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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
"""
@file python_muSpectre_gradient_integration_test.py
@author Richard Leute <richard.leute@imtek.uni-freiburg.de>
@date 23 Nov 2018
@brief test the functionality of gradient_integration.py
@section LICENSE
Copyright © 2018 Till Junge, Richard Leute
µSpectre is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation, either version 3, or (at
your option) any later version.
µSpectre is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with µSpectre; see the file COPYING. If not, write to the
Free Software Foundation, Inc., 59 Temple Place - Suite 330,
Boston, MA 02111-1307, USA.
Additional permission under GNU GPL version 3 section 7
If you modify this Program, or any covered work, by linking or combining it
with proprietary FFT implementations or numerical libraries, containing parts
covered by the terms of those libraries' licenses, the licensors of this
Program grant you additional permission to convey the resulting work.
"""
import unittest
import numpy as np
import scipy.misc as sm
import itertools
from python_test_imports import µ
### Helper functions
def init_X_F_Chi(lens, res, rank=2):
"""
Setup all the needed parameters for initialization of the deformation
gradient F and the corresponding deformation map/field Chi_X.
Keyword Arguments:
lens -- list [Lx, Ly, ...] of box lengths in each direction (dtype=float)
res -- list [Nx, Ny, ...] of grid resoultions (dtype = int)
rank -- int (default=2), rank of the deformation gradient tensor F.
(dtype = int)
Returns:
d : np.array of grid spacing for each spatial direction (dtype = float)
dim : int dimension of the structure, derived from len(res).
x_n : np.ndarray shape=(res.shape+1, dim) initial nodal/corner positions
as created by gradient_integration.compute_grid (dtype = float)
x_c : np.ndarray shape=(res.shape+1, dim) initial cell center positions
as created by gradient_integration.compute_grid (dtype = float)
F : np.zeros shape=(res.shape, dim*rank) initialise deformation gradient
(dtype = float)
Chi_n: np.zeros shape=((res+1).shape, dim) initialise deformation field
(dtype = float)
Chi_c: np.zeros shape=(res.shape, dim) initialise deformation field
(dtype = float)
freqs: np.ndarray as returned by compute_wave_vectors(). (dtype = float)
"""
lens = np.array(lens)
res = np.array(res)
d = lens / res
dim = len(res)
x_n, x_c = µ.gradient_integration.compute_grid(lens, res)
F = np.zeros(x_c.shape + (dim,)*(rank-1))
Chi_n = np.zeros(x_n.shape)
Chi_c = np.zeros(x_c.shape)
freqs = µ.gradient_integration.compute_wave_vectors(lens, res)
return d, dim, x_n, x_c, F, Chi_n, Chi_c, freqs
def correct_to_zero_mean(Chi, d, nodal=False):
"""
corrects the displacement field such that it's integral is zero. By this one
can get rid of a constant factor in the deformation gradient. This function
is specialized for this file and should not be used somewhere else.
Keywords:
Chi : np.ndarray of the uncorrected analytic placements (dtype = float)
d : np.array of the gridspacing in each spatial direction (dtype = float)
nodal : bool (default False) specifies if the input are nodal or cell/center
values. Default interpretation are cell/center values.
Returns:
Chi : np.ndarray of zero mean corrected analytic placements (dtype = float)
"""
Chi_zm = np.copy(Chi)
res = np.array(Chi_zm.shape[:-1])
dim = res.size
Chi_zm -= (Chi_zm.sum(axis=tuple(range(dim)))/np.prod(res))\
.reshape((1,)*dim + (dim,))
return Chi_zm
def test_integrate(order, F, Chi_n, Chi_c, tol):
"""
make the convergence tests for the integration
Keywords:
order : list of integration orders which are tested (dtype = int)
F : np.ndarray applied deformation gradient (dtype = float)
Chi_n : np.ndarray expected node positions (dtype = float)
Chi_c : np.ndarray expected cell positions (dtype = float)
tol : list of tolerances for each order. If it is a single value the same
tolerance is used for each order. (dtype = float)
"""
print('Maybe implement a function like this...')
def central_diff_derivative(data, d, order, rank=1):
"""
Compute the first derivative of a function with values 'data' with the
central difference approximation to the order 'order'. The function values
are on a rectangualar grid with constant grid spacing 'd' in each direction.
CAUTION:
The function is assumed to be periodic (pbc)!
Thus, if there is a discontinuity at the boundaries you have to expect
errors in the derivative at the vicinity close to the discontinuity.
Keyword Arguments:
data -- np.ndarray of shape=(resolution, dim*rank) function values on an
equally spaced grid, with grid spacing 'd' (dtype = float)
d -- scalar or np.array of grid spacing in each direction. Scalar is
interpreted as equal spacing in each direction (dtype = float)
order -- int >= 1, gives the accuracy order of the central difference
approximation (dtype = int)
rank -- int, rank of the data tensor
Returns:
deriv: np.ndarray of shape=(resolution, dim, dim) central difference
derivative of given order (dtype = float)
"""
dim = len(data.shape)-rank
weights = sm.central_diff_weights(2*order + 1)
deriv = np.zeros(data.shape + (dim,))
for ax in range(dim):
for i in range(2*order + 1):
deriv[...,ax] += weights[i]*np.roll(data, order-i, axis=ax) / d[ax]
return deriv
class MuSpectre_gradient_integration_Check(unittest.TestCase):
"""
Check the implementation of all muSpectre.gradient_integration functions.
"""
def setUp(self):
self.lengths = np.array([2.4, 3.7, 4.1])
self.resolution = np.array([5, 3, 5])
self.norm_tol = 1e-8
def test_central_diff_derivative(self):
"""
Test of the central difference approximation by central_diff_derivative
of the first derivative of a function on a grid.
"""
res = self.resolution * 15
lens = self.lengths
for j in range(1, len(res)):
d, dim, x_n, x_c, deriv, f_n, f_c, freqs = init_X_F_Chi(lens[:j],
res[:j])
f_c = np.sin(2*np.pi/lens[:j] * x_c)
for i in range(j):
deriv[...,i,i] =2*np.pi/lens[i]*np.cos(2*np.pi*
x_c[...,i]/lens[i])
approx_deriv = central_diff_derivative(f_c, d[:j+1], order=5)
self.assertLess(np.linalg.norm(deriv-approx_deriv),
self.norm_tol)
def test_compute_wave_vectors(self):
"""
Test the construction of a wave vector grid by compute_wave_vectors
for an arbitrary dimension.
"""
lens = [4, 6, 7]
res = [3, 4, 5]
Nx, Ny, Nz = res
q_1d = lambda i: 2*np.pi/lens[i] * \
np.append(np.arange(res[i]-res[i]//2),
-np.arange(1, res[i]//2 + 1)[::-1])
qx = q_1d(0)
qy = q_1d(1)
qz = q_1d(2)
q = np.zeros(tuple(res) + (3,))
for i,j,k in itertools.product(range(Nx), range(Ny), range(Nz)):
q[i,j,k,:] = np.array([qx[i], qy[j], qz[k]])
for n in range(1,4):
comp_q = µ.gradient_integration.compute_wave_vectors(lens[:n],
res[:n])
s = (np.s_[:],)*n + (0,)*(3-n) + (np.s_[:n],)
self.assertLess(np.linalg.norm(comp_q - q[s]), self.norm_tol)
def test_compute_grid(self):
"""
Test the function compute_grid which creates an orthogonal
equally spaced grid of the given resolution and lengths.
"""
lens = self.lengths
res = self.resolution
d = np.array(lens)/np.array(res)
grid_n = np.zeros(tuple(res+1) + (len(res),))
Nx, Ny, Nz = res+1
for i,j,k in itertools.product(range(Nx), range(Ny), range(Nz)):
grid_n[i,j,k,:] = np.array([i*d[0], j*d[1], k*d[2]])
grid_c = (grid_n - d/2)[1:,1:,1:,:]
for n in range(1,4):
x_n, x_c = µ.gradient_integration.compute_grid(lens[:n], res[:n])
s = (np.s_[:],)*n + (0,)*(3-n) + (np.s_[:n],)
self.assertLess(np.linalg.norm(x_c - grid_c[s]), self.norm_tol)
self.assertLess(np.linalg.norm(x_n - grid_n[s]), self.norm_tol)
def test_reshape_gradient(self):
"""
Test if reshape gradient transforms a flattend second order tensor in
the right way to a shape resolution + [dim, dim].
"""
lens = list(self.lengths)
res = list(self.resolution)
tol = 1e-5
formulation = µ.Formulation.finite_strain
DelF = np.array([[0 , 0.01, 0.02],
[0.03, 0 , 0.04],
[0.05, 0.06, 0 ]])
one = np.eye(3,3)
for n in range(2,4):
sys = µ.Cell(res[:n], lens[:n], formulation)
if n == 2:
mat = µ.material.MaterialLinearElastic1_2d.make(sys, "material",
10, 0.3)
if n == 3:
mat = µ.material.MaterialLinearElastic1_3d.make(sys, "material",
10, 0.3)
for pixel in sys:
mat.add_pixel(pixel)
solver = µ.solvers.SolverCG(sys, tol, maxiter=100, verbose=0)
r = µ.solvers.newton_cg(sys, DelF[:n, :n],
solver, tol, tol , verbose=0)
grad = µ.gradient_integration.reshape_gradient(r.grad,list(res[:n]))
grad_theo = (DelF[:n, :n] + one[:n, :n]).reshape((1,)*n+(n,n,))
self.assertEqual(grad.shape, tuple(res[:n])+(n,n,))
self.assertLess(np.linalg.norm(grad - grad_theo), self.norm_tol)
def test_complement_periodically(self):
"""
Test the periodic reconstruction of an array. Lower left entries are
added into the upper right part of the array.
"""
#1D grid scalars
x_test = np.array([0,1,2,3])
x_test_p = np.array([0,1,2,3, 0])
x_p = µ.gradient_integration.complement_periodically(x_test, 1)
self.assertLess(np.linalg.norm(x_p-x_test_p), self.norm_tol)
#2D grid scalars
x_test = np.array([[1,2,3,4],
[5,6,7,8]])
x_test_p = np.array([[1,2,3,4,1],
[5,6,7,8,5],
[1,2,3,4,1]])
x_p = µ.gradient_integration.complement_periodically(x_test, 2)
self.assertLess(np.linalg.norm(x_p-x_test_p), self.norm_tol)
#2D grid vectors
x_test = np.array([[[1,2,3] , [3,4,5]] ,
[[6,7,8] , [9,10,11]],
[[12,13,14], [15,6,17]] ])
x_test_p = np.array([[[1,2,3] , [3,4,5] , [1,2,3]] ,
[[6,7,8] , [9,10,11], [6,7,8]] ,
[[12,13,14], [15,6,17], [12,13,14]],
[[1,2,3] , [3,4,5] , [1,2,3]] ])
x_p = µ.gradient_integration.complement_periodically(x_test, 2)
self.assertLess(np.linalg.norm(x_p-x_test_p), self.norm_tol)
def test_get_integrator(self):
"""
Test if the right integrator is computed.
"""
#even grid
lens_e = np.array([1,1,1])
res_e = np.array([2,2,2])
x_n_e, x_c_e = µ.gradient_integration.compute_grid(lens_e, res_e)
freqs_e = µ.gradient_integration.compute_wave_vectors(lens_e, res_e)
#odd grid
lens_o = np.array([1,1])
res_o = np.array([3,3])
x_n_o, x_c_o = µ.gradient_integration.compute_grid(lens_o, res_o)
delta_x = 1/3
freqs_o = µ.gradient_integration.compute_wave_vectors(lens_o, res_o)
### order=0
int_ana = 1j/(2*np.pi)*np.array([[[[ 0 , 0 , 0], [ 0 , 0 ,-1 ]] ,
[[ 0 ,-1 , 0], [ 0 ,-1/2,-1/2]]],
[[[-1 , 0 , 0], [-1/2, 0 ,-1/2]] ,
[[-1/2,-1/2, 0], [-1/3,-1/3,-1/3]]] ])
dim,shape,integrator = µ.gradient_integration.\
get_integrator(x_c_e, freqs_e, order=0)
self.assertEqual(dim, len(res_e))
self.assertEqual(shape, tuple(res_e))
self.assertLess(np.linalg.norm(integrator-int_ana), self.norm_tol)
### order=1
#even grid
int_ana = np.zeros(res_e.shape)
dim,shape,integrator = µ.gradient_integration.\
get_integrator(x_c_e, freqs_e, order=1)
self.assertEqual(dim, len(res_e))
self.assertEqual(shape, tuple(res_e))
self.assertLess(np.linalg.norm(integrator-int_ana), self.norm_tol)
#odd grid
s = lambda q: np.sin(2*np.pi*q*delta_x)
sq = lambda q: (np.sin(2*np.pi*np.array(q)*delta_x)**2).sum()
int_ana = 1j * delta_x *\
np.array([[[ 0 , 0 ],
[ 0 , s(1)/sq([0,1]) ],
[ 0 , s(-1)/sq([0,-1]) ]],
[[ s(1)/sq([1,0]) , 0 ],
[ s(1)/sq([1,1]) , s(1)/sq([1,1]) ],
[ s(1)/sq([1,-1]) , s(-1)/sq([1,-1]) ]],
[[ s(-1)/sq([-1,0]) , 0 ],
[ s(-1)/sq([-1,1]) , s(1)/sq([-1,1]) ],
[ s(-1)/sq([-1,-1]) , s(-1)/sq([-1,-1]) ]]])
dim,shape,integrator = µ.gradient_integration.\
get_integrator(x_c_o, freqs_o, order=1)
self.assertEqual(dim, len(res_o))
self.assertEqual(shape, tuple(res_o))
self.assertLess(np.linalg.norm(integrator-int_ana), self.norm_tol)
### order=2
#even grid
int_ana = np.zeros(res_e.shape)
dim,shape,integrator = µ.gradient_integration.\
get_integrator(x_c_e, freqs_e, order=2)
self.assertEqual(dim, len(res_e))
self.assertEqual(shape, tuple(res_e))
self.assertLess(np.linalg.norm(integrator-int_ana), self.norm_tol)
#odd grid
lens_o = np.array([1,1])
res_o = np.array([3,3])
x_n, x_c = µ.gradient_integration.compute_grid(lens_o, res_o)
delta_x = 1/3
freqs = µ.gradient_integration.compute_wave_vectors(lens_o, res_o)
s = lambda q: 8*np.sin(2*np.pi*q*delta_x) + np.sin(2*2*np.pi*q*delta_x)
sq = lambda q: ( (64*np.sin(2*np.pi*np.array(q)*delta_x)**2).sum() -
(np.sin(2*2*np.pi*np.array(q)*delta_x)**2).sum() )
int_ana = 6 * 1j * delta_x *\
np.array([[[ 0 , 0 ],
[ 0 , s(1)/sq([0,1]) ],
[ 0 , s(-1)/sq([0,-1]) ]],
[[ s(1)/sq([1,0]) , 0 ],
[ s(1)/sq([1,1]) , s(1)/sq([1,1]) ],
[ s(1)/sq([1,-1]) , s(-1)/sq([1,-1]) ]],
[[ s(-1)/sq([-1,0]) , 0 ],
[ s(-1)/sq([-1,1]) , s(1)/sq([-1,1]) ],
[ s(-1)/sq([-1,-1]) , s(-1)/sq([-1,-1]) ]]])
dim,shape,integrator = µ.gradient_integration.\
get_integrator(x_c_o, freqs_o, order=2)
self.assertEqual(dim, len(res_o))
self.assertEqual(shape, tuple(res_o))
self.assertLess(np.linalg.norm(integrator-int_ana), self.norm_tol)
def test_integrate_tensor_2(self):
"""
Test the correct integration of a second-rank tensor gradient field,
like the deformation gradient.
"""
order = [1, 2] #list of higher order finite difference integration which
#will be checked.
### cosinus, diagonal deformation gradient
res = [15, 15, 14]
lens = [7, 1.4, 3]
d, dim, x_n, x_c, F, Chi_n, Chi_c, freqs = init_X_F_Chi(lens, res)
for i in range(dim):
F[:,:,:,i,i] = 0.8*np.pi/lens[i]*np.cos(4*np.pi*
x_c[:,:,:,i]/lens[i])
Chi_n = 0.2 * np.sin(4*np.pi*x_n/lens)
Chi_c = 0.2 * np.sin(4*np.pi*x_c/lens)
# zeroth order correction
placement_n = µ.gradient_integration.integrate_tensor_2(F, x_n, freqs,
staggered_grid=True, order=0)
placement_c = µ.gradient_integration.integrate_tensor_2(F, x_c, freqs,
staggered_grid=False, order=0)
self.assertLess(np.linalg.norm(Chi_n - placement_n), self.norm_tol)
self.assertLess(np.linalg.norm(Chi_c - placement_c), self.norm_tol)
# higher order correction
for n in order:
tol_n = [1.334, 0.2299] #adjusted tolerances for node points
F_c = central_diff_derivative(Chi_c, d, order=n)
placement_n = µ.gradient_integration.integrate_tensor_2(F_c, x_n,
freqs, staggered_grid=True, order=n)
placement_c = µ.gradient_integration.integrate_tensor_2(F_c, x_c,
freqs, staggered_grid=False,order=n)
self.assertLess(np.linalg.norm(Chi_n - placement_n), tol_n[n-1])
self.assertLess(np.linalg.norm(Chi_c - placement_c), self.norm_tol)
### cosinus, non-diagonal deformation gradient
res = [15, 12, 11]
lens = [8, 8, 8]
d, dim, x_n, x_c, F, Chi_n, Chi_c, freqs = init_X_F_Chi(lens, res)
F[:,:,:,0,0] = 4*np.pi/lens[0]*np.cos(2*np.pi/lens[0]*x_c[:,:,:,0])
F[:,:,:,1,1] = 2*np.pi/lens[1]*np.cos(2*np.pi/lens[1]*x_c[:,:,:,1])
F[:,:,:,2,2] = 2*np.pi/lens[2]*np.cos(2*np.pi/lens[2]*x_c[:,:,:,2])
F[:,:,:,1,0] = 2*np.pi/lens[0]*np.cos(2*np.pi/lens[0]*x_c[:,:,:,0])
F[:,:,:,2,0] = 2*np.pi/lens[0]*np.cos(2*np.pi/lens[0]*x_c[:,:,:,0])
for i in range(dim):
Chi_c[:,:,:,i]= np.sin(2*np.pi*x_c[:,:,:,i]/lens[i]) \
+ np.sin(2*np.pi*x_c[:,:,:,0]/lens[0])
Chi_n[:,:,:,i]= np.sin(2*np.pi*x_n[:,:,:,i]/lens[i]) \
+ np.sin(2*np.pi*x_n[:,:,:,0]/lens[0])
# zeroth order correction
placement_n = µ.gradient_integration.integrate_tensor_2(F, x_n, freqs,
staggered_grid=True, order=0)
placement_c = µ.gradient_integration.integrate_tensor_2(F, x_c, freqs,
staggered_grid=False, order=0)
self.assertLess(np.linalg.norm(Chi_n - placement_n), self.norm_tol)
self.assertLess(np.linalg.norm(Chi_c - placement_c), self.norm_tol)
# higher order correction
for n in order:
tol_n = [2.563, 0.1544] #adjusted tolerances for node points
F_c = central_diff_derivative(Chi_c, d, order=n)
placement_n = µ.gradient_integration.integrate_tensor_2(F_c, x_n,
freqs, staggered_grid=True, order=n)
placement_c = µ.gradient_integration.integrate_tensor_2(F_c, x_c,
freqs, staggered_grid=False,order=n)
self.assertLess(np.linalg.norm(Chi_n - placement_n), tol_n[n-1])
self.assertLess(np.linalg.norm(Chi_c - placement_c), self.norm_tol)
### polynomial, diagonal deformation gradient
# Choose the prefactors of the polynomial such that at least Chi_X and F
# have respectively the same values at the boundaries (here X_i=0 and
# X_i=4).
res = [13, 14, 11]
lens = [4, 4, 4]
d, dim, x_n, x_c, F, Chi_n, Chi_c, freqs = init_X_F_Chi(lens, res)
for i in range(dim):
F[:,:,:,i,i] = 32*x_c[:,:,:,i] -24*x_c[:,:,:,i]**2+4*x_c[:,:,:,i]**3
Chi_n = -128/15 + 16*x_n**2 -8*x_n**3 +x_n**4
Chi_c = -128/15 + 16*x_c**2 -8*x_c**3 +x_c**4
#subtract the mean of Chi_c, because the numeric integration is done to
#give a zero mean fluctuating deformation field.
mean_c = Chi_c.sum(axis=tuple(range(dim)))/ \
np.array(Chi_c.shape[:-1]).prod()
Chi_n -= mean_c.reshape((1,)*dim + (dim,))
Chi_c -= mean_c.reshape((1,)*dim + (dim,))
# zeroth order correction
placement_n = µ.gradient_integration.integrate_tensor_2(F, x_n, freqs,
staggered_grid=True, order=0)
placement_c = µ.gradient_integration.integrate_tensor_2(F, x_c, freqs,
staggered_grid=False, order=0)
self.assertLess(np.linalg.norm(Chi_n - placement_n), 0.19477)
self.assertLess(np.linalg.norm(Chi_c - placement_c), 0.67355)
# higher order correction
for n in order:
tol_n = [18.266, 2.9073] #adjusted tolerances for node points
F_c = central_diff_derivative(Chi_c, d, order=n)
placement_n = µ.gradient_integration.integrate_tensor_2(F_c, x_n,
freqs, staggered_grid=True, order=n)
placement_c = µ.gradient_integration.integrate_tensor_2(F_c, x_c,
freqs, staggered_grid=False,order=n)
self.assertLess(np.linalg.norm(Chi_n - placement_n), tol_n[n-1])
self.assertLess(np.linalg.norm(Chi_c - placement_c), self.norm_tol)
### Realistic test:
# shear of a two dimensional material with two different Young moduli.
order_all = [0]+order #orders for which the test is run
#initialize material structure
res = [ 9, 21] #resolution
lens = [ 9, 21] #lengths
d, dim, x_n, x_c, _, _, _, freqs = init_X_F_Chi(lens, res)
formulation = µ.Formulation.finite_strain
Young = [10, 20] #Youngs modulus for each phase (soft, hard)
Poisson = [0.3, 0.3] #Poissons ratio for each phase
#geometry (two slabs stacked in y-direction with,
#hight h (soft material) and hight res[1]-h (hard material))
h = res[1]//2
phase = np.zeros(tuple(res), dtype=int)
phase[:, h:] = 1
phase = phase.flatten()
cell = µ.Cell(res, lens, formulation)
mat = µ.material.MaterialLinearElastic4_2d.make(cell, "material")
for i, pixel in enumerate(cell):
mat.add_pixel(pixel, Young[phase[i]], Poisson[phase[i]])
cell.initialise()
DelF = np.array([[0 , 0.01],
[0 , 0 ]])
# µSpectre solution
solver = µ.solvers.SolverCG(cell, tol=1e-6, maxiter=100, verbose=0)
result = µ.solvers.newton_cg(cell, DelF, solver, newton_tol=1e-6,
equil_tol=1e-6, verbose=0)
F = µ.gradient_integration.reshape_gradient(result.grad, res)
fin_pos = {} #µSpectre computed center and node positions for all orders
for n in order_all:
placement_n = µ.gradient_integration.integrate_tensor_2(F, x_n,
freqs, staggered_grid=True, order=n)
placement_c = µ.gradient_integration.integrate_tensor_2(F, x_c,
freqs, staggered_grid=False, order=n)
fin_pos[str(n)+'_n'] = placement_n
fin_pos[str(n)+'_c'] = placement_c
# analytic solution, "placement_ana" (node and center)
l_soft = d[1] * h #height soft material
l_hard = d[1] * (res[1]-h) #height hard material
Shear_modulus = np.array(Young) / (2 * (1+np.array(Poisson)))
mean_shear_strain = 2*DelF[0,1]
shear_strain_soft = (lens[1]*mean_shear_strain) / (l_soft
+ l_hard * Shear_modulus[0]/Shear_modulus[1])
shear_strain_hard = (lens[1]*mean_shear_strain) / (l_soft
* Shear_modulus[1]/Shear_modulus[0] + l_hard)
placement_ana_n = np.zeros(x_n.shape)
placement_ana_c = np.zeros(x_c.shape)
#x-coordinate
#soft material
placement_ana_n[:,:h+1,0] = shear_strain_soft/2 * x_n[:, :h+1, 1]
placement_ana_c[:,:h ,0] = shear_strain_soft/2 * x_c[:, :h , 1]
#hard material
placement_ana_n[:,h+1:,0] =shear_strain_hard/2 * (x_n[:,h+1:,1]-l_soft)\
+ shear_strain_soft/2 * l_soft
placement_ana_c[:,h: ,0] =shear_strain_hard/2 * (x_c[:,h: ,1]-l_soft)\
+ shear_strain_soft/2 * l_soft
#y-coordinate
placement_ana_n[:, :, 1] = 0
placement_ana_c[:, :, 1] = 0
#shift the analytic solution such that the average nonaffine deformation
#is zero (integral of the nonaffine deformation gradient + N*const != 0)
F_homo = (1./(np.prod(res)) * F.sum(axis=tuple(np.arange(dim))))\
.reshape((1,)*dim+(dim,)*2)
#integration constant = integral of the nonaffine deformation gradient/N
int_const = - ((placement_ana_c[:,:,0] - F_homo[:,:,0,1] * x_c[:,:,1])
.sum(axis=1))[0] / res[1]
ana_sol_n = placement_ana_n + x_n + \
np.array([int_const, 0]).reshape((1,)*dim+(dim,))
ana_sol_c = placement_ana_c + x_c + \
np.array([int_const, 0]).reshape((1,)*dim+(dim,))
# check the numeric vs the analytic solution
tol_n = [2.2112e-3, 1.3488e-3, 1.8124e-3]
tol_c = [3.1095e-3, 3.2132e-2, 1.8989e-2]
for n in order_all:
norm_n = np.linalg.norm(fin_pos[str(n)+'_n'] - ana_sol_n)
norm_c = np.linalg.norm(fin_pos[str(n)+'_c'] - ana_sol_c)
self.assertLess(norm_n, tol_n[n])
self.assertLess(norm_c, tol_c[n])
def test_integrate_vector(self):
"""Test the integration of a first-rank tensor gradient field."""
order = [1,2]
### cosinus deformation gradient vector field
res = [13, 14, 13]
lens = [ 7, 4, 5]
d, dim, x_n, x_c, df, Chi_n, Chi_c, freqs = init_X_F_Chi(lens, res, 1)
for i in range(dim):
df[:,:,:,i] = 0.8*np.pi/lens[i]*np.cos(4*np.pi*x_c[:,:,:,i]/lens[i])
Chi_n = 0.2 * np.sin(4*np.pi*x_n/lens).sum(axis=-1)
Chi_c = 0.2 * np.sin(4*np.pi*x_c/lens).sum(axis=-1)
# zeroth order correction
placement_n = µ.gradient_integration.integrate_vector(
df, x_n, freqs, staggered_grid=True, order=0)
placement_c = µ.gradient_integration.integrate_vector(
df, x_c, freqs, staggered_grid=False, order=0)
self.assertLess(np.linalg.norm(Chi_n - placement_n), self.norm_tol)
self.assertLess(np.linalg.norm(Chi_c - placement_c), self.norm_tol)
# higher order correction
for n in order:
tol_n = [1.404, 0.2882] #adjusted tolerances for node points
df_c = central_diff_derivative(Chi_c, d, order=n, rank=0)
placement_n = µ.gradient_integration.integrate_vector(
df_c, x_n, freqs, staggered_grid=True, order=n)
placement_c = µ.gradient_integration.integrate_vector(
df_c, x_c, freqs, staggered_grid=False, order=n)
self.assertLess(np.linalg.norm(Chi_n - placement_n), tol_n[n-1])
self.assertLess(np.linalg.norm(Chi_c - placement_c), self.norm_tol)
### polynomial deformation gradient vector field
# Choose the prefactors of the polynomial such that at least Chi_X and F
# have respectively the same values at the boundaries (here X_i=0 and
# X_i=4).
res = [12, 11, 13]
lens = [4, 4, 4]
d, dim, x_n, x_c, df, Chi_n, Chi_c, freqs = init_X_F_Chi(lens, res, 1)
for i in range(dim):
df[:,:,:,i] = 32*x_c[:,:,:,i]-24*x_c[:,:,:,i]**2+4*x_c[:,:,:,i]**3
Chi_n = (-128/15 + 16*x_n**2 -8*x_n**3 +x_n**4).sum(axis=-1)
Chi_c = (-128/15 + 16*x_c**2 -8*x_c**3 +x_c**4).sum(axis=-1)
#subtract the mean of Chi_c, because the numeric integration is done to
#give a zero mean fluctuating deformation field.
mean_c = Chi_c.sum() / np.array(Chi_c.shape).prod()
Chi_n -= mean_c
Chi_c -= mean_c
# zeroth order correction
placement_n = µ.gradient_integration.integrate_vector(
df, x_n, freqs, staggered_grid=True, order=0)
placement_c = µ.gradient_integration.integrate_vector(
df, x_c, freqs, staggered_grid=False, order=0)
self.assertLess(np.linalg.norm(Chi_n - placement_n), 0.20539)
self.assertLess(np.linalg.norm(Chi_c - placement_c), 0.67380)
# higher order correction
for n in order:
tol_n = [18.815, 3.14153] #adjusted tolerances for node points
df_c = central_diff_derivative(Chi_c, d, order=n, rank=0)
placement_n = µ.gradient_integration.integrate_vector(
df_c, x_n, freqs, staggered_grid=True, order=n)
placement_c = µ.gradient_integration.integrate_vector(
df_c, x_c, freqs, staggered_grid=False, order=n)
self.assertLess(np.linalg.norm(Chi_n - placement_n), tol_n[n-1])
self.assertLess(np.linalg.norm(Chi_c - placement_c), self.norm_tol)
def test_compute_placement(self):
"""Test the computation of placements and the original positions."""
### shear of a homogeneous material ###
res = [ 3, 11] #resolution
lens = [10, 10] #lengths
dim = len(res) #dimension
x_n=µ.gradient_integration.compute_grid(np.array(lens),np.array(res))[0]
### finite strain
formulation = µ.Formulation.finite_strain
cell = µ.Cell(res, lens, formulation)
mat = µ.material.MaterialLinearElastic1_2d.make(cell, "material",
Young=10, Poisson=0.3)
for pixel in cell:
mat.add_pixel(pixel)
cell.initialise()
DelF = np.array([[0 , 0.05],
[0 , 0 ]])
# analytic
placement_ana = np.copy(x_n)
placement_ana[:,:,0] += DelF[0,1]*x_n[:,:,1]
# µSpectre solution
solver = µ.solvers.SolverCG(cell, tol=1e-6, maxiter=100, verbose=0)
result = µ.solvers.newton_cg(cell, DelF, solver, newton_tol=1e-6,
equil_tol=1e-6, verbose=0)
for r in [result, result.grad]:
#check input of result=OptimiseResult and result=np.ndarray
placement, x = µ.gradient_integration.compute_placement(
r, lens, res, order=0, formulation=µ.Formulation.finite_strain)
self.assertLess(np.linalg.norm(placement_ana - placement), 1e-12)
self.assertTrue((x_n == x).all())
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