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, '12': 1, '23': 5, '13': 6}
compp = compd[comp]
r_real = np.zeros([ns, el, el, el])
sn = 0
if set_id == "cal":
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
else:
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']
np.save('%s%s_fem_%s%s_s%s' % (typ[tensor_id], comp, ns, set_id, step),
r_real)
# fftn of response fields
r_fft = np.fft.fftn(r_real, axes=[1, 2, 3])
del r_real
np.save('%s%s_fft_fem_%s%s_s%s' % (typ[tensor_id], 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, component %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()
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(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 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 sn in xrange(ns):
filename = "Ti64_Dream3D_v01_Output_%s.vtk" % str(sn + 1)
r_temp = rr.read_vtk_tensor(filename=filename,
tensor_id=tensor_id,
comp=compp)
r_real[sn, ...] = r_temp.reshape([el, el, el])
# 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 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 read_meas(el, ns, set_id, step, typ, comp, tensor_id, newdir):
# 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]
r_real = np.zeros([ns, el, el, el])
sn = 0
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('..')
np.save('%s%s_%s%s_s%s' % (typ, comp, ns, set_id, step), r_real)
@author: nhpnp3
"""
import functions as rr
import numpy as np
el = 21
filename = 'Results_Ti64_Dream3D_XdirLoad_210microns_9261el_AbqInp_PowerLaw_LCF_021_data_v2_05.vtk'
r_fem = np.zeros([6, el**3])
compl = ['11', '22', '33', '12', '13', '23']
compd = {'11': 0, '22': 4, '33': 8, '12': 1, '13': 6, '23': 5}
for comp in xrange(6):
compp = compd[compl[comp]]
print comp
print compl[comp]
print compd[compl[comp]]
r_temp = rr.read_vtk_tensor(filename=filename, tensor_id=1, comp=compp)
r_fem[comp, ...] = r_temp
r_norm = np.sqrt(r_fem[0,...]**2 + r_fem[1,...]**2 + r_fem[2,...]**2 + \
2*(r_fem[3,...]**2) + 2*(r_fem[4,...]**2) + 2*(r_fem[5,...]**2))
print np.min(r_norm)
print np.max(r_norm)
