 1.198527187098616],
[-0.833333333333333, -0.166666666666667, -0.500000000000000, 1.198527187098616],
[-0.833333333333333, 0.166666666666667, -0.833333333333333, 1.198527187098616],
[-0.500000000000000, -0.833333333333333, -0.166666666666667, 1.198527187098616],
[-0.500000000000000, -0.500000000000000, -0.500000000000000, 1.198527187098616],
[-0.500000000000000, -0.166666666666667, -0.833333333333333, 1.198527187098616],
[-0.166666666666667, -0.833333333333333, -0.500000000000000, 1.198527187098616],
[-0.166666666666667, -0.500000000000000, -0.833333333333333, 1.198527187098616],
[ 0.166666666666667, -0.833333333333333, -0.833333333333333, 1.198527187098616],
[-0.833333333333333, -0.833333333333333, -0.166666666666667, 1.198527187098616],
[-0.833333333333333, -0.500000000000000, -0.500000000000000, 1.198527187098616],
[-0.833333333333333, -0.166666666666667, -0.833333333333333, 1.198527187098616],
[-0.500000000000000, -0.833333333333333, -0.500000000000000, 1.198527187098616],
[-0.500000000000000, -0.500000000000000, -0.833333333333333, 1.198527187098616],
[-0.166666666666667, -0.833333333333333, -0.833333333333333, 1.198527187098616],
[-0.833333333333333, -0.833333333333333, -0.500000000000000, 1.198527187098616],
[-0.833333333333333, -0.500000000000000, -0.833333333333333, 1.198527187098616],
[-0.500000000000000, -0.833333333333333, -0.833333333333333, 1.198527187098616],
[-0.833333333333333, -0.833333333333333, -0.833333333333333, 1.198527187098616],
[-0.800000000000000, -0.800000000000000, 0.400000000000000, -0.522755333229870],
[-0.800000000000000, -0.400000000000000, 0.000000000000000, -0.522755333229870],
[-0.800000000000000, 0.000000000000000, -0.400000000000000, -0.522755333229870],
[-0.800000000000000, 0.400000000000000, -0.800000000000000, -0.522755333229870],
[-0.400000000000000, -0.800000000000000, 0.000000000000000, -0.522755333229870],
[-0.400000000000000, -0.400000000000000, -0.400000000000000, -0.522755333229870],
[-0.400000000000000, 0.000000000000000, -0.800000000000000, -0.522755333229870],
[ 0.000000000000000, -0.800000000000000, -0.400000000000000, -0.522755333229870],
[ 0.000000000000000, -0.400000000000000, -0.800000000000000, -0.522755333229870],
[ 0.400000000000000, -0.800000000000000, -0.800000000000000, -0.522755333229870],
[-0.800000000000000, -0.800000000000000, 0.000000000000000, -0.522755333229870],
[-0.800000000000000, -0.400000000000000, -0.400000000000000, -0.522755333229870],
[-0.800000000000000, 0.000000000000000 , -0.800000000000000, -0.522755333229870],
[-0.400000000000000, -0.800000000000000, -0.400000000000000, -0.522755333229870],
[-0.400000000000000, -0.400000000000000, -0.800000000000000, -0.522755333229870],
[ 0.000000000000000, -0.800000000000000 , -0.800000000000000, -0.522755333229870],
[-0.800000000000000, -0.800000000000000, -0.400000000000000, -0.522755333229870],
[-0.800000000000000, -0.400000000000000, -0.800000000000000, -0.522755333229870],
[-0.400000000000000, -0.800000000000000, -0.800000000000000, -0.522755333229870],
[-0.800000000000000, -0.800000000000000, -0.800000000000000, -0.522755333229870],
[-0.750000000000000, -0.750000000000000, 0.250000000000000, 0.093401029697326],
[-0.750000000000000, -0.250000000000000, -0.250000000000000, 0.093401029697326],
[-0.750000000000000, 0.250000000000000, -0.750000000000000, 0.093401029697326],
[-0.250000000000000, -0.750000000000000, -0.250000000000000, 0.093401029697326],
[-0.250000000000000, -0.250000000000000, -0.750000000000000, 0.093401029697326],
[ 0.250000000000000, -0.750000000000000 , -0.750000000000000, 0.093401029697326],
[-0.750000000000000, -0.750000000000000, -0.250000000000000, 0.093401029697326],
[-0.750000000000000, -0.250000000000000, -0.750000000000000, 0.093401029697326],
[-0.250000000000000, -0.750000000000000, -0.750000000000000, 0.093401029697326],
[-0.750000000000000, -0.750000000000000, -0.750000000000000, 0.093401029697326],
[-0.666666666666667, -0.666666666666667, 0.000000000000000, -0.005325487012987],
[-0.666666666666667, 0.000000000000000, -0.666666666666667, -0.005325487012987],
[ 0.000000000000000, -0.666666666666667, -0.666666666666667, -0.005325487012987],
[-0.666666666666667, -0.666666666666667, -0.666666666666667, -0.005325487012987],
[-0.500000000000000, -0.500000000000000, -0.500000000000000, 0.000050166568685]], dtype=np.float)
else:
Print.master(' Orders higher than 6 are not supported by PETGEM')
exit(-1)
# Renormalization (reference tetrahedron is defined from [-1, -1, -1] to lambda1+lambda2+lambda3=-1).
weights = table[:, 3]/8.
points = np.zeros((len(weights), 3), dtype=np.float)
points[:, 0] = (1 + table[:, 1])/2.
points[:, 1] = -(1 + np.sum(table[:, 0:3], axis=1))/2.
points[:, 2] = (1 + table[:,0])/2.
return points, weights
[docs]def compute2DGaussPoints(Nord):
"""Compute 2D gauss points for high-order nédélec elements.
:param int Nord: polynomial order of nedelec basis functions
:return: coordinates of gauss points and its weights
:rtype: ndarray.
"""
if Nord == 0 or Nord == 1:
table = np.array([[-0.333333333333333, -0.333333333333333, 2.000000000000000]], dtype=np.float)
elif Nord == 2:
table = np.array([[-0.666666666666667, -0.666666666666667, 0.666666666666667],
[-0.666666666666667, 0.333333333333333, 0.666666666666667],
[ 0.333333333333333, -0.666666666666667, 0.666666666666667]], dtype=np.float)
elif Nord == 3:
table = np.array([[-0.333333333333333, -0.333333333333333, -1.125000000000000],
[-0.600000000000000, -0.600000000000000, 1.041666666666667],
[-0.600000000000000, 0.200000000000000, 1.041666666666667],
[ 0.200000000000000, -0.600000000000000, 1.041666666666667]], dtype=np.float)
elif Nord == 4:
table = np.array([[-0.108103018168070, -0.108103018168070, 0.446763179356022],
[-0.108103018168070, -0.783793963663860, 0.446763179356022],
[-0.783793963663860, -0.108103018168070, 0.446763179356022],
[-0.816847572980458, -0.816847572980458, 0.219903487310644],
[-0.816847572980458, 0.633695145960918, 0.219903487310644],
[ 0.633695145960918, -0.816847572980458, 0.219903487310644]], dtype=np.float)
elif Nord == 5:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.450000000000000],
[-0.059715871789770, -0.059715871789770, 0.264788305577012],
[-0.059715871789770, -0.880568256420460, 0.264788305577012],
[-0.880568256420460, -0.059715871789770, 0.264788305577012],
[-0.797426985353088, -0.797426985353088, 0.251878361089654],
[-0.797426985353088, 0.594853970706174, 0.251878361089654],
[ 0.594853970706174, -0.797426985353088, 0.251878361089654]], dtype=np.float)
elif Nord == 6:
table = np.array([[-0.501426509658180, -0.501426509658180, 0.233572551452758],
[-0.501426509658180, 0.002853019316358, 0.233572551452758],
[ 0.002853019316358, -0.501426509658180, 0.233572551452758],
[-0.873821971016996, -0.873821971016996, 0.101689812740414],
[-0.873821971016996, 0.747643942033992, 0.101689812740414],
[ 0.747643942033992, -0.873821971016996, 0.101689812740414],
[-0.379295097932432, 0.273004998242798, 0.165702151236748],
[ 0.273004998242798, -0.893709900310366, 0.165702151236748],
[-0.893709900310366, -0.379295097932432, 0.165702151236748],
[-0.379295097932432, -0.893709900310366, 0.165702151236748],
[ 0.273004998242798, -0.379295097932432, 0.165702151236748],
[-0.893709900310366, 0.273004998242798, 0.165702151236748]], dtype=np.float)
elif Nord == 7:
table = np.array([[-0.333333333333333, -0.333333333333333, -0.299140088935364],
[-0.479308067841920, -0.479308067841920, 0.351230514866416],
[-0.479308067841920, -0.041383864316160, 0.351230514866416],
[-0.041383864316160, -0.479308067841920, 0.351230514866416],
[-0.869739794195568, -0.869739794195568, 0.106694471217676],
[-0.869739794195568, 0.739479588391136, 0.106694471217676],
[ 0.739479588391136, -0.869739794195568, 0.106694471217676],
[-0.374269007990252, 0.276888377139620, 0.154227521780514],
[ 0.276888377139620, -0.902619369149368, 0.154227521780514],
[-0.902619369149368, -0.374269007990252, 0.154227521780514],
[-0.374269007990252, -0.902619369149368, 0.154227521780514],
[ 0.276888377139620, -0.374269007990252, 0.154227521780514],
[-0.902619369149368, 0.276888377139620, 0.154227521780514]], dtype=np.float)
elif Nord == 8:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.288631215355574],
[-0.081414823414554, -0.081414823414554, 0.190183268534570],
[-0.081414823414554, -0.837170353170892, 0.190183268534570],
[-0.837170353170892, -0.081414823414554, 0.190183268534570],
[-0.658861384496480, -0.658861384496480, 0.206434741069436],
[-0.658861384496480, 0.317722768992960, 0.206434741069436],
[ 0.317722768992960, -0.658861384496480, 0.206434741069436],
[-0.898905543365938, -0.898905543365938, 0.064916995246396],
[-0.898905543365938, 0.797811086731876, 0.064916995246395],
[ 0.797811086731876, -0.898905543365938, 0.064916995246396],
[-0.473774340730724, 0.456984785910808, 0.054460628348870],
[ 0.456984785910808, -0.983210445180084, 0.054460628348870],
[-0.983210445180084, -0.473774340730724, 0.054460628348870],
[-0.473774340730724, -0.983210445180084, 0.054460628348870],
[ 0.456984785910808, -0.473774340730724, 0.054460628348870],
[-0.983210445180084, 0.456984785910808, 0.054460628348870]], dtype=np.float)
elif Nord == 9:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.194271592565598],
[-0.020634961602524, -0.020634961602524, 0.062669400454278],
[-0.020634961602524, -0.958730076794950, 0.062669400454278],
[-0.958730076794950, -0.020634961602524, 0.062669400454278],
[-0.125820817014126, -0.125820817014126, 0.155655082009548],
[-0.125820817014126, -0.748358365971746, 0.155655082009548],
[-0.748358365971746, -0.125820817014126, 0.155655082009548],
[-0.623592928761934, -0.623592928761934, 0.159295477854420],
[-0.623592928761934, 0.247185857523870, 0.159295477854420],
[ 0.247185857523870, -0.623592928761934, 0.159295477854420],
[-0.910540973211094, -0.910540973211094, 0.051155351317396],
[-0.910540973211094, 0.821081946422190, 0.051155351317396],
[ 0.821081946422190, -0.910540973211094, 0.051155351317396],
[-0.556074021678468, 0.482397197568996, 0.086567078754578],
[ 0.482397197568996, -0.926323175890528, 0.086567078754578],
[-0.926323175890528, -0.556074021678468, 0.086567078754578],
[-0.556074021678468, -0.926323175890528, 0.086567078754578],
[ 0.482397197568996, -0.556074021678468, 0.086567078754578],
[-0.926323175890528, 0.482397197568996, 0.086567078754578]], dtype=np.float)
elif Nord == 10:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.181635980765508],
[-0.028844733232686, -0.028844733232686, 0.073451915512934],
[-0.028844733232686, -0.942310533534630, 0.073451915512934],
[-0.942310533534630, -0.028844733232686, 0.073451915512934],
[-0.781036849029926, -0.781036849029926, 0.090642118871056],
[-0.781036849029926, 0.562073698059852, 0.090642118871056],
[ 0.562073698059852, -0.781036849029926, 0.090642118871056],
[-0.384120322471758, 0.100705883641998, 0.145515833690840],
[ 0.100705883641998, -0.716585561170240, 0.145515833690840],
[-0.716585561170240, -0.384120322471758, 0.145515833690840],
[-0.384120322471758, -0.716585561170240, 0.145515833690840],
[ 0.100705883641998, -0.384120322471758, 0.145515833690840],
[-0.716585561170240, 0.100705883641998, 0.145515833690840],
[-0.506654878720194, 0.456647809194822, 0.056654485062114],
[ 0.456647809194822, -0.949992930474628, 0.056654485062114],
[-0.949992930474628, -0.506654878720194, 0.056654485062114],
[-0.506654878720194, -0.949992930474628, 0.056654485062114],
[ 0.456647809194822, -0.506654878720194, 0.056654485062114],
[-0.949992930474628, 0.456647809194822, 0.056654485062114],
[-0.866393497975600, 0.847311867175000, 0.018843333927466],
[ 0.847311867175000, -0.980918369199402, 0.018843333927466],
[-0.980918369199402, -0.866393497975600, 0.018843333927466],
[-0.866393497975600, -0.980918369199402, 0.018843333927466],
[ 0.847311867175000, -0.866393497975600, 0.018843333927466],
[-0.980918369199402, 0.847311867175000, 0.018843333927466]], dtype=np.float)
elif Nord == 11:
table = np.array([[0.069222096541516, 0.069222096541516, 0.001854012657922],
[ 0.069222096541516, -1.138444193083034, 0.001854012657922],
[-1.138444193083034, 0.069222096541516, 0.001854012657922],
[-0.202061394068290, -0.202061394068290, 0.154299069829626],
[-0.202061394068290, -0.595877211863420, 0.154299069829626],
[-0.595877211863420, -0.202061394068290, 0.154299069829626],
[-0.593380199137436, -0.593380199137436, 0.118645954761548],
[-0.593380199137436, 0.186760398274870, 0.118645954761548],
[ 0.186760398274870, -0.593380199137436, 0.118645954761548],
[-0.761298175434838, -0.761298175434838, 0.072369081006836],
[-0.761298175434838, 0.522596350869674, 0.072369081006836],
[ 0.522596350869674, -0.761298175434838, 0.072369081006836],
[-0.935270103777448, -0.935270103777448, 0.027319462005356],
[-0.935270103777448, 0.870540207554896, 0.027319462005356],
[ 0.870540207554896, -0.935270103777448, 0.027319462005356],
[-0.286758703477414, 0.186402426856426, 0.104674223924408],
[ 0.186402426856426, -0.899643723379010, 0.104674223924408],
[-0.899643723379010, -0.286758703477414, 0.104674223924408],
[-0.286758703477414, -0.899643723379010, 0.104674223924408],
[ 0.186402426856426, -0.286758703477414, 0.104674223924408],
[-0.899643723379010, 0.186402426856426, 0.104674223924408],
[-0.657022039391916, 0.614978006319584, 0.041415319278282],
[ 0.614978006319584, -0.957955966927668, 0.041415319278282],
[-0.957955966927668, -0.657022039391916, 0.041415319278282],
[-0.657022039391916, -0.957955966927668, 0.041415319278282],
[ 0.614978006319584, -0.657022039391916, 0.041415319278282],
[-0.957955966927668, 0.614978006319584, 0.041415319278282]], dtype=np.float)
elif Nord == 12:
table = np.array([[-0.023565220452390, -0.023565220452390, 0.051462132880910],
[-0.023565220452390, -0.952869559095220, 0.051462132880910],
[-0.952869559095220, -0.023565220452390, 0.051462132880910],
[-0.120551215411080, -0.120551215411080, 0.087385089076076],
[-0.120551215411080, -0.758897569177842, 0.087385089076076],
[-0.758897569177842, -0.120551215411080, 0.087385089076076],
[-0.457579229975768, -0.457579229975768, 0.125716448435770],
[-0.457579229975768, -0.084841540048464, 0.125716448435770],
[-0.084841540048464, -0.457579229975768, 0.125716448435770],
[-0.744847708916828, -0.744847708916828, 0.069592225861418],
[-0.744847708916828, 0.489695417833656, 0.069592225861418],
[ 0.489695417833656, -0.744847708916828, 0.069592225861418],
[-0.957365299093580, -0.957365299093580, 0.012332522103118],
[-0.957365299093580, 0.914730598187158, 0.012332522103118],
[ 0.914730598187158, -0.957365299093580, 0.012332522103118],
[-0.448573460628972, 0.217886471559576, 0.080743115532762],
[ 0.217886471559576, -0.769313010930604, 0.080743115532762],
[-0.769313010930604, -0.448573460628972, 0.080743115532762],
[-0.448573460628972, -0.769313010930604, 0.080743115532762],
[ 0.217886471559576, -0.448573460628972, 0.080743115532762],
[-0.769313010930604, 0.217886471559576, 0.080743115532762],
[-0.437348838020120, 0.391672173575606, 0.044713546404606],
[ 0.391672173575606, -0.954323335555486, 0.044713546404606],
[-0.954323335555486, -0.437348838020120, 0.044713546404606],
[-0.437348838020120, -0.954323335555486, 0.044713546404606],
[ 0.391672173575606, -0.437348838020120, 0.044713546404606],
[-0.954323335555486, 0.391672173575606, 0.044713546404606],
[-0.767496168184806, 0.716028067088146, 0.034632462217318],
[ 0.716028067088146, -0.948531898903340, 0.034632462217318],
[-0.948531898903340, -0.767496168184806, 0.034632462217318],
[-0.767496168184806, -0.948531898903340, 0.034632462217318],
[ 0.716028067088146, -0.767496168184806, 0.034632462217318],
[-0.948531898903340, 0.716028067088146, 0.034632462217318]], dtype=np.float)
elif Nord == 13:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.105041846801604],
[-0.009903630120590, -0.009903630120590, 0.022560290418660],
[-0.009903630120590, -0.980192739758818, 0.022560290418660],
[-0.980192739758818, -0.009903630120590, 0.022560290418660],
[-0.062566729780852, -0.062566729780852, 0.062847036724908],
[-0.062566729780852, -0.874866540438296, 0.062847036724908],
[-0.874866540438296, -0.062566729780852, 0.062847036724908],
[-0.170957326397446, -0.170957326397446, 0.094145005008388],
[-0.170957326397446, -0.658085347205106, 0.094145005008388],
[-0.658085347205106, -0.170957326397446, 0.094145005008388],
[-0.541200855914338, -0.541200855914338, 0.094727173072710],
[-0.541200855914338, 0.082401711828674, 0.094727173072710],
[ 0.082401711828674, -0.541200855914338, 0.094727173072710],
[-0.771151009607340, -0.771151009607340, 0.062335058091588],
[-0.771151009607340, 0.542302019214680, 0.062335058091588],
[ 0.542302019214680, -0.771151009607340, 0.062335058091588],
[-0.950377217273082, -0.950377217273082, 0.015951542930148],
[-0.950377217273082, 0.900754434546164, 0.015951542930148],
[ 0.900754434546164, -0.950377217273082, 0.015951542930148],
[-0.462410005882478, 0.272702349123320, 0.073696805457464],
[ 0.272702349123320, -0.810292343240842, 0.073696805457464],
[-0.810292343240842, -0.462410005882478, 0.073696805457464],
[-0.462410005882478, -0.810292343240842, 0.073696805457464],
[ 0.272702349123320, -0.462410005882478, 0.073696805457464],
[-0.810292343240842, 0.272702349123320, 0.073696805457464],
[-0.416539866531424, 0.380338319973810, 0.034802926607644],
[ 0.380338319973810, -0.963798453442386, 0.034802926607644],
[-0.963798453442386, -0.416539866531424, 0.034802926607644],
[-0.416539866531424, -0.963798453442386, 0.034802926607644],
[ 0.380338319973810, -0.416539866531424, 0.034802926607644],
[-0.963798453442386, 0.380338319973810, 0.034802926607644],
[-0.747285229016662, 0.702819075668482, 0.031043573678090],
[ 0.702819075668482, -0.955533846651820, 0.031043573678090],
[-0.955533846651820, -0.747285229016662, 0.031043573678090],
[-0.747285229016662, -0.955533846651820, 0.031043573678090],
[ 0.702819075668482, -0.747285229016662, 0.031043573678090],
[-0.955533846651820, 0.702819075668482, 0.031043573678090]], dtype=np.float)
elif Nord == 14:
table = np.array([[-0.022072179275642, -0.022072179275642, 0.043767162738858],
[-0.022072179275642, -0.955855641448714, 0.043767162738858],
[-0.955855641448714, -0.022072179275642, 0.043767162738858],
[-0.164710561319092, -0.164710561319092, 0.065576707088250],
[-0.164710561319092, -0.670578877361816, 0.065576707088250],
[-0.670578877361816, -0.164710561319092, 0.065576707088250],
[-0.453044943382322, -0.453044943382322, 0.103548209014584],
[-0.453044943382322, -0.093910113235354, 0.103548209014584],
[-0.093910113235354, -0.453044943382322, 0.103548209014584],
[-0.645588935174914, -0.645588935174914, 0.084325177473986],
[-0.645588935174914, 0.291177870349826, 0.084325177473986],
[ 0.291177870349826, -0.645588935174914, 0.084325177473986],
[-0.876400233818254, -0.876400233818254, 0.028867399339554],
[-0.876400233818254, 0.752800467636510, 0.028867399339554],
[ 0.752800467636510, -0.876400233818254, 0.028867399339554],
[-0.961218077502598, -0.961218077502598, 0.009846807204800],
[-0.961218077502598, 0.922436155005196, 0.009846807204800],
[ 0.922436155005196, -0.961218077502598, 0.009846807204800],
[-0.655466624357288, 0.541217109549992, 0.049331506425128],
[ 0.541217109549992, -0.885750485192704, 0.049331506425128],
[-0.885750485192704, -0.655466624357288, 0.049331506425128],
[-0.655466624357288, -0.885750485192704, 0.049331506425128],
[ 0.541217109549992, -0.655466624357288, 0.049331506425128],
[-0.885750485192704, 0.541217109549992, 0.049331506425128],
[-0.326277080407310, 0.140444581693366, 0.077143021574122],
[ 0.140444581693366, -0.814167501286056, 0.077143021574122],
[-0.814167501286056, -0.326277080407310, 0.077143021574122],
[-0.326277080407310, -0.814167501286056, 0.077143021574122],
[ 0.140444581693366, -0.326277080407310, 0.077143021574122],
[-0.814167501286056, 0.140444581693366, 0.077143021574122],
[-0.403254235727484, 0.373960335616176, 0.028872616227068],
[ 0.373960335616176, -0.970706099888692, 0.028872616227068],
[-0.970706099888692, -0.403254235727484, 0.028872616227068],
[-0.403254235727484, -0.970706099888692, 0.028872616227068],
[ 0.373960335616176, -0.403254235727484, 0.028872616227068],
[-0.970706099888692, 0.373960335616176, 0.028872616227068],
[-0.762051004606086, 0.759514342740342, 0.010020457677002],
[ 0.759514342740342, -0.997463338134256, 0.010020457677002],
[-0.997463338134256, -0.762051004606086, 0.010020457677002],
[-0.762051004606086, -0.997463338134256, 0.010020457677002],
[ 0.759514342740342, -0.762051004606086, 0.010020457677002],
[-0.997463338134256, 0.759514342740342, 0.010020457677002]], dtype=np.float)
elif Nord == 15:
table = np.array([[0.013945833716486, 0.013945833716486, 0.003833751285698],
[ 0.013945833716486, -1.027891667432972, 0.003833751285698],
[-1.027891667432972, 0.013945833716486, 0.003833751285698],
[-0.137187291433954, -0.137187291433954, 0.088498054542290],
[-0.137187291433954, -0.725625417132090, 0.088498054542290],
[-0.725625417132090, -0.137187291433954, 0.088498054542290],
[-0.444612710305712, -0.444612710305712, 0.102373097437704],
[-0.444612710305712, -0.110774579388578, 0.102373097437704],
[-0.110774579388578, -0.444612710305712, 0.102373097437704],
[-0.747070217917492, -0.747070217917492, 0.047375471741376],
[-0.747070217917492, 0.494140435834984, 0.047375471741376],
[ 0.494140435834984, -0.747070217917492, 0.047375471741376],
[-0.858383228050628, -0.858383228050628, 0.026579551380042],
[-0.858383228050628, 0.716766456101256, 0.026579551380042],
[ 0.716766456101256, -0.858383228050628, 0.026579551380042],
[-0.962069659517854, -0.962069659517854, 0.009497833216384],
[-0.962069659517854, 0.924139319035706, 0.009497833216384],
[ 0.924139319035706, -0.962069659517854, 0.009497833216384],
[-0.477377257719826, 0.209908933786582, 0.077100145199186],
[ 0.209908933786582, -0.732531676066758, 0.077100145199186],
[-0.732531676066758, -0.477377257719826, 0.077100145199186],
[-0.477377257719826, -0.732531676066758, 0.077100145199186],
[ 0.209908933786582, -0.477377257719826, 0.077100145199186],
[-0.732531676066758, 0.209908933786582, 0.077100145199186],
[-0.223906465819462, 0.151173111025628, 0.054431628641248],
[ 0.151173111025628, -0.927266645206166, 0.054431628641248],
[-0.927266645206166, -0.223906465819462, 0.054431628641248],
[-0.223906465819462, -0.927266645206166, 0.054431628641248],
[ 0.151173111025628, -0.223906465819462, 0.054431628641248],
[-0.927266645206166, 0.151173111025628, 0.054431628641248],
[-0.428575559900168, 0.448925326153310, 0.004364154733594],
[ 0.448925326153310, -1.020349766253142, 0.004364154733594],
[-1.020349766253142, -0.428575559900168, 0.004364154733594],
[-0.428575559900168, -1.020349766253142, 0.004364154733594],
[ 0.448925326153310, -0.428575559900168, 0.004364154733594],
[-1.020349766253142, 0.448925326153310, 0.004364154733594],
[-0.568800671855432, 0.495112932103676, 0.043010639695462],
[ 0.495112932103676, -0.926312260248244, 0.043010639695462],
[-0.926312260248244, -0.568800671855432, 0.043010639695462],
[-0.568800671855432, -0.926312260248244, 0.043010639695462],
[ 0.495112932103676, -0.568800671855432, 0.043010639695462],
[-0.926312260248244, 0.495112932103676, 0.043010639695462],
[-0.792848766847228, 0.767929148184832, 0.015347885262098],
[ 0.767929148184832, -0.975080381337602, 0.015347885262098],
[-0.975080381337602, -0.792848766847228, 0.015347885262098],
[-0.792848766847228, -0.975080381337602, 0.015347885262098],
[ 0.767929148184832, -0.792848766847228, 0.015347885262098],
[-0.975080381337602, 0.767929148184832, 0.015347885262098]], dtype=np.float)
elif Nord == 16:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.093751394855284],
[-0.005238916103124, -0.005238916103124, 0.012811757157170],
[-0.005238916103124, -0.989522167793754, 0.012811757157170],
[-0.989522167793754, -0.005238916103124, 0.012811757157170],
[-0.173061122901296, -0.173061122901296, 0.083420593478774],
[-0.173061122901296, -0.653877754197410, 0.083420593478774],
[-0.653877754197410, -0.173061122901296, 0.083420593478774],
[-0.059082801866018, -0.059082801866018, 0.053782968500128],
[-0.059082801866018, -0.881834396267966, 0.053782968500128],
[-0.881834396267966, -0.059082801866018, 0.053782968500128],
[-0.518892500060958, -0.518892500060958, 0.084265045523300],
[-0.518892500060958, 0.037785000121916, 0.084265045523300],
[ 0.037785000121916, -0.518892500060958, 0.084265045523300],
[-0.704068411554854, -0.704068411554854, 0.060000533685546],
[-0.704068411554854, 0.408136823109708, 0.060000533685546],
[ 0.408136823109708, -0.704068411554854, 0.060000533685546],
[-0.849069624685052, -0.849069624685052, 0.028400197850048],
[-0.849069624685052, 0.698139249370104, 0.028400197850048],
[ 0.698139249370104, -0.849069624685052, 0.028400197850048],
[-0.966807194753950, -0.966807194753950, 0.007164924702546],
[-0.966807194753950, 0.933614389507900, 0.007164924702546],
[ 0.933614389507900, -0.966807194753950, 0.007164924702546],
[-0.406888806840226, 0.199737422349722, 0.065546294921254],
[ 0.199737422349722, -0.792848615509496, 0.065546294921254],
[-0.792848615509496, -0.406888806840226, 0.065546294921254],
[-0.406888806840226, -0.792848615509496, 0.065546294921254],
[ 0.199737422349722, -0.406888806840226, 0.065546294921254],
[-0.792848615509496, 0.199737422349722, 0.065546294921254],
[-0.324553873193842, 0.284387049883010, 0.030596612496882],
[ 0.284387049883010, -0.959833176689168, 0.030596612496882],
[-0.959833176689168, -0.324553873193842, 0.030596612496882],
[-0.324553873193842, -0.959833176689168, 0.030596612496882],
[ 0.284387049883010, -0.324553873193842, 0.030596612496882],
[-0.959833176689168, 0.284387049883010, 0.030596612496882],
[-0.590503436714376, 0.599185441942654, 0.004772488385678],
[ 0.599185441942654, -1.008682005228278, 0.004772488385678],
[-1.008682005228278, -0.590503436714376, 0.004772488385678],
[-0.590503436714376, -1.008682005228278, 0.004772488385678],
[ 0.599185441942654, -0.590503436714376, 0.004772488385678],
[-1.008682005228278, 0.599185441942654, 0.004772488385678],
[-0.621283015738754, 0.537399442802736, 0.038169585511798],
[ 0.537399442802736, -0.916116427063980, 0.038169585511798],
[-0.916116427063980, -0.621283015738754, 0.038169585511798],
[-0.621283015738754, -0.916116427063980, 0.038169585511798],
[ 0.537399442802736, -0.621283015738754, 0.038169585511798],
[-0.916116427063980, 0.537399442802736, 0.038169585511798],
[-0.829432768634686, 0.800798128173322, 0.013700109093084],
[ 0.800798128173322, -0.971365359538638, 0.013700109093084],
[-0.971365359538638, -0.829432768634686, 0.013700109093084],
[-0.829432768634686, -0.971365359538638, 0.013700109093084],
[ 0.800798128173322, -0.829432768634686, 0.013700109093084],
[-0.971365359538638, 0.800798128173322, 0.013700109093084]], dtype=np.float)
elif Nord == 17:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.066874398581606],
[-0.005658918886452, -0.005658918886452, 0.010186830881014],
[-0.005658918886452, -0.988682162227096, 0.010186830881014],
[-0.988682162227096, -0.005658918886452, 0.010186830881014],
[-0.035647354750750, -0.035647354750750, 0.029341729055276],
[-0.035647354750750, -0.928705290498498, 0.029341729055276],
[-0.928705290498498, -0.035647354750750, 0.029341729055276],
[-0.099520061958436, -0.099520061958436, 0.048701756707344],
[-0.099520061958436, -0.800959876083126, 0.048701756707344],
[-0.800959876083126, -0.099520061958436, 0.048701756707344],
[-0.199467521245206, -0.199467521245206, 0.062215101737938],
[-0.199467521245206, -0.601064957509588, 0.062215101737938],
[-0.601064957509588, -0.199467521245206, 0.062215101737938],
[-0.495717464058094, -0.495717464058094, 0.062514222437240],
[-0.495717464058094, -0.008565071883810, 0.062514222437240],
[-0.008565071883810, -0.495717464058094, 0.062514222437240],
[-0.675905990683078, -0.675905990683078, 0.049631308679330],
[-0.675905990683078, 0.351811981366154, 0.049631308679330],
[ 0.351811981366154, -0.675905990683078, 0.049631308679330],
[-0.848248235478508, -0.848248235478508, 0.028112146141114],
[-0.848248235478508, 0.696496470957016, 0.028112146141114],
[ 0.696496470957016, -0.848248235478508, 0.028112146141114],
[-0.968690546064356, -0.968690546064356, 0.006389352347558],
[-0.968690546064356, 0.937381092128712, 0.006389352347558],
[ 0.937381092128712, -0.968690546064356, 0.006389352347558],
[-0.331360265272684, 0.310986407618846, 0.016239310637986],
[ 0.310986407618846, -0.979626142346162, 0.016239310637986],
[-0.979626142346162, -0.331360265272684, 0.016239310637986],
[-0.331360265272684, -0.979626142346162, 0.016239310637986],
[ 0.310986407618846, -0.331360265272684, 0.016239310637986],
[-0.979626142346162, 0.310986407618846, 0.016239310637986],
[-0.415556924406112, 0.144675181064040, 0.053611484566326],
[ 0.144675181064040, -0.729118256657928, 0.053611484566326],
[-0.729118256657928, -0.415556924406112, 0.053611484566326],
[-0.415556924406112, -0.729118256657928, 0.053611484566326],
[ 0.144675181064040, -0.415556924406112, 0.053611484566326],
[-0.729118256657928, 0.144675181064040, 0.053611484566326],
[-0.360850229153620, 0.252002380572456, 0.036919986421644],
[ 0.252002380572456, -0.891152151418834, 0.036919986421644],
[-0.891152151418834, -0.360850229153620, 0.036919986421644],
[-0.360850229153620, -0.891152151418834, 0.036919986421644],
[ 0.252002380572456, -0.360850229153620, 0.036919986421644],
[-0.891152151418834, 0.252002380572456, 0.036919986421644],
[-0.618591551615416, 0.592854429948142, 0.016953737068656],
[ 0.592854429948142, -0.974262878332726, 0.016953737068656],
[-0.974262878332726, -0.618591551615416, 0.016953737068656],
[-0.618591551615416, -0.974262878332726, 0.016953737068656],
[ 0.592854429948142, -0.618591551615416, 0.016953737068656],
[-0.974262878332726, 0.592854429948142, 0.016953737068656],
[-0.639033576702508, 0.504702011875458, 0.036585593540050],
[ 0.504702011875458, -0.865668435172952, 0.036585593540050],
[-0.865668435172952, -0.639033576702508, 0.036585593540050],
[-0.639033576702508, -0.865668435172952, 0.036585593540050],
[ 0.504702011875458, -0.639033576702508, 0.036585593540050],
[-0.865668435172952, 0.504702011875458, 0.036585593540050],
[-0.838577372640872, 0.809251008191216, 0.013331264008330],
[ 0.809251008191216, -0.970673635550344, 0.013331264008330],
[-0.970673635550344, -0.838577372640872, 0.013331264008330],
[-0.838577372640872, -0.970673635550344, 0.013331264008330],
[ 0.809251008191216, -0.838577372640872, 0.013331264008330],
[-0.970673635550344, 0.809251008191216, 0.013331264008330]], dtype=np.float)
elif Nord == 18:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.061619879875294],
[-0.013310382738158, -0.013310382738158, 0.018144873358808],
[-0.013310382738158, -0.973379234523686, 0.018144873358808],
[-0.973379234523686, -0.013310382738158, 0.018144873358808],
[-0.061578811516086, -0.061578811516086, 0.037522633879188],
[-0.061578811516086, -0.876842376967828, 0.037522633879188],
[-0.876842376967828, -0.061578811516086, 0.037522633879188],
[-0.127437208225988, -0.127437208225988, 0.038882195970954],
[-0.127437208225988, -0.745125583548022, 0.038882195970954],
[-0.745125583548022, -0.127437208225988, 0.038882195970954],
[-0.210307658653168, -0.210307658653168, 0.055507897221620],
[-0.210307658653168, -0.579384682693664, 0.055507897221620],
[-0.579384682693664, -0.210307658653168, 0.055507897221620],
[-0.500410862393686, -0.500410862393686, 0.064512450702914],
[-0.500410862393686, 0.000821724787372, 0.064512450702914],
[ 0.000821724787372, -0.500410862393686, 0.064512450702914],
[-0.677135612512314, -0.677135612512314, 0.050148065233844],
[-0.677135612512314, 0.354271225024630, 0.050148065233844],
[ 0.354271225024630, -0.677135612512314, 0.050148065233844],
[-0.846803545029258, -0.846803545029258, 0.030543855943664],
[-0.846803545029258, 0.693607090058514, 0.030543855943664],
[ 0.693607090058514, -0.846803545029258, 0.030543855943664],
[-0.951495121293100, -0.951495121293100, 0.013587844045926],
[-0.951495121293100, 0.902990242586200, 0.013587844045926],
[ 0.902990242586200, -0.951495121293100, 0.013587844045926],
[-0.913707265566070, -0.913707265566070, -0.004446197459840],
[-0.913707265566070, 0.827414531132142, -0.004446197459840],
[ 0.827414531132142, -0.913707265566070, -0.004446197459840],
[-0.282177010118112, 0.265315937713272, 0.012663828152812],
[ 0.265315937713272, -0.983138927595160, 0.012663828152812],
[-0.983138927595160, -0.282177010118112, 0.012663828152812],
[-0.282177010118112, -0.983138927595160, 0.012663828152812],
[ 0.265315937713272, -0.282177010118112, 0.012663828152812],
[-0.983138927595160, 0.265315937713272, 0.012663828152812],
[-0.411195046496086, 0.148821943021710, 0.054515076098276],
[ 0.148821943021710, -0.737626896525624, 0.054515076098276],
[-0.737626896525624, -0.411195046496086, 0.054515076098276],
[-0.411195046496086, -0.737626896525624, 0.054515076098276],
[ 0.148821943021710, -0.411195046496086, 0.054515076098276],
[-0.737626896525624, 0.148821943021710, 0.054515076098276],
[-0.349964396716372, 0.249558093585024, 0.035353571298930],
[ 0.249558093585024, -0.899593696868650, 0.035353571298930],
[-0.899593696868650, -0.349964396716372, 0.035353571298930],
[-0.349964396716372, -0.899593696868650, 0.035353571298930],
[ 0.249558093585024, -0.349964396716372, 0.035353571298930],
[-0.899593696868650, 0.249558093585024, 0.035353571298930],
[-0.630524880667908, 0.497866353046074, 0.036758969276140],
[ 0.497866353046074, -0.867341472378168, 0.036758969276140],
[-0.867341472378168, -0.630524880667908, 0.036758969276140],
[-0.630524880667908, -0.867341472378168, 0.036758969276140],
[ 0.497866353046074, -0.630524880667908, 0.036758969276140],
[-0.867341472378168, 0.497866353046074, 0.036758969276140],
[-0.562406399973358, 0.538414010840886, 0.016209465616384],
[ 0.538414010840886, -0.976007610867528, 0.016209465616384],
[-0.976007610867528, -0.562406399973358, 0.016209465616384],
[-0.562406399973358, -0.976007610867528, 0.016209465616384],
[ 0.538414010840886, -0.562406399973358, 0.016209465616384],
[-0.976007610867528, 0.538414010840886, 0.016209465616384],
[-0.797640805727184, 0.767924604546934, 0.015268258141450],
[ 0.767924604546934, -0.970283798819750, 0.015268258141450],
[-0.970283798819750, -0.797640805727184, 0.015268258141450],
[-0.797640805727184, -0.970283798819750, 0.015268258141450],
[ 0.767924604546934, -0.797640805727184, 0.015268258141450],
[-0.970283798819750, 0.767924604546934, 0.015268258141450],
[-0.958250489434828, 1.028694520010726, 0.000092375321588],
[ 1.028694520010726, -1.070444030575898, 0.000092375321588],
[-1.070444030575898, -0.958250489434828, 0.000092375321588],
[-0.958250489434828, -1.070444030575898, 0.000092375321588],
[ 1.028694520010726, -0.958250489434828, 0.000092375321588],
[-1.070444030575898, 1.028694520010726, 0.000092375321588]], dtype=np.float)
elif Nord == 19:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.065812662777838],
[-0.020780025853988, -0.020780025853988, 0.020661463782544],
[-0.020780025853988, -0.958439948292026, 0.020661463782544],
[-0.958439948292026, -0.020780025853988, 0.020661463782544],
[-0.090926214604214, -0.090926214604214, 0.044774494526032],
[-0.090926214604214, -0.818147570791570, 0.044774494526032],
[-0.818147570791570, -0.090926214604214, 0.044774494526032],
[-0.197166638701138, -0.197166638701138, 0.060532251738936],
[-0.197166638701138, -0.605666722597724, 0.060532251738936],
[-0.605666722597724, -0.197166638701138, 0.060532251738936],
[-0.488896691193804, -0.488896691193804, 0.060981935604396],
[-0.488896691193804, -0.022206617612390, 0.060981935604396],
[-0.022206617612390, -0.488896691193804, 0.060981935604396],
[-0.645844115695740, -0.645844115695740, 0.048318425483282],
[-0.645844115695740, 0.291688231391482, 0.048318425483282],
[ 0.291688231391482, -0.645844115695740, 0.048318425483282],
[-0.779877893544096, -0.779877893544096, 0.032101607173602],
[-0.779877893544096, 0.559755787088192, 0.032101607173602],
[ 0.559755787088192, -0.779877893544096, 0.032101607173602],
[-0.888942751496320, -0.888942751496320, 0.016169160523568],
[-0.888942751496320, 0.777885502992642, 0.016169160523568],
[ 0.777885502992642, -0.888942751496320, 0.016169160523568],
[-0.974756272445542, -0.974756272445542, 0.004158724054970],
[-0.974756272445542, 0.949512544891086, 0.004158724054970],
[ 0.949512544891086, -0.974756272445542, 0.004158724054970],
[-0.208490425286114, 0.201267589589290, 0.007769753809962],
[ 0.201267589589290, -0.992777164303176, 0.007769753809962],
[-0.992777164303176, -0.208490425286114, 0.007769753809962],
[-0.208490425286114, -0.992777164303176, 0.007769753809962],
[ 0.201267589589290, -0.208490425286114, 0.007769753809962],
[-0.992777164303176, 0.201267589589286, 0.007769753809962],
[-0.384140032239128, 0.115206523177568, 0.051148321224044],
[ 0.115206523177568, -0.731066490938440, 0.051148321224044],
[-0.731066490938440, -0.384140032239128, 0.051148321224044],
[-0.384140032239128, -0.731066490938440, 0.051148321224044],
[ 0.115206523177568, -0.384140032239128, 0.051148321224044],
[-0.731066490938440, 0.115206523177568, 0.051148321224044],
[-0.470866103186960, 0.441974051634730, 0.017761807146676],
[ 0.441974051634730, -0.971107948447770, 0.017761807146676],
[-0.971107948447770, -0.470866103186960, 0.017761807146676],
[-0.470866103186960, -0.971107948447770, 0.017761807146676],
[ 0.441974051634730, -0.470866103186960, 0.017761807146676],
[-0.971107948447770, 0.441974051634730, 0.017761807146676],
[-0.282921295588098, 0.189054137911742, 0.032249093523462],
[ 0.189054137911742, -0.906132842323644, 0.032249093523462],
[-0.906132842323644, -0.282921295588098, 0.032249093523462],
[-0.282921295588098, -0.906132842323644, 0.032249093523462],
[ 0.189054137911742, -0.282921295588098, 0.032249093523462],
[-0.906132842323644, 0.189054137911742, 0.032249093523462],
[-0.684385188062810, 0.678662947361678, 0.004983883634982],
[ 0.678662947361678, -0.994277759298866, 0.004983883634982],
[-0.994277759298866, -0.684385188062810, 0.004983883634982],
[-0.684385188062810, -0.994277759298866, 0.004983883634982],
[ 0.678662947361678, -0.684385188062810, 0.004983883634982],
[-0.994277759298866, 0.678662947361678, 0.004983883634982],
[-0.849898806048178, 0.402175957852346, 0.036485680237902],
[ 0.402175957852346, -0.552277151804168, 0.036485680237902],
[-0.552277151804168, -0.849898806048178, 0.036485680237902],
[-0.849898806048178, -0.552277151804168, 0.036485680237902],
[ 0.402175957852346, -0.849898806048178, 0.036485680237902],
[-0.552277151804168, 0.402175957852346, 0.036485680237902],
[-0.715156797773234, 0.645862648139714, 0.020517127472398],
[ 0.645862648139714, -0.930705850366480, 0.020517127472398],
[-0.930705850366480, -0.715156797773234, 0.020517127472398],
[-0.715156797773234, -0.930705850366480, 0.020517127472398],
[ 0.645862648139714, -0.715156797773234, 0.020517127472398],
[-0.930705850366480, 0.645862648139714, 0.020517127472398],
[-0.869010743834124, 0.848688505241568, 0.007599857710604],
[ 0.848688505241568, -0.979677761407444, 0.007599857710604],
[-0.979677761407444, -0.869010743834124, 0.007599857710604],
[-0.869010743834124, -0.979677761407444, 0.007599857710604],
[ 0.848688505241568, -0.869010743834124, 0.007599857710604],
[-0.979677761407444, 0.848688505241568, 0.007599857710604]], dtype=np.float)
elif Nord == 20:
table = np.array([[-0.333333333333333, -0.333333333333333, 0.066114111083248],
[ 0.001900928704400, 0.001900928704400, 0.001734038371326],
[ 0.001900928704400, -1.003801857408800, 0.001734038371326],
[-1.003801857408800, 0.001900928704400, 0.001734038371326],
[-0.023574084130543, -0.023574084130543, 0.023320105432896],
[-0.023574084130543, -0.952851831738914, 0.023320105432896],
[-0.952851831738914, -0.023574084130543, 0.023320105432896],
[-0.089726626099435, -0.089726626099435, 0.045753872712842],
[-0.089726626099435, -0.820546727801130, 0.045753872712842],
[-0.820546727801130, -0.089726626099435, 0.045753872712842],
[-0.196007481363421, -0.196007481363421, 0.060897965347876],
[-0.196007481363421, -0.607985037273158, 0.060897965347876],
[-0.607985037273158, -0.196007481363421, 0.060897965347876],
[-0.488214180481157, -0.488214180481157, 0.061249783450710],
[-0.488214180481157, -0.023571639037686, 0.061249783450710],
[-0.023571639037686, -0.488214180481157, 0.061249783450710],
[-0.647023488009788, -0.647023488009788, 0.048736115353600],
[-0.647023488009788, 0.294046976019576, 0.048736115353600],
[ 0.294046976019576, -0.647023488009788, 0.048736115353600],
[-0.791658289326483, -0.791658289326483, 0.031994864064048],
[-0.791658289326483, 0.583316578652966, 0.031994864064048],
[ 0.583316578652966, -0.791658289326483, 0.031994864064048],
[-0.893862072318140, -0.893862072318140, 0.015396603631204],
[-0.893862072318140, 0.787724144636280, 0.015396603631204],
[ 0.787724144636280, -0.893862072318140, 0.015396603631204],
[-0.916762569607942, -0.916762569607942, -0.001264120994976],
[-0.916762569607942, 0.833525139215884, -0.001264120994976],
[ 0.833525139215884, -0.916762569607942, -0.001264120994976],
[-0.976836157186356, -0.976836157186356, 0.003502268602386],
[-0.976836157186356, 0.953672314372712, 0.003502268602386],
[ 0.953672314372712, -0.976836157186356, 0.003502268602386],
[-0.310288459541998, 0.212805292212320, 0.032931678379152],
[ 0.212805292212320, -0.902516832670322, 0.032931678379152],
[-0.902516832670322, -0.310288459541998, 0.032931678379152],
[-0.310288459541998, -0.902516832670322, 0.032931678379152],
[ 0.212805292212320, -0.310288459541998, 0.032931678379152],
[-0.902516832670322, 0.212805292212320, 0.032931678379152],
[-0.244313460810292, 0.231685228913082, 0.009678067080970],
[ 0.231685228913082, -0.987371768102790, 0.009678067080970],
[-0.987371768102790, -0.244313460810292, 0.009678067080970],
[-0.244313460810292, -0.987371768102790, 0.009678067080970],
[ 0.231685228913082, -0.244313460810292, 0.009678067080970],
[-0.987371768102790, 0.231685228913082, 0.009678067080970],
[-0.386729041875286, 0.118096000780590, 0.051609813069300],
[ 0.118096000780590, -0.731366958905304, 0.051609813069300],
[-0.731366958905304, -0.386729041875286, 0.051609813069300],
[-0.386729041875286, -0.731366958905304, 0.051609813069300],
[ 0.118096000780590, -0.386729041875286, 0.051609813069300],
[-0.731366958905304, 0.118096000780590, 0.051609813069300],
[-0.501161274450516, 0.473213486525732, 0.016942182108882],
[ 0.473213486525732, -0.972052212075216, 0.016942182108882],
[-0.972052212075216, -0.501161274450516, 0.016942182108882],
[-0.501161274450516, -0.972052212075216, 0.016942182108882],
[ 0.473213486525732, -0.501161274450516, 0.016942182108882],
[-0.972052212075216, 0.473213486525732, 0.016942182108882],
[-0.574448550394396, 0.423350284574868, 0.036709828212560],
[ 0.423350284574868, -0.848901734180472, 0.036709828212560],
[-0.848901734180472, -0.574448550394396, 0.036709828212560],
[-0.574448550394396, -0.848901734180472, 0.036709828212560],
[ 0.423350284574868, -0.574448550394396, 0.036709828212560],
[-0.848901734180472, 0.423350284574868, 0.036709828212560],
[-0.706069127893522, 0.722805434309974, 0.001408809355816],
[ 0.722805434309974, -1.016736306416454, 0.001408809355816],
[-1.016736306416454, -0.706069127893522, 0.001408809355816],
[-0.706069127893522, -1.016736306416454, 0.001408809355816],
[ 0.722805434309974, -0.706069127893522, 0.001408809355816],
[-1.016736306416454, 0.722805434309974, 0.001408809355816],
[-0.724546042342154, 0.671173915824726, 0.020225369854924],
[ 0.671173915824726, -0.946627873482572, 0.020225369854924],
[-0.946627873482572, -0.724546042342154, 0.020225369854924],
[-0.724546042342154, -0.946627873482572, 0.020225369854924],
[ 0.671173915824726, -0.724546042342154, 0.020225369854924],
[-0.946627873482572, 0.671173915824726, 0.020225369854924],
[-0.880607781701986, 0.859512343113706, 0.007147818771900],
[ 0.859512343113706, -0.978904561411718, 0.007147818771900],
[-0.978904561411718, -0.880607781701986, 0.007147818771900],
[-0.880607781701986, -0.978904561411718, 0.007147818771900],
[ 0.859512343113706, -0.880607781701986, 0.007147818771900],
[-0.978904561411718, 0.859512343113706, 0.007147818771900]], dtype=np.float)
else:
Print.master(' Orders higher than 20 are not supported for triangle yet')
exit(-1)
# Renormalization (reference triangle)
weights = table[:,2]/4.
points = np.zeros((weights.size,2), dtype=np.float)
points[:, 0] = (1.+table[:,0])/2.
points[:, 1] = -(np.sum(table[:,0:2], axis=1))/2.
return points, weights
[docs]def computeSourceVectorRotation(azimuth, dip):
"""Compute the weigths vector for source rotation in the xyz plane.
:param float azimuth: degrees for x-y plane rotation
:param float dip: degrees for x-z plane rotation
:return: weigths for source rotation
:rtype: ndarray.
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Define reference unit vector (x-directed pointing to East)
base_vector = np.array([1,0,0], dtype=np.float)
# ---------------------------------------------------------------
# Compute vector for source rotation
# ---------------------------------------------------------------
# Convert degrees to radians for rotation
alpha = azimuth*np.pi/180. # x-y plane
beta = dip*np.pi/180. # x-z plane
tetha = 0*np.pi/180. # y-z plane
# Define rotation matrices for each plane
# x-y plane
M1 = np.array([[np.cos(alpha), -np.sin(alpha), 0.],
[np.sin(alpha), np.cos(alpha), 0.],
[ 0., 0., 1.]], dtype=np.float)
# x-z plane
M2 = np.array([[np.cos(beta), 0., -np.sin(beta)],
[ 0., 1., 0. ],
[np.sin(beta), 0., np.cos(beta)]], dtype=np.float)
# y-z plane
M3 = np.array([[1., 0., 0],
[0., np.cos(tetha), -np.sin(tetha)],
[0., np.sin(tetha), np.cos(tetha)]], dtype=np.float)
# Apply rotation
rotSourceVector = np.dot(np.matmul(np.matmul(M1,M2),M3), base_vector)
return rotSourceVector
[docs]def tetrahedronXYZToXiEtaZeta(eleNodes, points):
"""Compute the reference tetrahedron coordinates from xyz global tetrahedron coordinates.
:param ndarray eleNodes: spatial coordinates of the nodes with dimensions = (4,3)
:param ndarray points: xyz points coordinates to be transformed
:return: xietazeta points coordinates
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Get number of points
if points.ndim == 1:
num_points = 1
XiEtaZeta = np.zeros(3, dtype=np.float)
else:
num_points = points.shape[0]
# Allocate
XiEtaZeta = np.zeros((num_points, 3), dtype=np.float)
# ---------------------------------------------------------------
# Compute mapping
# ---------------------------------------------------------------
# Loop over points
if num_points == 1:
pointX = points[0]
pointY = points[1]
pointZ = points[2]
J = eleNodes[0,2] * ( eleNodes[1,0] * (eleNodes[3,1] - eleNodes[2,1]) \
+ eleNodes[2,0] * (eleNodes[1,1] - eleNodes[3,1]) \
+ eleNodes[3,0] * (eleNodes[2,1] - eleNodes[1,1]) ) \
+ eleNodes[1,2] * ( eleNodes[0,0] * (eleNodes[2,1] - eleNodes[3,1]) \
+ eleNodes[2,0] * (eleNodes[3,1] - eleNodes[0,1]) \
+ eleNodes[3,0] * (eleNodes[0,1] - eleNodes[2,1]) ) \
+ eleNodes[2,2] * ( eleNodes[0,0] * (eleNodes[3,1] - eleNodes[1,1]) \
+ eleNodes[1,0] * (eleNodes[0,1] - eleNodes[3,1]) \
+ eleNodes[3,0] * (eleNodes[1,1] - eleNodes[0,1]) ) \
+ eleNodes[3,2] * ( eleNodes[0,0] * (eleNodes[1,1] - eleNodes[2,1]) \
+ eleNodes[1,0] * (eleNodes[2,1] - eleNodes[0,1]) \
+ eleNodes[2,0] * (eleNodes[0,1] - eleNodes[1,1]) )
xi = ( eleNodes[3,2] * (eleNodes[2,1] - eleNodes[0,1]) + eleNodes[0,2] * (eleNodes[3,1] - eleNodes[2,1]) \
+ eleNodes[2,2] * (eleNodes[0,1] - eleNodes[3,1]) ) / J * pointX + \
( eleNodes[0,2] * (eleNodes[2,0] - eleNodes[3,0]) + eleNodes[3,2] * (eleNodes[0,0] - eleNodes[2,0]) \
+ eleNodes[2,2] * (eleNodes[3,0] - eleNodes[0,0]) ) / J * pointY + \
( eleNodes[0,0] * (eleNodes[2,1] - eleNodes[3,1]) + eleNodes[3,0] * (eleNodes[0,1] - eleNodes[2,1]) \
+ eleNodes[2,0] * (eleNodes[3,1] - eleNodes[0,1]) ) / J * pointZ + \
( eleNodes[2,2] * (eleNodes[0,0] * eleNodes[3,1] - eleNodes[3,0] * eleNodes[0,1]) \
+ eleNodes[0,2] * (eleNodes[3,0] * eleNodes[2,1] - eleNodes[2,0] * eleNodes[3,1]) \
+ eleNodes[3,2] * (eleNodes[2,0] * eleNodes[0,1] - eleNodes[0,0] * eleNodes[2,1]) ) / J
eta = ( eleNodes[1,2] * (eleNodes[3,1] - eleNodes[0,1]) + eleNodes[3,2] * (eleNodes[0,1] - eleNodes[1,1]) \
+ eleNodes[0,2] * (eleNodes[1,1] - eleNodes[3,1]) ) / J * pointX + \
( eleNodes[1,2] * (eleNodes[0,0] - eleNodes[3,0]) + eleNodes[0,2] * (eleNodes[3,0] - eleNodes[1,0]) \
+ eleNodes[3,2] * (eleNodes[1,0] - eleNodes[0,0]) ) / J * pointY + \
( eleNodes[1,0] * (eleNodes[0,1] - eleNodes[3,1]) + eleNodes[0,0] * (eleNodes[3,1] - eleNodes[1,1]) \
+ eleNodes[3,0] * (eleNodes[1,1] - eleNodes[0,1]) ) / J * pointZ + \
( eleNodes[1,2] * (eleNodes[3,0] * eleNodes[0,1] - eleNodes[0,0] * eleNodes[3,1]) \
+ eleNodes[0,2] * (eleNodes[1,0] * eleNodes[3,1] - eleNodes[3,0] * eleNodes[1,1]) \
+ eleNodes[3,2] * (eleNodes[0,0] * eleNodes[1,1] - eleNodes[1,0] * eleNodes[0,1]) ) / J
zeta = ( eleNodes[0,2] * (eleNodes[2,1] - eleNodes[1,1]) + eleNodes[2,2] * (eleNodes[1,1] - eleNodes[0,1]) \
+ eleNodes[1,2] * (eleNodes[0,1] - eleNodes[2,1]) ) / J * pointX + \
( eleNodes[2,2] * (eleNodes[0,0] - eleNodes[1,0]) + eleNodes[0,2] * (eleNodes[1,0] - eleNodes[2,0]) \
+ eleNodes[1,2] * (eleNodes[2,0] - eleNodes[0,0]) ) / J * pointY + \
( eleNodes[0,0] * (eleNodes[1,1] - eleNodes[2,1]) + eleNodes[1,0] * (eleNodes[2,1] - eleNodes[0,1]) \
+ eleNodes[2,0] * (eleNodes[0,1] - eleNodes[1,1]) ) / J * pointZ + \
( eleNodes[0,2] * ( eleNodes[3,0] * (eleNodes[1,1] - eleNodes[2,1]) \
+ eleNodes[3,1] * (eleNodes[2,0] - eleNodes[1,0]) ) \
+ eleNodes[2,2] * ( eleNodes[3,0] * (eleNodes[0,1] - eleNodes[1,1]) \
+ eleNodes[3,1] * (eleNodes[1,0] - eleNodes[0,0]) ) \
+ eleNodes[1,2] * ( eleNodes[3,0] * (eleNodes[2,1] - eleNodes[0,1]) \
+ eleNodes[3,1] * (eleNodes[0,0] - eleNodes[2,0]) ) \
+ eleNodes[3,2] * ( eleNodes[1,0] * (eleNodes[0,1] - eleNodes[2,1]) \
+ eleNodes[0,0] * (eleNodes[2,1] - eleNodes[1,1]) \
+ eleNodes[2,0] * (eleNodes[1,1] - eleNodes[0,1]) ) + J ) / J
XiEtaZeta[0] = xi
XiEtaZeta[1] = eta
XiEtaZeta[2] = zeta
else:
for i in np.arange(num_points):
pointX = points[i,0]
pointY = points[i,1]
pointZ = points[i,2]
J = eleNodes[0,2] * ( eleNodes[1,0] * (eleNodes[3,1] - eleNodes[2,1]) \
+ eleNodes[2,0] * (eleNodes[1,1] - eleNodes[3,1]) \
+ eleNodes[3,0] * (eleNodes[2,1] - eleNodes[1,1]) ) \
+ eleNodes[1,2] * ( eleNodes[0,0] * (eleNodes[2,1] - eleNodes[3,1]) \
+ eleNodes[2,0] * (eleNodes[3,1] - eleNodes[0,1]) \
+ eleNodes[3,0] * (eleNodes[0,1] - eleNodes[2,1]) ) \
+ eleNodes[2,2] * ( eleNodes[0,0] * (eleNodes[3,1] - eleNodes[1,1]) \
+ eleNodes[1,0] * (eleNodes[0,1] - eleNodes[3,1]) \
+ eleNodes[3,0] * (eleNodes[1,1] - eleNodes[0,1]) ) \
+ eleNodes[3,2] * ( eleNodes[0,0] * (eleNodes[1,1] - eleNodes[2,1]) \
+ eleNodes[1,0] * (eleNodes[2,1] - eleNodes[0,1]) \
+ eleNodes[2,0] * (eleNodes[0,1] - eleNodes[1,1]) )
xi = ( eleNodes[3,2] * (eleNodes[2,1] - eleNodes[0,1]) + eleNodes[0,2] * (eleNodes[3,1] - eleNodes[2,1]) \
+ eleNodes[2,2] * (eleNodes[0,1] - eleNodes[3,1]) ) / J * pointX + \
( eleNodes[0,2] * (eleNodes[2,0] - eleNodes[3,0]) + eleNodes[3,2] * (eleNodes[0,0] - eleNodes[2,0]) \
+ eleNodes[2,2] * (eleNodes[3,0] - eleNodes[0,0]) ) / J * pointY + \
( eleNodes[0,0] * (eleNodes[2,1] - eleNodes[3,1]) + eleNodes[3,0] * (eleNodes[0,1] - eleNodes[2,1]) \
+ eleNodes[2,0] * (eleNodes[3,1] - eleNodes[0,1]) ) / J * pointZ + \
( eleNodes[2,2] * (eleNodes[0,0] * eleNodes[3,1] - eleNodes[3,0] * eleNodes[0,1]) \
+ eleNodes[0,2] * (eleNodes[3,0] * eleNodes[2,1] - eleNodes[2,0] * eleNodes[3,1]) \
+ eleNodes[3,2] * (eleNodes[2,0] * eleNodes[0,1] - eleNodes[0,0] * eleNodes[2,1]) ) / J
eta = ( eleNodes[1,2] * (eleNodes[3,1] - eleNodes[0,1]) + eleNodes[3,2] * (eleNodes[0,1] - eleNodes[1,1]) \
+ eleNodes[0,2] * (eleNodes[1,1] - eleNodes[3,1]) ) / J * pointX + \
( eleNodes[1,2] * (eleNodes[0,0] - eleNodes[3,0]) + eleNodes[0,2] * (eleNodes[3,0] - eleNodes[1,0]) \
+ eleNodes[3,2] * (eleNodes[1,0] - eleNodes[0,0]) ) / J * pointY + \
( eleNodes[1,0] * (eleNodes[0,1] - eleNodes[3,1]) + eleNodes[0,0] * (eleNodes[3,1] - eleNodes[1,1]) \
+ eleNodes[3,0] * (eleNodes[1,1] - eleNodes[0,1]) ) / J * pointZ + \
( eleNodes[1,2] * (eleNodes[3,0] * eleNodes[0,1] - eleNodes[0,0] * eleNodes[3,1]) \
+ eleNodes[0,2] * (eleNodes[1,0] * eleNodes[3,1] - eleNodes[3,0] * eleNodes[1,1]) \
+ eleNodes[3,2] * (eleNodes[0,0] * eleNodes[1,1] - eleNodes[1,0] * eleNodes[0,1]) ) / J
zeta = ( eleNodes[0,2] * (eleNodes[2,1] - eleNodes[1,1]) + eleNodes[2,2] * (eleNodes[1,1] - eleNodes[0,1]) \
+ eleNodes[1,2] * (eleNodes[0,1] - eleNodes[2,1]) ) / J * pointX + \
( eleNodes[2,2] * (eleNodes[0,0] - eleNodes[1,0]) + eleNodes[0,2] * (eleNodes[1,0] - eleNodes[2,0]) \
+ eleNodes[1,2] * (eleNodes[2,0] - eleNodes[0,0]) ) / J * pointY + \
( eleNodes[0,0] * (eleNodes[1,1] - eleNodes[2,1]) + eleNodes[1,0] * (eleNodes[2,1] - eleNodes[0,1]) \
+ eleNodes[2,0] * (eleNodes[0,1] - eleNodes[1,1]) ) / J * pointZ + \
( eleNodes[0,2] * ( eleNodes[3,0] * (eleNodes[1,1] - eleNodes[2,1]) \
+ eleNodes[3,1] * (eleNodes[2,0] - eleNodes[1,0]) ) \
+ eleNodes[2,2] * ( eleNodes[3,0] * (eleNodes[0,1] - eleNodes[1,1]) \
+ eleNodes[3,1] * (eleNodes[1,0] - eleNodes[0,0]) ) \
+ eleNodes[1,2] * ( eleNodes[3,0] * (eleNodes[2,1] - eleNodes[0,1]) \
+ eleNodes[3,1] * (eleNodes[0,0] - eleNodes[2,0]) ) \
+ eleNodes[3,2] * ( eleNodes[1,0] * (eleNodes[0,1] - eleNodes[2,1]) \
+ eleNodes[0,0] * (eleNodes[2,1] - eleNodes[1,1]) \
+ eleNodes[2,0] * (eleNodes[1,1] - eleNodes[0,1]) ) + J ) / J
XiEtaZeta[i,0] = xi
XiEtaZeta[i,1] = eta
XiEtaZeta[i,2] = zeta
return XiEtaZeta
[docs]def computeBasisFunctions(edge_orientation, face_orientation, jacobian, invjacob, Nord, points):
"""Compute the basis function for a given element.
:param ndarray edges_orientation: orientation for edges
:param ndarray faces_orientation: orientation for faces
:param ndarray jacobian: jacobian matrix
:param ndarray invjacob: inverse of jacobian matrix
:param int Nord: polynomial order of nedelec basis functions
:param ndarray points: spatial points at which basis functions will be computed
:return: basis functions and its curl for p-order=Nord
:rtype: ndarray
"""
# ---------------------------------------------------------------
# Initialization
# ---------------------------------------------------------------
# Get number of points
if points.ndim == 1:
num_points = 1
points = points.reshape((1,3))
else:
num_points = points.shape[0]
# Compute number of dofs for element
num_dof_in_element = np.int(Nord*(Nord+2)*(Nord+3)/2)
# Allocate
basis = np.zeros((3, num_dof_in_element, num_points), dtype=np.float)
curl_basis = np.zeros((3, num_dof_in_element, num_points), dtype=np.float)
for i in np.arange(num_points):
# Get point coordinates
X = points[i, :]
# Polynomial order (6 edges, 4 faces, 1 volume)
Nord_vector = np.ones(11, dtype=np.int)*Nord
# Edge orientation (6 edges)
NoriE = edge_orientation
# Face orientation (4 faces)
NoriF = face_orientation
# Compute basis for iPoint
NrdofE, ShapE, CurlE = shape3DETet(X, Nord_vector, NoriE, NoriF)
# Verify consistency of number of dofs for this point
if (NrdofE != num_dof_in_element):
Print.master(' Number of DOFs is not consistent')
exit(-1)
# Niref=Ni in reference element
Niref = ShapE[0:3, 0:NrdofE]
# Ni=Ni in real element
Ni_real = np.matmul(invjacob, Niref)
# Curl_real = curl in real element
Curl_real = np.matmul(np.transpose(jacobian), CurlE[0:3,0:NrdofE]/np.linalg.det(np.transpose(jacobian)))
# Store basis functions and its curl for i
basis[:,:,i] = Ni_real
curl_basis[:,:,i] = Curl_real
return basis, curl_basis
[docs]def getNeumannBCface(face_flag, polarization, ud):
"""Get Neumann boundary condition for boundary face.
:param int face_flag: face flag (side on which face belongs)
:param int polarization: polarization mode (x-mode or y-mode)
:param ndarray ud: magnetic field vector
:return: value of Neumann boundary condition for boundary face
:rtype: ndarray
"""
# Allocate
xzero = np.zeros(ud.shape, dtype=np.complex)
if polarization == 1:
if face_flag == 0:
ex = ud
ey = xzero
ez = xzero
elif face_flag == 1:
ex = xzero
ey = xzero
ez = -ud
elif face_flag == 2:
ex = xzero
ey = xzero
ez = xzero
elif face_flag == 3:
ex = xzero
ey = xzero
ez = ud
elif face_flag == 4:
ex = xzero
ey = xzero
ez = xzero
elif face_flag == 5:
ex = -ud
ey = xzero
ez = xzero
elif polarization == 2:
if face_flag == 0:
ex = xzero
ey = ud
ez = xzero
elif face_flag == 1:
ex = xzero
ey = xzero
ez = xzero
elif face_flag == 2:
ex = xzero
ey = xzero
ez = -ud
elif face_flag == 3:
ex = xzero
ey = xzero
ez = xzero
elif face_flag == 4:
ex = xzero
ey = xzero
ez = ud
elif face_flag == 5:
ex = xzero
ey = -ud
ez = xzero
else:
Print.master(' Polarization mode not supported by PETGEM')
exit(-1)
return ex, ey, ez
[docs]def getNormalVector(faceNumber, invJacobMatrix):
"""This function computes the normal vector for a given tetrahedral face.
:param int faceNumber: local face number
:param int invJacobMatrix: inverse of jacobian matrix
:return: face normal vector
:rtype: ndarray
"""
normalRefVectorArray = np.array([[0., 0., -1.],
[0., -1., 0.],
[1./np.sqrt(3.), 1./np.sqrt(3.), 1./np.sqrt(3.)],
[-1., 0., 0.]], dtype=np.float)
normalRefVector = normalRefVectorArray[faceNumber,:]
normalVector = np.matmul(invJacobMatrix,normalRefVector)
return normalVector
[docs]def get2DJacobDet(coordEle, faceNumber):
"""Compute the determinant of the jacobian for 2D integrals (when 3D basis functions are used)
:param ndarray coordEle: coordinates of the tetrahedron
:param int faceNumber: local face number
:return: determinant of the 2D jacobian
:rtype: ndarray
"""
faceByLocalNodes = getFaceByLocalNodes(faceNumber)
normalVector = np.cross(coordEle[faceByLocalNodes[1],:]-coordEle[faceByLocalNodes[0],:],
coordEle[faceByLocalNodes[2],:]-coordEle[faceByLocalNodes[0],:])
detJacob2D = np.linalg.norm(normalVector)
return detJacob2D
[docs]def getFaceByLocalNodes(faceNumber):
"""Get local nodes ordering for a given face
:param int faceNumber: local face number
:return: list of local nodes for faceNumber
:rtype: ndarray
"""
# Allocate
facesByLocalNodes = np.zeros((4,3), dtype=np.int)
# Reference ordering
facesByLocalNodes[0,:] = [0, 1, 2]
facesByLocalNodes[1,:] = [0, 1, 3]
facesByLocalNodes[2,:] = [1, 2, 3]
facesByLocalNodes[3,:] = [0, 2, 3]
faceByLocalNodes = facesByLocalNodes[faceNumber,:]
return faceByLocalNodes
[docs]def getRealFromReference(rRef, verticesReal):
"""Translate a point defined in the reference element (rRef) to the real element (rReal)
defined by verticesReal
:param ndarray rRef: reference coordinates
:param ndarray verticesReal: real coordinates of element (4x3) = (4 nodes x 3 coordinates)
:return: points in real element
:rtype: ndarray
"""
# You need to change the order if this order is changed. (basically, the
# interpolatory functions are the affine coordinates L1, L2, L3, L4). With
# curved elements higher-order interpolatory functions need to be
# introduced.
rReal = [(1-rRef[0]-rRef[1]-rRef[2])*verticesReal[0,:] +
rRef[0]*verticesReal[1,:] +
rRef[1]*verticesReal[2,:] +
rRef[2]*verticesReal[3,:]]
rReal = np.asarray(rReal[0], dtype=np.float)
return rReal
[docs]def computeBasisFunctionsReferenceElement(edge_orientation, face_orientation, Nord, points):
"""Compute the basis function for the reference element.
:param ndarray edges_orientation: orientation for edges
:param ndarray faces_orientation: orientation for faces
:param int Nord: polynomial order of nedelec basis functions
:param ndarray points: spatial points at which basis functions will be computed
:return: basis functions on reference element
:rtype: ndarray
"""
# Get number of points
if points.ndim == 1:
num_points = 1
points = points.reshape((1,3))
else:
num_points = points.shape[0]
# Compute number of dofs for element
num_dof_in_element = np.int(Nord*(Nord+2)*(Nord+3)/2)
# Allocate
basis = np.zeros((3, num_dof_in_element, num_points), dtype=np.float)
for i in np.arange(num_points):
# Get gauss point coordinates
X = points[i,:]
# Polynomial order (6 edges, 4 faces, 1 volume)
Nord_vector = np.ones(11, dtype=np.int)*Nord
# Edge orientation (6 edges)
NoriE = edge_orientation
# Face orientation (4 faces)
NoriF = face_orientation
# Compute basis for iPoint
NrdofE, ShapE, CurlE = shape3DETet(X, Nord_vector, NoriE, NoriF)
# Verify consistency of number of dofs for this point
if (NrdofE != num_dof_in_element):
Print.master(' Number of DOFs is not consistent')
exit(-1)
# Niref=Ni in reference element
Niref = ShapE[0:3, 0:NrdofE]
# Store basis functions for i
basis[:,:,i] = Niref
return basis
[docs]def unitary_test():
"""Unitary test for hvfem.py script."""
if __name__ == '__main__':
unitary_test()
