def data_for_stress_strain(self, stands, sample_folder, job_file):
"""
Calculation for data of stress strain curves of multi-stand rolling.
Parameters
----------
stands : int
Number of stands being considered in the simulation.
sample_folder : str
Name of the sample folder.
job_file : str
Name of the output file.
"""
import damask
fig = PyPlot.figure()
max_strain = np.zeros(1)
for i in range(1, stands + 1):
d = damask.Result('/nethome/v.shah/{}/{}_stand/{}'.format(
sample_folder, i, job_file))
d.add_Cauchy()
d.add_strain_tensor()
d.add_Mises('sigma')
d.add_Mises('epsilon_V^0.0(F)')
d.add_calculation('avg_sigma', "np.average(#sigma_vM#)")
d.add_calculation('avg_epsilon',
"np.average(#epsilon_V^0.0(F)_vM#)")
def eval_stress(job_file):
"""
return the stress as a numpy array
Parameters
----------
job_file : str
Name of the job_file
"""
d = damask.Result(job_file)
stress_path = d.get_dataset_location('avg_sigma')
stress = np.zeros(len(stress_path))
hdf = h5py.File(d.fname)
for count, path in enumerate(stress_path):
stress[count] = np.array(hdf[path])
stress = np.array(stress) / 1E6
return stress
def eval_strain(job_file):
"""
return the strain as a numpy array
Parameters
----------
job_file : str
Name of the job_file
"""
d = damask.Result(job_file)
stress_path = d.get_dataset_location('avg_sigma')
strain = np.zeros(len(stress_path))
hdf = h5py.File(d.fname)
for count, path in enumerate(stress_path):
strain[count] = np.array(hdf[path.split('avg_sigma')[0] +
'avg_epsilon'])
return strain
def plot(job_file):
"""
Plot the stress strain curve from the job file
Parameters
----------
job_file : str
Name of the job_file
"""
d = damask.Result(job_file)
stress_path = d.get_dataset_location('avg_sigma')
stress = np.zeros(len(stress_path))
strain = np.zeros(len(stress_path))
hdf = h5py.File(d.fname)
for count,path in enumerate(stress_path):
stress[count] = np.array(hdf[path])
strain[count] = np.array(hdf[path.split('avg_sigma')[0] + 'avg_epsilon'])
stress = np.array(stress)/1E6
PyPlot.plot(strain,stress,linestyle='-',linewidth='2.5')
PyPlot.xlabel(r'$\varepsilon_{VM} $',fontsize=18)
PyPlot.ylabel(r'$\sigma_{VM}$ (MPa)',fontsize=18)
def Initial_processing(self, job_file, simulation_folder):
"""
Initial post processing required for MDRX simulations.
Parameters
----------
job_file : str
Name of the damask output file to be processed.
simulation_folder : str
Name of the simulation folder where the job file exists.
"""
import damask
os.chdir('/nethome/v.shah/{}/'.format(simulation_folder))
d = damask.Result('{}.hdf5'.format(job_file.split('.')[0]))
orientation0 = d.get_initial_orientation()
d.add_grainrotation(orientation0)
d.add_Eulers('orientation')
d.add_calculation(
'tot_density',
'np.sum((np.sum(#rho_mob#,1),np.sum(#rho_dip#,1)),0)')
d.add_calculation('r_s', "40/np.sqrt(#tot_density#)")
def add_nuclei_info(self, sample_folder, job_file, casipt, stand):
"""
Add nucleation tag to the data for visualization of the nuclei.
Parameters
----------
sample_folder : str
Name of the sample folder.
job_file : str
Name of the output file (2nd stand and so on).
casipt : str
Name of the casipt file with nucleation info.
stand : int
Number of the stand.
"""
import damask
d = damask.Result('/nethome/v.shah/{}/{}_stand/{}'.format(
sample_folder, stand, job_file))
rex_array = np.loadtxt(casipt, dtype=int, usecols=(1))
total_cells = np.shape(
d.read_dataset([d.get_dataset_location('grain_rotation')[-1]]))[0]
nuclei_array = np.zeros(total_cells)
for i in rex_array:
nuclei_array[i] = 1
path = d.groups_with_datasets('rho_mob')
with h5py.File(d.fname, 'a') as f:
for i in range(len(path)):
try:
f[path[i] + '/Nucleation_tag'] != []
data = f[path[i] + '/Nucleation_tag']
data[...] = nuclei_array
except KeyError:
f[path[i] + '/Nucleation_tag'] = nuclei_array
'--dir',
dest='dir',
default='postProc',
metavar='string',
help=
'name of subdirectory relative to the location of the DADF5 file to hold output'
)
parser.add_argument('--inc',
nargs='+',
help='Increment for which DREAM3D to be used, eg. 25',
type=int)
options = parser.parse_args()
for filename in options.filenames:
f = damask.Result(filename)
N_digits = int(np.floor(np.log10(int(f.increments[-1][3:])))) + 1
f.pick('increments', options.inc)
for inc in damask.util.show_progress(f.iterate('increments'),
len(f.selection['increments'])):
dirname = os.path.abspath(
os.path.join(os.path.dirname(filename), options.dir))
try:
os.mkdir(dirname)
except FileExistsError:
pass
o = h5py.File(dirname + '/' + os.path.splitext(filename)[0] \
+ '_inc_{}.dream3D'.format(inc[3:].zfill(N_digits)),'w')
o.attrs['DADF5toDREAM3D'] = '1.0'
def __init__(self, current_file):
""" Opens the HDF5 file in which rotation is to be calculated."""
import damask
self.current_file = damask.Result(current_file)
def plot_stress_strain(self, stands, sample_folder, job_file):
"""
Plot stress strain curves of multi-stand rolling.
Parameters
----------
stands : int
Number of stands being considered in the simulation.
sample_folder : str
Name of the sample folder.
job_file : str
Name of the output file.
"""
import damask
fig = PyPlot.figure()
max_strain = np.zeros(1)
colormap = PyPlot.cm.gist_ncar #nipy_spectral, Set1,Paired
colors = [colormap(i) for i in np.linspace(0, 1, stands)]
print(colors)
for i in range(1, stands + 1):
d = damask.Result('/nethome/v.shah/{}/{}_stand/{}'.format(
sample_folder, i, job_file))
d.add_Cauchy()
d.add_strain_tensor()
d.add_Mises('sigma')
d.add_Mises('epsilon_V^0.0(F)')
d.add_calculation('avg_sigma', "np.average(#sigma_vM#)")
d.add_calculation('avg_epsilon',
"np.average(#epsilon_V^0.0(F)_vM#)")
print(d.fname)
stress_path = d.get_dataset_location('avg_sigma')
stress = np.zeros(len(stress_path))
strain = np.zeros(len(stress_path))
hdf = h5py.File(d.fname)
for count, path in enumerate(stress_path):
stress[count] = np.array(hdf[path])
strain[count] = np.array(hdf[path.split('avg_sigma')[0] +
'avg_epsilon'])
stress = np.array(stress) / 1E6
strain = (1.0 + np.array(strain)) * (1.0 + max_strain) - 1.0
PyPlot.plot(strain,
stress,
linestyle='-',
linewidth='2.5',
label='{} hit'.format(i))
PyPlot.xlabel(r'$\varepsilon_{VM} $', fontsize=18)
PyPlot.ylabel(r'$\sigma_{VM}$ (MPa)', fontsize=18)
axes = PyPlot.gca()
max_strain = np.max(strain)
expt_data = pd.read_csv(
'/nethome/v.shah/{}/Shen_2016_fig2.csv'.format(sample_folder),
names=['strain', 'stress'],
skiprows=4,
nrows=28)
expt_data_1 = pd.read_csv(
'/nethome/v.shah/{}/Shen_2016_fig2_2nd_hit.csv'.format(
sample_folder),
names=['strain', 'stress'],
skiprows=1)
expt_data = expt_data.append(expt_data_1)
PyPlot.plot(expt_data['strain'],
expt_data['stress'],
'o',
label='Experimental data')
PyPlot.legend()
fig.savefig('/nethome/v.shah/{}/Multi_stand_stress_strain.png'.format(
sample_folder),
dpi=300)
import subprocess, signal, time, os
import psutil
import damask
import h5py
import re
cmd = "mpiexec -n 2 DAMASK_grid -l tensionX.yaml -g 20grains16x16x16.vtr"
with open('check.txt', 'w') as f:
P = subprocess.Popen(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
r = re.compile(' increment 3 converged')
record = []
while P.poll() is None:
for count, line in enumerate(iter(P.stdout.readline, b'')):
record.append(line.decode('utf-8'))
if re.search(r, record[-1]):
os.kill(P.pid + 1, signal.SIGSTOP)
d = damask.Result('20grains16x16x16_tensionX.hdf5')
print(d.get_dataset_location('F'))
os.kill(P.pid + 1, signal.SIGUSR2)
os.kill(P.pid + 1, signal.SIGUSR1)
os.kill(P.pid + 1, signal.SIGCONT)
for children in psutil.Process(P.pid).children(recursive=True):
if children.name() == 'DAMASK_grid':
children.terminate()
for line in record:
f.write(line)
