def transform(el, ns, H, set_id, step, wrt_file):
st = time.time()
n_corr = H**2
f_red = h5py.File("sve_reduced.hdf5", 'a')
f_stats = h5py.File("spatial_stats.hdf5", 'r')
f_master = h5py.File("sve_reduced_all.hdf5", 'r')
V = f_master.get('V')[...]
corr_mean = f_master.get('corr_mean')[...]
ff = f_stats.get('ff_%s' % set_id)[...]
ff = ff.reshape(ns, n_corr * el**3)
# subtract out the mean feature values
ff_r = ff
ff_r = ff_r - corr_mean
# tmp = pca.transform(ff_r)
# calculate the pc scores for ff_r
tmp = np.dot(ff_r, V)
f_red.create_dataset('reduced_%s' % set_id, data=tmp, dtype='complex128')
# f_red.create_dataset('reduced_%s' % set_id_set[ii],
# data=tmp,
# dtype='float64')
f_red.close()
f_stats.close()
f_master.close()
timeE = np.round(time.time() - st, 2)
msg = "transform to low dimensional space, %s: %s s" % (set_id, timeE)
rr.WP(msg, wrt_file)
def euler_to_gsh(el, H, ns, set_id, step, wrt_file):
start = time.time()
f = h5py.File("data.hdf5", 'a')
dset_name = 'euler_%s%s_s%s' % (ns, set_id, step)
euler = f.get(dset_name)[...]
euler = euler.swapaxes(1, 2)
euler_GSH = np.zeros([ns, H, el**3], dtype='complex128')
for sn in range(ns):
tmp = gsh.gsh_eval(euler[sn, :, :], np.arange(H))
euler_GSH[sn, :, :] = tmp.T
dset_name = 'euler_GSH_%s%s_s%s.npy' % (ns, set_id, step)
f.create_dataset(dset_name, data=euler_GSH)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "Conversion from Euler angles to GSH coefficients completed:"\
" %s seconds" % timeE
rr.WP(msg, wrt_file)
def validation_procedure(ns_cal,ns_val,set_id_cal,set_id_val,step,comp,wrt_file):
start = time.time()
## el is the # of elements per side of the cube
el = 21
## H is the number of GSH coefficients
H = 15
M = np.load('M_%s%s_s%s.npy' %(ns_val,set_id_val,step))
specinfc = np.load('specinfc%s_%s%s_s%s.npy' %(comp,ns_cal,set_id_cal,step))
specinfc = np.reshape(specinfc,[el,el,el,H])
mks_R = np.zeros([el,el,el,ns_val])
for sn in xrange(ns_val):
mks_F = np.sum(np.conjugate(specinfc) * M[:,:,:,sn,:],3)
mks_R[:,:,:,sn] = np.fft.ifftn(mks_F).real
np.save('mksR%s_%s%s_s%s' %(comp,ns_val,set_id_val,step), mks_R)
end = time.time()
timeE = np.round((end - start),3)
msg = 'validation performed for component %s: %s seconds' %(comp, timeE)
rr.WP(msg,wrt_file)
def inclusion(el, ns, set_id, step, wrt_file, vfrac):
start = time.time()
n_phase = len(vfrac)
f = h5py.File("ref_%s%s_s%s.hdf5" % (ns, set_id, step), 'a')
euler = f.create_dataset("euler", (ns, 3, el**3), dtype='float64')
for sn in xrange(ns):
euler[sn, 0, :] = reul(el**3)
euler[sn, 1, :] = reul(el**3)
euler[sn, 2, :] = reul(el**3)
tmp = np.random.rand(el**3)
for ii in xrange(n_phase):
indx = (tmp > np.sum(vfrac[:ii])) * (tmp < np.sum(vfrac[:
(ii + 1)]))
euler[sn, 0, indx] = reul(np.sum(indx))
euler[sn, 1, indx] = reul(np.sum(indx))
euler[sn, 2, indx] = reul(np.sum(indx))
del indx
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "%s SVEs with inclusions generated with %s orientations: %ss" \
% (ns, n_phase, timeE)
rr.WP(msg, wrt_file)
def validation(el, H, ns_cal, ns_val, set_id_cal, set_id_val, step, comp, typ,
wrt_file):
start = time.time()
f = h5py.File("coef.hdf5", 'r')
dset_name = 'coef%s_%s%s_s%s' % (comp, ns_cal, set_id_cal, step)
coef = f.get(dset_name)[...].reshape(H, el, el, el)
f.close()
f = h5py.File("data.hdf5", 'a')
dset_name = 'M_%s%s_s%s' % (ns_val, set_id_val, step)
M = f.get(dset_name)[...]
tmp = np.sum(np.conjugate(coef) * M, 1)
mks_R = np.fft.ifftn(tmp, [el, el, el], [1, 2, 3]).real
dset_name = '%s%s_mks_%s%s_s%s' % (typ, comp, ns_val, set_id_val, step)
f.create_dataset(dset_name, data=mks_R)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'validation performed for component %s: %s seconds' % (comp, timeE)
rr.WP(msg, wrt_file)
def read_euler(el, ns, set_id, step, newdir, wrt_file, funit):
start = time.time()
euler = np.zeros([ns, 3, el**3])
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir #for unix
os.chdir(nwd)
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % step):
euler[sn, :, :] = rr.read_vtk_vector(filename=filename)
sn += 1
if funit == 1:
euler = euler * (np.pi / 180.)
## return to the original directory
os.chdir('..')
np.save('euler_%s%s_s%s' % (ns, set_id, step), euler)
end = time.time()
timeE = np.round((end - start), 3)
msg = 'euler angles read from .vtk file for %s: %s seconds' % (set_id,
timeE)
rr.WP(msg, wrt_file)
def calibration_procedure(ns,set_id,step,comp,wrt_file):
## el is the # of elements per side of the cube
el = 21
## specify the number of local states you are using
H = 15
M = np.load('M_%s%s_s%s.npy' %(ns,set_id,step))
r_fft = np.load('r%s_fft_%s%s_s%s.npy' %(comp,ns,set_id,step))
start = time.time()
specinfc = np.zeros((el**3,H),dtype = 'complex64')
## here we perform the calibration for the scalar FIP
specinfc[0,:] = rr.calib(0,M,r_fft,0,H,el,ns)
[specinfc[1,:],p] = rr.calib(1,M,r_fft,0,H,el,ns)
## calib_red is simply calib with some default arguments
calib_red = partial(rr.calib,M=M,r_fft=r_fft,
p=p,H=H,el=el,ns=ns)
specinfc[2:(el**3),:] = np.asarray(map(calib_red,range(2,el**3)))
np.save('specinfc%s_%s%s_s%s' %(comp,ns,set_id,step),specinfc)
end = time.time()
timeE = np.round((end - start),3)
msg = 'Calibration, component %s: %s seconds' %(comp, timeE)
rr.WP(msg,wrt_file)
def read_euler(strt, ns, set_id, newdir, funit):
start = time.time()
C = const()
euler = np.zeros([ns, 3, C['el']**3])
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
for ii in xrange(ns):
sn = strt + ii + 1
filename = "Ti64_Dream3D_v01_Output_%s.vtk" % sn
euler[ii, :, :] = rr.read_vtk_vector(filename=filename)
if funit == 1:
euler = euler * (np.pi / 180.)
# return to the original directory
os.chdir('..')
f = h5py.File("spatial.hdf5", 'a')
dset_name = 'euler_%s' % set_id
f.create_dataset(dset_name, data=euler)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'euler angles read from .vtk file for %s: %s seconds' \
% (set_id, timeE)
rr.WP(msg, C['wrt_file'])
def read_fip(ns, set_id, newdir):
start = time.time()
C = const()
fip = np.zeros([ns, C['el']**3])
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % C['step']):
fip[sn, :] = rr.read_vtk_scalar(filename=filename)
sn += 1
"""return to the original directory"""
os.chdir('..')
f = h5py.File("responses.hdf5", 'a')
f.create_dataset('fip_%s' % set_id, data=(1e9) * fip)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'fip values read from .vtk file for %s: %s seconds' % (set_id, timeE)
rr.WP(msg, C['wrt_file'])
def read_fip(el, ns, set_id, step, newdir, wrt_file):
start = time.time()
fip = np.zeros([ns, el**3])
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % step):
fip[sn, :] = rr.read_vtk_scalar(filename=filename)
sn += 1
"""return to the original directory"""
os.chdir('..')
f = h5py.File("fip_%s%s_s%s.hdf5" % (ns, set_id, step), 'a')
f.create_dataset('fip', data=fip)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'fip values read from .vtk file for %s: %s seconds' % (set_id, timeE)
rr.WP(msg, wrt_file)
def bicrystal(el, ns, set_id, step, wrt_file):
start = time.time()
sshape = (ns, el, el, el)
f = h5py.File("ref_%s%s_s%s.hdf5" % (ns, set_id, step), 'a')
sves = f.create_dataset("sves", sshape, dtype='int8')
M = f.create_dataset("M", sshape, dtype='complex64')
sves[...] = np.zeros(sshape)
for sn in xrange(ns):
direc = np.int8(3 * np.random.rand()) # define a random direction
vf = np.int8(
20 * np.random.rand()) + 1 # define a random volume fraction
if direc == 0:
sves[sn, :vf, :, :] = np.ones((vf, el, el))
elif direc == 1:
sves[sn, :, :vf, :] = np.ones((el, vf, el))
elif direc == 2:
sves[sn, :, :, :vf] = np.ones((el, el, vf))
M[...] = np.fft.fftn(sves[...], axes=[1, 2, 3])
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "%s bicrystal SVEs generated: %ss" % (ns, timeE)
rr.WP(msg, wrt_file)
def regress(ns, set_id):
st = time.time()
C = const()
"""load the feature data"""
f = h5py.File("pre_regress_%s.hdf5" % set_id, 'r')
X = f.get('X')[...]
f.close()
"""load the dependent variable data"""
f = h5py.File("responses.hdf5", 'r')
y = f.get('y_sim_%s' % set_id)[...]
y = y.reshape((ns * C['el']**3))
f.close()
# clf = svm.SVR()
# clf = neighbors.KNeighborsRegressor(n_neighbors=1, weights='uniform')
# clf = tree.DecisionTreeRegressor(max_depth=10)
clf = linear_model.LinearRegression(n_jobs=1)
clf.fit(X, y)
joblib.dump(clf, 'modelfit.pkl')
timeE = np.round(time.time() - st, 1)
msg = "fit completed: %s s" % timeE
rr.WP(msg, C['wrt_file'])
def calibration_procedure(el, H, ns, set_id, step, comp, typ, wrt_file):
f = h5py.File("data.hdf5", 'r')
dset_name = 'M_%s%s_s%s' % (ns, set_id, step)
M = f.get(dset_name)[...]
dset_name = '%s%s_fft_fem_%s%s_s%s' % (typ, comp, ns, set_id, step)
r_fft = f.get(dset_name)[...]
f.close()
start = time.time()
coef = np.zeros((H, el**3), dtype='complex64')
# here we perform the calibration for the scalar FIP
coef[:, 0] = rr.calib(0, M, r_fft, 0, H, el, ns)
[coef[:, 1], p] = rr.calib(1, M, r_fft, 0, H, el, ns)
# calib_red is simply calib with some default arguments
calib_red = partial(rr.calib, M=M, r_fft=r_fft, p=p, H=H, el=el, ns=ns)
coef[:, 2:(el**3)] = np.asarray(map(calib_red,
range(2, el**3))).swapaxes(0, 1)
f = h5py.File("coef.hdf5", 'a')
dset_name = 'coef%s_%s%s_s%s' % (comp, ns, set_id, step)
f.create_dataset(dset_name, data=coef)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'Calibration, component %s: %s seconds' % (comp, timeE)
rr.WP(msg, wrt_file)
def read_euler(el, ns, set_id, step, newdir, wrt_file, funit):
start = time.time()
euler = np.zeros([ns, 3, el**3])
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % step):
euler[sn, :, :] = rr.read_vtk_vector(filename=filename)
sn += 1
if funit == 1:
euler = euler * (np.pi / 180.)
"""return to the original directory"""
os.chdir('..')
f = h5py.File("spatial_stats.hdf5", 'a')
f.create_dataset('euler_%s' % set_id, data=euler)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'euler angles read from .vtk file for %s: %s seconds' % (set_id,
timeE)
rr.WP(msg, wrt_file)
def calibration_procedure(el, H, ns, set_id, step, comp, typ, wrt_file):
M = np.load('M_%s%s_s%s.npy' % (ns, set_id, step))
r_fft = np.load('%s%s_fft_fem_%s%s_s%s.npy' %
(typ, comp, ns, set_id, step))
start = time.time()
specinfc = np.zeros((H, el**3), dtype='complex64')
# here we perform the calibration for the scalar FIP
specinfc[:, 0] = rr.calib(0, M, r_fft, 0, H, el, ns)
[specinfc[:, 1], p] = rr.calib(1, M, r_fft, 0, H, el, ns)
# calib_red is simply calib with some default arguments
calib_red = partial(rr.calib, M=M, r_fft=r_fft, p=p, H=H, el=el, ns=ns)
specinfc[:, 2:(el**3)] = np.asarray(map(calib_red,
range(2, el**3))).swapaxes(0, 1)
np.save('specinfc%s_%s%s_s%s' % (comp, ns, set_id, step), specinfc)
end = time.time()
timeE = np.round((end - start), 3)
msg = 'Calibration, component %s: %s seconds' % (comp, timeE)
rr.WP(msg, wrt_file)
def euler_to_gsh(el, H, ns, set_id, step, wrt_file):
start = time.time()
# open HDF5 file
base = tb.open_file("ref_%s%s_s%s.h5" % (ns, set_id, step), mode="r")
euler = base.root.msf.euler[...]
# close the HDF5 file
base.close()
euler_GSH = np.zeros([ns, H, el**3], dtype='complex128')
for sn in xrange(ns):
euler_GSH[sn, :, :] = gsh.gsh(euler[sn, :, :])
end = time.time()
timeE = np.round((end - start), 3)
msg = "Conversion from Euler angles to GSH coefficients completed:" + \
" %s seconds" % timeE
rr.WP(msg, wrt_file)
euler_GSH = euler_GSH.reshape([ns, H, el, el, el])
# MICROSTRUCTURE FUNCTIONS IN FREQUENCY SPACE
start = time.time()
M = np.fft.fftn(euler_GSH, axes=[2, 3, 4])
del euler_GSH
size = M.nbytes
# open HDF5 file
base = tb.open_file("D_%s%s_s%s.h5" % (ns, set_id, step), mode="a")
# initialize array for the euler angles
base.create_array('/msf', 'M', M, 'FFT of GSH microstructure function')
# close the HDF5 file
base.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "FFT3 conversion of micr to M_%s%s_s%s: %s seconds" % \
(ns, set_id, step, timeE)
rr.WP(msg, wrt_file)
msg = 'Size of M_%s%s_s%s: %s bytes' % (ns, set_id, step, size)
rr.WP(msg, wrt_file)
def get_M(ns, set_id):
start = time.time()
C = const()
"""get the euler angle files"""
f = h5py.File("spatial.hdf5", 'a')
euler = f.get('euler_%s' % set_id)[...]
mf = np.zeros([ns, C['H'], C['el']**3], dtype='float64')
c = 0
for h in xrange(C['H']):
tmp = gsh.gsh_eval(euler.swapaxes(1, 2), [h])
tmp = np.squeeze(tmp)
mf[:, c, :] = tmp.real
c += 1
# mf[:, h, :] = (2*indxvec[h, 0]+1)*tmp # 2*l+1 included in maple generator
end = time.time()
timeE = np.round((end - start), 3)
msg = "Conversion from Euler angles to GSH coefficients completed:" + \
" %s seconds" % timeE
rr.WP(msg, C['wrt_file'])
mf = mf.reshape([ns, C['H'], C['el'], C['el'], C['el']])
# MICROSTRUCTURE FUNCTIONS IN FREQUENCY SPACE
start = time.time()
M = np.fft.fftn(mf, axes=[2, 3, 4])
del mf
f.create_dataset('M_%s' % set_id, data=M)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "FFT3 conversion of mf to M for %s: %s seconds" % \
(set_id, timeE)
rr.WP(msg, C['wrt_file'])
msg = 'Size of M: %s gb' % str(M.nbytes/(1e9))
rr.WP(msg, C['wrt_file'])
def msf(el, ns, H, set_id, wrt_file):
start = time.time()
## import microstructures
tmp = sio.loadmat('micr_H%s_%s.mat' % (H, set_id))['gshS']
# #### 12/18/2014 CHANGE BACK
#
# tmp = np.swapaxes(tmp,0,2)
#
# tmp = tmp/np.array([[[1,5,5,5,5,5,9,9,9,9,9,9,9,9,9]]]).T #remove normalization
#
# tmp = np.swapaxes(tmp,0,1)
#
# ####
tmp = np.swapaxes(np.swapaxes(tmp, 0, 2), 0, 1)
micr = tmp.reshape([ns, H, el, el, el])
del tmp
np.save('msf_%s%s' % (ns, set_id), micr)
end = time.time()
timeE = np.round((end - start), 3)
msg = "generate real-space microstructure function from GSH-coefficients: %s seconds" % timeE
rr.WP(msg, wrt_file)
## Microstructure functions in frequency space
start = time.time()
M = np.fft.fftn(micr, axes=[2, 3, 4])
del micr
size = M.nbytes
np.save('M_%s%s' % (ns, set_id), M)
end = time.time()
timeE = np.round((end - start), 3)
msg = "FFT3 conversion of micr to M_%s%s: %s seconds" % (ns, set_id, timeE)
rr.WP(msg, wrt_file)
msg = 'Size of M_%s%s: %s bytes' % (ns, set_id, size)
rr.WP(msg, wrt_file)
def euler_to_gsh(el, H, ns, set_id, step, wrt_file):
start = time.time()
f = h5py.File("ref_%s%s_s%s.hdf5" % (ns, set_id, step), 'r')
euler = f.get('euler')[...]
f.close()
euler_GSH = np.zeros([ns, H, el**3], dtype='complex128')
for sn in xrange(ns):
tmp = gsh.gsh_eval(euler[sn, ...].swapaxes(0, 1), np.arange(15))
euler_GSH[sn, :, :] = tmp.swapaxes(0, 1)
end = time.time()
timeE = np.round((end - start), 3)
msg = "Conversion from Euler angles to GSH coefficients completed:" + \
" %s seconds" % timeE
rr.WP(msg, wrt_file)
euler_GSH = euler_GSH.reshape([ns, H, el, el, el])
# MICROSTRUCTURE FUNCTIONS IN FREQUENCY SPACE
start = time.time()
M = np.fft.fftn(euler_GSH, axes=[2, 3, 4])
del euler_GSH
size = M.nbytes
f = h5py.File("D_%s%s_s%s.hdf5" % (ns, set_id, step), 'a')
f.create_dataset('M', data=M)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "FFT3 conversion of micr to M_%s%s_s%s: %s seconds" % \
(ns, set_id, step, timeE)
rr.WP(msg, wrt_file)
msg = 'Size of M_%s%s_s%s: %s bytes' % (ns, set_id, step, size)
rr.WP(msg, wrt_file)
def read_meas(el, ns, set_id, step, comp, tensor_id, newdir, wrt_file):
start = time.time()
typ = ['sigma', 'epsilon_t', 'epsilon_p']
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
compd = {'11': 0, '22': 4, '33': 8, '12': 1, '13': 6, '23': 5}
compp = compd[comp]
r_fem = np.zeros([ns, el, el, el])
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % step):
r_temp = rr.read_vtk_tensor(filename=filename,
tensor_id=tensor_id,
comp=compp)
r_fem[sn, ...] = r_temp.reshape([el, el, el])
sn += 1
# return to the original directory
os.chdir('..')
# open HDF5 file
base = tb.open_file("ref_%s%s_s%s.h5" % (ns, set_id, step), mode="a")
# create a group one level below root called r[comp]
group = base.create_group('/%s' % typ[tensor_id], 'r%s' % comp,
'comp %s response fields' % comp)
# initialize array for the euler angles
base.create_array(group, 'r_fem', r_fem, 'FEM generated response fields')
# close the HDF5 file
base.close()
# FFT OF RESPONSE FIELD
# open HDF5 file
base = tb.open_file("D_%s%s_s%s.h5" % (ns, set_id, step), mode="a")
# create a group one level below root called r[comp]
group = base.create_group('/%s' % typ[tensor_id], 'r%s' % comp,
'FFTs of comp %s response fields' % comp)
# initialize array for the euler angles
base.create_array(group, 'r_fft', np.fft.fftn(r_fem, axes=[1, 2, 3]),
'FFT of FEM generated response fields')
# close the HDF5 file
base.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'The measure of interest has been read from .vtk file' \
' for %s, set %s: %s seconds' % (set_id, comp, timeE)
rr.WP(msg, wrt_file)
def read_meas(el, ns, set_id, step, comp, tensor_id, newdir, wrt_file):
start = time.time()
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
compd = {'11': 0, '22': 4, '33': 8, '23': 5, '12': 1, '13': 6}
compp = compd[comp]
r_real = np.zeros([ns, el, el, el])
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % step):
r_temp = rr.read_vtk_tensor(filename=filename,
tensor_id=tensor_id,
comp=compp)
r_real[sn, ...] = r_temp.reshape([el, el, el])
sn += 1
# for filename in os.listdir(nwd):
# if filename.endswith('.vtk'):
# r_temp = rr.read_vtk_tensor(filename=filename,
# tensor_id=tensor_id,
# comp=compp)
# r_real[sn, ...] = r_temp.reshape([el, el, el])
# sn += 1
# return to the original directory
os.chdir('..')
typ = ['sigma', 'epsilon', 'epsilonp']
f = h5py.File("data.hdf5", 'a')
dset_name = '%s%s_fem_%s%s_s%s' % (typ[tensor_id], comp, ns, set_id, step)
f.create_dataset(dset_name, data=r_real)
# fftn of response fields
r_fft = np.fft.fftn(r_real, axes=[1, 2, 3])
del r_real
dset_name = '%s%s_fft_fem_%s%s_s%s' % (typ[tensor_id], comp, ns, set_id,
step)
f.create_dataset(dset_name, data=r_fft)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'The measure of interest has been read from .vtk file for %s, component %s: %s seconds' % (
set_id, comp, timeE)
rr.WP(msg, wrt_file)
def validation(el, H, ns_cal, ns_val, set_id_cal, set_id_val, step, comp,
wrt_file):
start = time.time()
# open HDF5 file
base = tb.open_file("infl_%s%s_s%s.h5" % (ns_cal, set_id_cal, step),
mode="r")
infl = base.get_node('/', 'infl%s' % comp)
infl_coef = infl.infl_coef[...].reshape(H, el, el, el)
# close HDF5 file
base.close()
# open HDF5 file
base = tb.open_file("D_%s%s_s%s.h5" % (ns_val, set_id_val, step), mode="r")
M = base.root.msf.M[...]
# close HDF5 file
base.close()
# perform the validation calculations
tmp = np.sum(np.conjugate(infl_coef) * M, 1)
r_mks = np.fft.ifftn(tmp, [el, el, el], [1, 2, 3]).real
# open HDF5 file
base = tb.open_file("ref_%s%s_s%s.h5" % (ns_val, set_id_val, step),
mode="a")
# save the MKS predicted total strain fields
group = base.get_node('/epsilon_t', 'r%s' % comp)
base.create_array(group, 'r_mks', r_mks)
# find the node containing the fem total strain fields
r_et = base.get_node('/epsilon_t', 'r%s' % comp)
et_fem = r_et.r_fem[...]
# find the node containing the fem plastic strain fields
r_ep = base.get_node('/epsilon_p', 'r%s' % comp)
ep_fem = r_ep.r_fem[...]
# estimate the plastic strain predicted by fem as the mks predicted total
# strain minus the FEM elastic strain field
ep_mks = r_mks - (et_fem - ep_fem)
del et_fem, ep_fem
# save the estimated plastic strain from MKS to an array
base.create_array(r_ep, 'r_mks', ep_mks)
# close HDF5 file
base.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'validation performed for component %s: %s seconds' % (comp, timeE)
rr.WP(msg, wrt_file)
def read_meas(ns, set_id, step, comp, vtk_filename, tensor_id, newdir, wrt_file):
start = time.time()
## el is the # of elements per side of the cube
el = 21
r_real = np.zeros([el,el,el,ns])
## change to directory with the .vtk files
# cwd = os.getcwd()
## os.chdir(cwd + '\\' + newdir)
# os.chdir(cwd + '/' + newdir) #for unix
#
# compd = {'11':0,'22':4,'33':8,'12':1,'23':5,'31':6}
# compp = compd[comp]
# print compp
#
# for sn in xrange(ns):
# l_sn = str(sn+1).zfill(5)
# r_temp = rr.read_vtk_tensor(filename = vtk_filename %l_sn, tensor_id = tensor_id, comp = compp)
# r_real[:,:,:,sn] = np.swapaxes(np.reshape(np.flipud(r_temp), [el,el,el]),1,2)
nwd = os.getcwd() + '\\' + newdir
# nwd = os.getcwd() + '/' + newdir #for unix
os.chdir(nwd)
compd = {'11':0,'22':4,'33':8,'12':1,'23':5,'31':6}
compp = compd[comp]
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' %step):
r_temp = rr.read_vtk_tensor(filename = filename, tensor_id = tensor_id, comp = compp)
r_real[:,:,:,sn] = np.swapaxes(np.reshape(np.flipud(r_temp), [el,el,el]),1,2)
sn += 1
## return to the original directory
os.chdir('..')
np.save('r%s_%s%s_s%s' %(comp,ns,set_id,step), r_real)
## fftn of response fields
r_fft = np.fft.fftn(r_real, axes = [0,1,2])
del r_real
np.save('r%s_fft_%s%s_s%s' %(comp,ns,set_id,step),r_fft)
end = time.time()
timeE = np.round((end - start),3)
msg = 'The measure of interest has been read from .vtk file for %s, set %s: %s seconds' %(set_id,comp,timeE)
rr.WP(msg,wrt_file)
def micr_func(ns, set_id, step, wrt_file):
start = time.time()
el = 21
## specify the number of local states you are using
H = 15
## import microstructures
micr = np.zeros([el, el, el, ns, H], dtype='complex128')
pre_micr = np.load('euler_GSH_%s%s_s%s.npy' % (ns, set_id, step))
for h in xrange(H):
for sn in range(ns):
micr[:, :, :, sn, h] = np.swapaxes(
np.reshape(np.flipud(pre_micr[:, sn, h]), [el, el, el]), 1, 2)
del pre_micr
end = time.time()
timeE = np.round((end - start), 3)
msg = "generate real-space microstructure function from GSH-coefficients: %s seconds" % timeE
rr.WP(msg, wrt_file)
## Microstructure functions in frequency space
start = time.time()
M = np.fft.fftn(micr, axes=[0, 1, 2])
del micr
size = M.nbytes
np.save('M_%s%s_s%s' % (ns, set_id, step), M)
end = time.time()
timeE = np.round((end - start), 3)
msg = "FFT3 conversion of micr to M_%s%s_s%s: %s seconds" % (ns, set_id,
step, timeE)
rr.WP(msg, wrt_file)
msg = 'Size of M_%s%s_s%s: %s bytes' % (ns, set_id, step, size)
rr.WP(msg, wrt_file)
def micr_func(el, H, ns, set_id, step, wrt_file):
start = time.time()
f = h5py.File("data.hdf5", 'a')
dset_name = 'euler_GSH_%s%s_s%s.npy' % (ns, set_id, step)
tmp = f.get(dset_name)[...]
# tmp = np.swapaxes(np.swapaxes(tmp,0,2),0,1)
micr = tmp.reshape([ns, H, el, el, el])
del tmp
end = time.time()
timeE = np.round((end - start), 3)
msg = "generate real-space microstructure function from GSH-coefficients:"\
" %s seconds" % timeE
rr.WP(msg, wrt_file)
# Microstructure functions in frequency space
start = time.time()
M = np.fft.fftn(micr, axes=[2, 3, 4])
del micr
size = M.nbytes
dset_name = 'M_%s%s_s%s' % (ns, set_id, step)
f.create_dataset(dset_name, data=M)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "FFT3 conversion of micr to M_%s%s_s%s: %s seconds" % (ns, set_id,
step, timeE)
rr.WP(msg, wrt_file)
msg = 'Size of M_%s%s_s%s: %s bytes' % (ns, set_id, step, size)
rr.WP(msg, wrt_file)
def rand(el, ns, set_id, step, wrt_file):
start = time.time()
f = h5py.File("ref_%s%s_s%s.hdf5" % (ns, set_id, step), 'a')
euler = f.create_dataset("euler", (ns, 3, el**3), dtype='float64')
euler[...] = np.random.rand(ns, 3, el**3) * 360
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "%s random SVEs generated random orientations: %ss" % (ns, timeE)
rr.WP(msg, wrt_file)
def read_meas(ns, set_id, comp, tensor_id, newdir):
start = time.time()
C = const()
typ = ['sigma', 'epsilon_t', 'epsilon_p']
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
compd = {'11': 0, '22': 4, '33': 8, '12': 1, '13': 6, '23': 5}
compp = compd[comp]
r_fem = np.zeros([ns, C['el'], C['el'], C['el']])
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % C['step']):
r_temp = rr.read_vtk_tensor(filename=filename,
tensor_id=tensor_id,
comp=compp)
r_fem[sn, ...] = r_temp.reshape([C['el'], C['el'], C['el']])
sn += 1
"""return to the original directory"""
os.chdir('..')
f = h5py.File("responses.hdf5", 'a')
f.create_dataset('%s_%s' % (typ[tensor_id], set_id), data=r_fem)
f.close()
# """FFT OF RESPONSE FIELD"""
# f = h5py.File("D_%s%s_s%s.hdf5" % (ns, set_id, step), 'a')
# tmp = np.fft.fftn(r_fem, axes=[1, 2, 3])
# print tmp.shape
# f.create_dataset('rfft%s_%s' % (comp, typ[tensor_id]), data=tmp)
# f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'The measure of interest has been read from .vtk file' \
' for %s, component %s, type %s: %s seconds' % (set_id, comp,
typ[tensor_id],
timeE)
rr.WP(msg, C['wrt_file'])
def improcess(el, ns, H, set_id, wrt_file):
start = time.time()
sshape = (ns, el, el)
f = h5py.File("spatial_stats.hdf5", 'a')
sves = f.create_dataset("sves_%s" % set_id, sshape, dtype='int64')
sigset = [0., .2, .4, .6, .8, 2, 5]
for sn in xrange(ns):
base = np.random.random((el, el))
r2a = np.random.randint(1, 5)
r2b = np.random.randint(1, 5)
weights = np.random.random(size=(r2a, r2b))
raw = convolve(base, weights, mode='wrap')
blur = gaussian_filter(raw, sigma=np.random.choice(sigset))
scaled = scale_array(blur)
scaled_lin = scaled.reshape(el**2)
sve = np.zeros((el**2))
vf_bounds = np.zeros(H + 1)
vf_bounds[-1] = 1
tmp = np.sort(np.random.rand(H - 1))
vf_bounds[1:H] = tmp
for ii in xrange(H):
indx = (scaled_lin > vf_bounds[ii]) * (scaled_lin <=
vf_bounds[ii + 1])
sve[indx] = ii
sves[sn, ...] = sve.reshape(el, el)
tmp = sves[...]
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = "%s bicrystal SVEs generated: %ss" % (ns, timeE)
rr.WP(msg, wrt_file)
return tmp
def calibration_procedure(el, H, ns, set_id, step, comp, typ, wrt_file):
st = time.time()
# open HDF5 file
base = tb.open_file("gsh_try_%s%s_s%s.h5" % (ns, set_id, step),
mode="r")
M = base.root.msf.M[...]
# close the HDF5 file
base.close()
# open HDF5 file
base = tb.open_file("ref_%s%s_s%s.h5" % (ns, set_id, step),
mode="r")
# retrieve data from HDF5 file
resp = base.get_node('/%s' % typ, 'r%s' % comp)
r_fft = resp.r_fft[...]
# close the HDF5 file
base.close()
specinfc = np.zeros((H, el**3), dtype='complex64')
# here we perform the calibration for the scalar FIP
specinfc[:, 0] = rr.calib(0, M, r_fft, 0, H, el, ns)
[specinfc[:, 1], p] = rr.calib(1, M, r_fft, 0, H, el, ns)
# calib_red is simply calib with some default arguments
calib_red = partial(rr.calib, M=M, r_fft=r_fft,
p=p, H=H, el=el, ns=ns)
specinfc[:, 2:(el**3)] = np.asarray(map(calib_red, range(2, el**3))).swapaxes(0, 1)
# open HDF5 file
base = tb.open_file("infl_%s%s_s%s.h5" % (ns, set_id, step), mode="w")
# create a group one level below root called infl[comp]
group = base.create_group('/',
'infl%s' % comp,
'influence function for component %s' % comp)
base.create_array(group,
'infl_coef',
specinfc,
'array of influence coefficients')
# close the HDF5 file
base.close()
msg = 'Calibration, component %s: %s seconds' % \
(comp, np.round((time.time() - st), 3))
rr.WP(msg, wrt_file)
def features(ns, set_id):
st = time.time()
C = const()
"""gather the independent variable data"""
f = h5py.File("spatial.hdf5", 'r')
neig = f.get('neig_%s' % set_id)[...]
neig = neig.reshape((C['n_samp'], C['H'], C['cmax']))
f.close()
"""calculate the X matrix"""
X = np.zeros((C['n_samp'], C['xmax']), dtype='float64')
c = 0 # keep track of position in X
"""for 0th order polynomial"""
X[:, 0] = 1
c += 1
"""for 1st order polynomial"""
Imax = C['H']*C['cmax']
Imat = np.unravel_index(np.arange(Imax), (C['H'], C['cmax']))
Imat = np.array(Imat).T
for I in xrange(Imax):
h, pos = Imat[I, :]
X[:, c] = neig[:, h, pos]
c += 1
"""for 2nd order polynomial"""
Imax = C['H']*C['cmax']**2
Imat = np.unravel_index(np.arange(Imax), (C['H'], C['cmax'], C['cmax']))
Imat = np.array(Imat).T
for I in xrange(Imax):
h, pos1, pos2 = Imat[I, :]
X[:, c] = neig[:, h, pos1]*neig[:, h, pos2]
c += 1
f = h5py.File("pre_regress_%s.hdf5" % set_id, 'w')
f.create_dataset('X', data=X)
f.close()
timeE = np.round(time.time()-st, 1)
msg = "features extracted for %s: %s s" % (set_id, timeE)
rr.WP(msg, C['wrt_file'])
