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DAMASK_EICMD
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Merge branch 'DADF5_point_calculations-2' into development

This commit is contained in:
f.basile 2020-02-25 17:09:28 +01:00
parent 7f3a1a0238 1683e18c1f
commit 822e6b7199
11 changed files with 1619 additions and 1306 deletions
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2
PRIVATE

@ -1 +1 @@
Subproject commit ec615d249d39e5d01446b01ab9a5b7e7601340ad
Subproject commit 6db5f4666fc651b4de3b44ceaed3f2b848170ac9

4
processing/post/addCauchy.py
View File

@ -42,8 +42,8 @@ for name in filenames:
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
table.add('Cauchy',
damask.mechanics.Cauchy(table.get(options.defgrad).reshape(-1,3,3),
table.get(options.stress ).reshape(-1,3,3)).reshape(-1,9),
damask.mechanics.Cauchy(table.get(options.stress ).reshape(-1,3,3),
table.get(options.defgrad).reshape(-1,3,3)).reshape(-1,9),
scriptID+' '+' '.join(sys.argv[1:]))
table.to_ASCII(sys.stdout if name is None else name)

4
processing/post/addPK2.py
View File

@ -43,8 +43,8 @@ for name in filenames:
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
table.add('S',
damask.mechanics.PK2(table.get(options.defgrad).reshape(-1,3,3),
table.get(options.stress ).reshape(-1,3,3)).reshape(-1,9),
damask.mechanics.PK2(table.get(options.stress ).reshape(-1,3,3),
table.get(options.defgrad).reshape(-1,3,3)).reshape(-1,9),
scriptID+' '+' '.join(sys.argv[1:]))
table.to_ASCII(sys.stdout if name is None else name)

69
processing/post/addSpectralDecomposition.py
View File

@ -2,6 +2,7 @@
import os
import sys
from io import StringIO
from optparse import OptionParser
import numpy as np
@ -33,69 +34,27 @@ parser.add_option('--no-check',
parser.set_defaults(rh = True,
)
(options,filenames) = parser.parse_args()
if options.tensor is None:
parser.error('no data column specified.')
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name = name,
buffered = False)
except: continue
damask.util.report(scriptName,name)
# ------------------------------------------ read header ------------------------------------------
table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
table.head_read()
for tensor in options.tensor:
t = table.get(tensor).reshape(-1,3,3)
(u,v) = np.linalg.eigh(damask.mechanics.symmetric(t))
if options.rh: v[np.linalg.det(v) < 0.0,:,2] *= -1.0
# ------------------------------------------ assemble header 1 ------------------------------------
for i,o in enumerate(['Min','Mid','Max']):
table.add('eigval{}({})'.format(o,tensor),u[:,i],
scriptID+' '+' '.join(sys.argv[1:]))
items = {
'tensor': {'dim': 9, 'shape': [3,3], 'labels':options.tensor, 'column': []},
}
errors = []
remarks = []
for i,o in enumerate(['Min','Mid','Max']):
table.add('eigvec{}({})'.format(o,tensor),v[:,:,i],
scriptID+' '+' '.join(sys.argv[1:]))
for type, data in items.items():
for what in data['labels']:
dim = table.label_dimension(what)
if dim != data['dim']: remarks.append('column {} is not a {}...'.format(what,type))
else:
items[type]['column'].append(table.label_index(what))
for order in ['Min','Mid','Max']:
table.labels_append(['eigval{}({})'.format(order,what)]) # extend ASCII header with new labels
for order in ['Min','Mid','Max']:
table.labels_append(['{}_eigvec{}({})'.format(i+1,order,what) for i in range(3)]) # extend ASCII header with new labels
if remarks != []: damask.util.croak(remarks)
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
continue
# ------------------------------------------ assemble header 2 ------------------------------------
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
table.head_write()
# ------------------------------------------ process data -----------------------------------------
outputAlive = True
while outputAlive and table.data_read(): # read next data line of ASCII table
for type, data in items.items():
for column in data['column']:
(u,v) = np.linalg.eigh(np.array(list(map(float,table.data[column:column+data['dim']]))).reshape(data['shape']))
if options.rh and np.dot(np.cross(v[:,0], v[:,1]), v[:,2]) < 0.0 : v[:, 2] *= -1.0 # ensure right-handed eigenvector basis
table.data_append(list(u)) # vector of max,mid,min eigval
table.data_append(list(v.transpose().reshape(data['dim']))) # 3x3=9 combo vector of max,mid,min eigvec coordinates
outputAlive = table.data_write() # output processed line in accordance with column labeling
# ------------------------------------------ output finalization -----------------------------------
table.close() # close input ASCII table (works for stdin)
table.to_ASCII(sys.stdout if name is None else name)

1
python/damask/__init__.py
View File

@ -15,6 +15,7 @@ from .config import Material # noqa
from .colormaps import Colormap, Color # noqa
from .orientation import Symmetry, Lattice, Rotation, Orientation # noqa
from .dadf5 import DADF5 # noqa
from .dadf5 import DADF5 as Result # noqa
from .geom import Geom # noqa
from .solver import Solver # noqa

489
python/damask/dadf5.py
View File

@ -11,8 +11,11 @@ import numpy as np
from . import util
from . import version
from . import mechanics
from . import Rotation
from . import Orientation
from . import Environment
from . import grid_filters
# ------------------------------------------------------------------
class DADF5():
"""
Read and write to DADF5 files.
@ -20,7 +23,6 @@ class DADF5():
DADF5 files contain DAMASK results.
"""
# ------------------------------------------------------------------
def __init__(self,fname):
"""
Opens an existing DADF5 file.
@ -72,10 +74,9 @@ class DADF5():
self.mat_physics += f['/'.join([self.increments[0],'materialpoint',m])].keys()
self.mat_physics = list(set(self.mat_physics)) # make unique
self.visible= {'increments': self.increments,
self.selection= {'increments': self.increments,
'constituents': self.constituents,
'materialpoints': self.materialpoints,
'constituent': range(self.Nconstituents), # ToDo: stupid naming
'con_physics': self.con_physics,
'mat_physics': self.mat_physics}
@ -92,7 +93,7 @@ class DADF5():
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
what : str
attribute to change (must be in self.visible)
attribute to change (must be in self.selection)
action : str
select from 'set', 'add', and 'del'
@ -105,15 +106,18 @@ class DADF5():
choice = [datasets] if isinstance(datasets,str) else datasets
valid = [e for e_ in [glob.fnmatch.filter(getattr(self,what),s) for s in choice] for e in e_]
existing = set(self.visible[what])
existing = set(self.selection[what])
if action == 'set':
self.visible[what] = valid
self.selection[what] = valid
elif action == 'add':
self.visible[what] = list(existing.union(valid))
add=existing.union(valid)
add_sorted=sorted(add, key=lambda x: int("".join([i for i in x if i.isdigit()])))
self.selection[what] = add_sorted
elif action == 'del':
self.visible[what] = list(existing.difference_update(valid))
diff=existing.difference(valid)
diff_sorted=sorted(diff, key=lambda x: int("".join([i for i in x if i.isdigit()])))
self.selection[what] = diff_sorted
def __time_to_inc(self,start,end):
selected = []
@ -229,17 +233,17 @@ class DADF5():
Parameters
----------
what : str
attribute to change (must be in self.visible)
attribute to change (must be in self.selection)
"""
datasets = self.visible[what]
datasets = self.selection[what]
last_datasets = datasets.copy()
for dataset in datasets:
if last_datasets != self.visible[what]:
if last_datasets != self.selection[what]:
self.__manage_visible(datasets,what,'set')
raise Exception
self.__manage_visible(dataset,what,'set')
last_datasets = self.visible[what]
last_datasets = self.selection[what]
yield dataset
self.__manage_visible(datasets,what,'set')
@ -254,7 +258,7 @@ class DADF5():
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
what : str
attribute to change (must be in self.visible)
attribute to change (must be in self.selection)
"""
self.__manage_visible(datasets,what,'set')
@ -270,7 +274,7 @@ class DADF5():
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
what : str
attribute to change (must be in self.visible)
attribute to change (must be in self.selection)
"""
self.__manage_visible(datasets,what,'add')
@ -278,7 +282,7 @@ class DADF5():
def del_visible(self,what,datasets):
"""
Delete from active groupse.
Delete from active groupe.
Parameters
----------
@ -286,7 +290,7 @@ class DADF5():
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
what : str
attribute to change (must be in self.visible)
attribute to change (must be in self.selection)
"""
self.__manage_visible(datasets,what,'del')
@ -351,7 +355,8 @@ class DADF5():
for d in f[group].keys():
try:
dataset = f['/'.join([group,d])]
message+=' {} / ({}): {}\n'.format(d,dataset.attrs['Unit'].decode(),dataset.attrs['Description'].decode())
message+=' {} / ({}): {}\n'.\
format(d,dataset.attrs['Unit'].decode(),dataset.attrs['Description'].decode())
except KeyError:
pass
return message
@ -435,12 +440,7 @@ class DADF5():
def cell_coordinates(self):
"""Return initial coordinates of the cell centers."""
if self.structured:
delta = self.size/self.grid*0.5
z, y, x = np.meshgrid(np.linspace(delta[2],self.size[2]-delta[2],self.grid[2]),
np.linspace(delta[1],self.size[1]-delta[1],self.grid[1]),
np.linspace(delta[0],self.size[0]-delta[0],self.grid[0]),
)
return np.concatenate((x[:,:,:,None],y[:,:,:,None],z[:,:,:,None]),axis = 3).reshape([np.product(self.grid),3])
return grid_filters.cell_coord0(self.grid,self.size,self.origin)
else:
with h5py.File(self.fname,'r') as f:
return f['geometry/x_c'][()]
@ -453,10 +453,10 @@ class DADF5():
Parameters
----------
x : str
Label of the dataset containing a scalar, vector, or tensor.
Label of scalar, vector, or tensor dataset to take absolute value of.
"""
def __add_absolute(x):
def _add_absolute(x):
return {
'data': np.abs(x['data']),
@ -468,33 +468,31 @@ class DADF5():
}
}
requested = [{'label':x,'arg':'x'}]
self.__add_generic_pointwise(__add_absolute,requested)
self.__add_generic_pointwise(_add_absolute,{'x':x})
def add_calculation(self,formula,label,unit='n/a',description=None,vectorized=True):
def add_calculation(self,label,formula,unit='n/a',description=None,vectorized=True):
"""
Add result of a general formula.
Parameters
----------
formula : str
Formula, refer to datasets by ‘#Label#‘.
label : str
Label of the dataset containing the result of the calculation.
Label of resulting dataset.
formula : str
Formula to calculate resulting dataset. Existing datasets are referenced by ‘#TheirLabel#‘.
unit : str, optional
Physical unit of the result.
description : str, optional
Human readable description of the result.
Human-readable description of the result.
vectorized : bool, optional
Indicate whether the formula is written in vectorized form. Default is ‘True’.
Indicate whether the formula can be used in vectorized form. Defaults to ‘True’.
"""
if vectorized is False:
if not vectorized:
raise NotImplementedError
def __add_calculation(**kwargs):
def _add_calculation(**kwargs):
formula = kwargs['formula']
for d in re.findall(r'#(.*?)#',formula):
@ -510,154 +508,233 @@ class DADF5():
}
}
requested = [{'label':d,'arg':d} for d in set(re.findall(r'#(.*?)#',formula))] # datasets used in the formula
pass_through = {'formula':formula,'label':label,'unit':unit,'description':description}
dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
args = {'formula':formula,'label':label,'unit':unit,'description':description}
self.__add_generic_pointwise(__add_calculation,requested,pass_through)
self.__add_generic_pointwise(_add_calculation,dataset_mapping,args)
def add_Cauchy(self,P='P',F='F'):
"""
Add Cauchy stress calculated from 1. Piola-Kirchhoff stress and deformation gradient.
Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
Parameters
----------
P : str, optional
Label of the dataset containing the 1. Piola-Kirchhoff stress. Default value is ‘P’.
Label of the dataset containing the first Piola-Kirchhoff stress. Defaults to ‘P’.
F : str, optional
Label of the dataset containing the deformation gradient. Default value is ‘F’.
Label of the dataset containing the deformation gradient. Defaults to ‘F’.
"""
def __add_Cauchy(F,P):
def _add_Cauchy(P,F):
return {
'data': mechanics.Cauchy(F['data'],P['data']),
'data': mechanics.Cauchy(P['data'],F['data']),
'label': 'sigma',
'meta': {
'Unit': P['meta']['Unit'],
'Description': 'Cauchy stress calculated from {} ({}) '.format(P['label'],P['meta']['Description'])+\
'and deformation gradient {} ({})'.format(F['label'],F['meta']['Description']),
'Description': 'Cauchy stress calculated from {} ({}) '.format(P['label'],
P['meta']['Description'])+\
'and {} ({})'.format(F['label'],F['meta']['Description']),
'Creator': 'dadf5.py:add_Cauchy v{}'.format(version)
}
}
requested = [{'label':F,'arg':'F'},
{'label':P,'arg':'P'} ]
self.__add_generic_pointwise(__add_Cauchy,requested)
self.__add_generic_pointwise(_add_Cauchy,{'P':P,'F':F})
def add_determinant(self,x):
def add_determinant(self,T):
"""
Add the determinant of a tensor.
Parameters
----------
x : str
Label of the dataset containing a tensor.
T : str
Label of tensor dataset.
"""
def __add_determinant(x):
def _add_determinant(T):
return {
'data': np.linalg.det(x['data']),
'label': 'det({})'.format(x['label']),
'data': np.linalg.det(T['data']),
'label': 'det({})'.format(T['label']),
'meta': {
'Unit': x['meta']['Unit'],
'Description': 'Determinant of tensor {} ({})'.format(x['label'],x['meta']['Description']),
'Unit': T['meta']['Unit'],
'Description': 'Determinant of tensor {} ({})'.format(T['label'],T['meta']['Description']),
'Creator': 'dadf5.py:add_determinant v{}'.format(version)
}
}
requested = [{'label':x,'arg':'x'}]
self.__add_generic_pointwise(__add_determinant,requested)
self.__add_generic_pointwise(_add_determinant,{'T':T})
def add_deviator(self,x):
def add_deviator(self,T):
"""
Add the deviatoric part of a tensor.
Parameters
----------
x : str
Label of the dataset containing a tensor.
T : str
Label of tensor dataset.
"""
def __add_deviator(x):
def _add_deviator(T):
if not np.all(np.array(x['data'].shape[1:]) == np.array([3,3])):
if not np.all(np.array(T['data'].shape[1:]) == np.array([3,3])):
raise ValueError
return {
'data': mechanics.deviatoric_part(x['data']),
'label': 's_{}'.format(x['label']),
'data': mechanics.deviatoric_part(T['data']),
'label': 's_{}'.format(T['label']),
'meta': {
'Unit': x['meta']['Unit'],
'Description': 'Deviator of tensor {} ({})'.format(x['label'],x['meta']['Description']),
'Unit': T['meta']['Unit'],
'Description': 'Deviator of tensor {} ({})'.format(T['label'],T['meta']['Description']),
'Creator': 'dadf5.py:add_deviator v{}'.format(version)
}
}
requested = [{'label':x,'arg':'x'}]
self.__add_generic_pointwise(__add_deviator,requested)
self.__add_generic_pointwise(_add_deviator,{'T':T})
def add_maximum_shear(self,x):
def add_eigenvalues(self,S):
"""
Add eigenvalues of symmetric tensor.
Parameters
----------
S : str
Label of symmetric tensor dataset.
"""
def _add_eigenvalue(S):
return {
'data': mechanics.eigenvalues(S['data']),
'label': 'lambda({})'.format(S['label']),
'meta' : {
'Unit': S['meta']['Unit'],
'Description': 'Eigenvalues of {} ({})'.format(S['label'],S['meta']['Description']),
'Creator': 'dadf5.py:add_eigenvalues v{}'.format(version)
}
}
self.__add_generic_pointwise(_add_eigenvalue,{'S':S})
def add_eigenvectors(self,S):
"""
Add eigenvectors of symmetric tensor.
Parameters
----------
S : str
Label of symmetric tensor dataset.
"""
def _add_eigenvector(S):
return {
'data': mechanics.eigenvectors(S['data']),
'label': 'v({})'.format(S['label']),
'meta' : {
'Unit': '1',
'Description': 'Eigenvectors of {} ({})'.format(S['label'],S['meta']['Description']),
'Creator': 'dadf5.py:add_eigenvectors v{}'.format(version)
}
}
self.__add_generic_pointwise(_add_eigenvector,{'S':S})
def add_IPFcolor(self,q,l):
"""
Add RGB color tuple of inverse pole figure (IPF) color.
Parameters
----------
q : str
Label of the dataset containing the crystallographic orientation as quaternions.
l : numpy.array of shape (3)
Lab frame direction for inverse pole figure.
"""
def _add_IPFcolor(q,l):
d = np.array(l)
d_unit = d/np.linalg.norm(d)
m = util.scale_to_coprime(d)
colors = np.empty((len(q['data']),3),np.uint8)
lattice = q['meta']['Lattice']
for i,q in enumerate(q['data']):
o = Orientation(np.array([q['w'],q['x'],q['y'],q['z']]),lattice).reduced()
colors[i] = np.uint8(o.IPFcolor(d_unit)*255)
return {
'data': colors,
'label': 'IPFcolor_[{} {} {}]'.format(*m),
'meta' : {
'Unit': 'RGB (8bit)',
'Lattice': lattice,
'Description': 'Inverse Pole Figure (IPF) colors for direction/plane [{} {} {})'.format(*m),
'Creator': 'dadf5.py:add_IPFcolor v{}'.format(version)
}
}
self.__add_generic_pointwise(_add_IPFcolor,{'q':q},{'l':l})
def add_maximum_shear(self,S):
"""
Add maximum shear components of symmetric tensor.
Parameters
----------
x : str
Label of the dataset containing a symmetric tensor.
S : str
Label of symmetric tensor dataset.
"""
def __add_maximum_shear(x):
def _add_maximum_shear(S):
return {
'data': mechanics.maximum_shear(x['data']),
'label': 'max_shear({})'.format(x['label']),
'data': mechanics.maximum_shear(S['data']),
'label': 'max_shear({})'.format(S['label']),
'meta': {
'Unit': x['meta']['Unit'],
'Description': 'Maximum shear component of of {} ({})'.format(x['label'],x['meta']['Description']),
'Unit': S['meta']['Unit'],
'Description': 'Maximum shear component of {} ({})'.format(S['label'],S['meta']['Description']),
'Creator': 'dadf5.py:add_maximum_shear v{}'.format(version)
}
}
requested = [{'label':x,'arg':'x'}]
self.__add_generic_pointwise(__add_maximum_shear,requested)
self.__add_generic_pointwise(_add_maximum_shear,{'S':S})
def add_Mises(self,x):
def add_Mises(self,S):
"""
Add the equivalent Mises stress or strain of a symmetric tensor.
Parameters
----------
x : str
Label of the dataset containing a symmetric stress or strain tensor.
S : str
Label of symmetric tensorial stress or strain dataset.
"""
def __add_Mises(x):
def _add_Mises(S):
t = 'strain' if x['meta']['Unit'] == '1' else \
t = 'strain' if S['meta']['Unit'] == '1' else \
'stress'
return {
'data': mechanics.Mises_strain(x['data']) if t=='strain' else mechanics.Mises_stress(x['data']),
'label': '{}_vM'.format(x['label']),
'data': mechanics.Mises_strain(S['data']) if t=='strain' else mechanics.Mises_stress(S['data']),
'label': '{}_vM'.format(S['label']),
'meta': {
'Unit': x['meta']['Unit'],
'Description': 'Mises equivalent {} of {} ({})'.format(t,x['label'],x['meta']['Description']),
'Unit': S['meta']['Unit'],
'Description': 'Mises equivalent {} of {} ({})'.format(t,S['label'],S['meta']['Description']),
'Creator': 'dadf5.py:add_Mises v{}'.format(version)
}
}
requested = [{'label':x,'arg':'x'}]
self.__add_generic_pointwise(__add_Mises,requested)
self.__add_generic_pointwise(_add_Mises,{'S':S})
def add_norm(self,x,ord=None):
@ -667,12 +744,12 @@ class DADF5():
Parameters
----------
x : str
Label of the dataset containing a vector or tensor.
Label of vector or tensor dataset.
ord : {non-zero int, inf, -inf, ‘fro’, ‘nuc’}, optional
Order of the norm. inf means numpy’s inf object. For details refer to numpy.linalg.norm.
Order of the norm. inf means NumPy’s inf object. For details refer to numpy.linalg.norm.
"""
def __add_norm(x,ord):
def _add_norm(x,ord):
o = ord
if len(x['data'].shape) == 2:
@ -691,93 +768,155 @@ class DADF5():
'label': '|{}|_{}'.format(x['label'],o),
'meta': {
'Unit': x['meta']['Unit'],
'Description': '{}-Norm of {} {} ({})'.format(ord,t,x['label'],x['meta']['Description']),
'Description': '{}-norm of {} {} ({})'.format(ord,t,x['label'],x['meta']['Description']),
'Creator': 'dadf5.py:add_norm v{}'.format(version)
}
}
requested = [{'label':x,'arg':'x'}]
self.__add_generic_pointwise(__add_norm,requested,{'ord':ord})
self.__add_generic_pointwise(_add_norm,{'x':x},{'ord':ord})
def add_principal_components(self,x):
def add_PK2(self,P='P',F='F'):
"""
Add principal components of symmetric tensor.
The principal components are sorted in descending order, each repeated according to its multiplicity.
Add 2. Piola-Kirchhoff calculated from first Piola-Kirchhoff stress and deformation gradient.
Parameters
----------
x : str
Label of the dataset containing a symmetric tensor.
P : str, optional
Label first Piola-Kirchhoff stress dataset. Defaults to ‘P’.
F : str, optional
Label of deformation gradient dataset. Defaults to ‘F’.
"""
def __add_principal_components(x):
def _add_PK2(P,F):
return {
'data': mechanics.principal_components(x['data']),
'label': 'lambda_{}'.format(x['label']),
'data': mechanics.PK2(P['data'],F['data']),
'label': 'S',
'meta': {
'Unit': x['meta']['Unit'],
'Description': 'Pricipal components of {} ({})'.format(x['label'],x['meta']['Description']),
'Creator': 'dadf5.py:add_principal_components v{}'.format(version)
'Unit': P['meta']['Unit'],
'Description': '2. Kirchhoff stress calculated from {} ({}) '.format(P['label'],
P['meta']['Description'])+\
'and {} ({})'.format(F['label'],F['meta']['Description']),
'Creator': 'dadf5.py:add_PK2 v{}'.format(version)
}
}
requested = [{'label':x,'arg':'x'}]
self.__add_generic_pointwise(__add_principal_components,requested)
self.__add_generic_pointwise(_add_PK2,{'P':P,'F':F})
def add_spherical(self,x):
def add_pole(self,q,p,polar=False):
"""
Add coordinates of stereographic projection of given pole in crystal frame.
Parameters
----------
q : str
Label of the dataset containing the crystallographic orientation as quaternions.
p : numpy.array of shape (3)
Crystallographic direction or plane.
polar : bool, optional
Give pole in polar coordinates. Defaults to False.
"""
def _add_pole(q,p,polar):
pole = np.array(p)
unit_pole = pole/np.linalg.norm(pole)
m = util.scale_to_coprime(pole)
coords = np.empty((len(q['data']),2))
for i,q in enumerate(q['data']):
o = Rotation(np.array([q['w'],q['x'],q['y'],q['z']]))
rotatedPole = o*unit_pole # rotate pole according to crystal orientation
(x,y) = rotatedPole[0:2]/(1.+abs(unit_pole[2])) # stereographic projection
coords[i] = [np.sqrt(x*x+y*y),np.arctan2(y,x)] if polar else [x,y]
return {
'data': coords,
'label': 'p^{}_[{} {} {})'.format(u'rφ' if polar else 'xy',*m),
'meta' : {
'Unit': '1',
'Description': '{} coordinates of stereographic projection of pole (direction/plane) in crystal frame'\
.format('Polar' if polar else 'Cartesian'),
'Creator' : 'dadf5.py:add_pole v{}'.format(version)
}
}
self.__add_generic_pointwise(_add_pole,{'q':q},{'p':p,'polar':polar})
def add_rotational_part(self,F):
"""
Add rotational part of a deformation gradient.
Parameters
----------
F : str, optional
Label of deformation gradient dataset.
"""
def _add_rotational_part(F):
return {
'data': mechanics.rotational_part(F['data']),
'label': 'R({})'.format(F['label']),
'meta': {
'Unit': F['meta']['Unit'],
'Description': 'Rotational part of {} ({})'.format(F['label'],F['meta']['Description']),
'Creator': 'dadf5.py:add_rotational_part v{}'.format(version)
}
}
self.__add_generic_pointwise(_add_rotational_part,{'F':F})
def add_spherical(self,T):
"""
Add the spherical (hydrostatic) part of a tensor.
Parameters
----------
x : str
Label of the dataset containing a tensor.
T : str
Label of tensor dataset.
"""
def __add_spherical(x):
def _add_spherical(T):
if not np.all(np.array(x['data'].shape[1:]) == np.array([3,3])):
if not np.all(np.array(T['data'].shape[1:]) == np.array([3,3])):
raise ValueError
return {
'data': mechanics.spherical_part(x['data']),
'label': 'p_{}'.format(x['label']),
'data': mechanics.spherical_part(T['data']),
'label': 'p_{}'.format(T['label']),
'meta': {
'Unit': x['meta']['Unit'],
'Description': 'Spherical component of tensor {} ({})'.format(x['label'],x['meta']['Description']),
'Unit': T['meta']['Unit'],
'Description': 'Spherical component of tensor {} ({})'.format(T['label'],T['meta']['Description']),
'Creator': 'dadf5.py:add_spherical v{}'.format(version)
}
}
requested = [{'label':x,'arg':'x'}]
self.__add_generic_pointwise(__add_spherical,requested)
self.__add_generic_pointwise(_add_spherical,{'T':T})
def add_strain_tensor(self,F='F',t='U',m=0):
def add_strain_tensor(self,F='F',t='V',m=0.0):
"""
Add strain tensor calculated from a deformation gradient.
Add strain tensor of a deformation gradient.
For details refer to damask.mechanics.strain_tensor
Parameters
----------
F : str, optional
Label of the dataset containing the deformation gradient. Default value is ‘F’.
Label of deformation gradient dataset. Defaults to ‘F’.
t : {‘V’, ‘U’}, optional
Type of the polar decomposition, ‘V’ for right stretch tensor and ‘U’ for left stretch tensor.
Defaults value is ‘U’.
Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor.
Defaults to ‘V’.
m : float, optional
Order of the strain calculation. Default value is ‘0.0’.
Order of the strain calculation. Defaults to ‘0.0’.
"""
def __add_strain_tensor(F,t,m):
def _add_strain_tensor(F,t,m):
return {
'data': mechanics.strain_tensor(F['data'],t,m),
@ -789,12 +928,39 @@ class DADF5():
}
}
requested = [{'label':F,'arg':'F'}]
self.__add_generic_pointwise(__add_strain_tensor,requested,{'t':t,'m':m})
self.__add_generic_pointwise(_add_strain_tensor,{'F':F},{'t':t,'m':m})
def __add_generic_pointwise(self,func,datasets_requested,extra_args={}):
def add_stretch_tensor(self,F='F',t='V'):
"""
Add stretch tensor of a deformation gradient.
Parameters
----------
F : str, optional
Label of deformation gradient dataset. Defaults to ‘F’.
t : {‘V’, ‘U’}, optional
Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor.
Defaults to ‘V’.
"""
def _add_stretch_tensor(F,t):
return {
'data': mechanics.left_stretch(F['data']) if t == 'V' else mechanics.right_stretch(F['data']),
'label': '{}({})'.format(t,F['label']),
'meta': {
'Unit': F['meta']['Unit'],
'Description': '{} stretch tensor of {} ({})'.format('Left' if t == 'V' else 'Right',
F['label'],F['meta']['Description']),
'Creator': 'dadf5.py:add_stretch_tensor v{}'.format(version)
}
}
self.__add_generic_pointwise(_add_stretch_tensor,{'F':F},{'t':t})
def __add_generic_pointwise(self,func,dataset_mapping,args={}):
"""
General function to add pointwise data.
@ -802,8 +968,8 @@ class DADF5():
----------
func : function
Function that calculates a new dataset from one or more datasets per HDF5 group.
datasets_requested : list of dictionaries
Details of the datasets to be used: label (in HDF5 file) and arg (argument to which the data is parsed in func).
dataset_mapping : dictionary
Mapping HDF5 data label to callback function argument
extra_args : dictionary, optional
Any extra arguments parsed to func.
@ -812,8 +978,9 @@ class DADF5():
"""Call function with input data + extra arguments, returns results + group."""
args['results'].put({**args['func'](**args['in']),'group':args['group']})
N_threads = 1 # ToDo: should be a parameter
env = Environment()
N_threads = int(env.options['DAMASK_NUM_THREADS'])
N_threads //=N_threads # disable for the moment
results = Queue(N_threads)
pool = util.ThreadPool(N_threads)
@ -821,16 +988,16 @@ class DADF5():
todo = []
# ToDo: It would be more memory efficient to read only from file when required, i.e. do to it in pool.add_task
for group in self.groups_with_datasets([d['label'] for d in datasets_requested]):
for group in self.groups_with_datasets(dataset_mapping.values()):
with h5py.File(self.fname,'r') as f:
datasets_in = {}
for d in datasets_requested:
loc = f[group+'/'+d['label']]
for arg,label in dataset_mapping.items():
loc = f[group+'/'+label]
data = loc[()]
meta = {k:loc.attrs[k].decode() for k in loc.attrs.keys()}
datasets_in[d['arg']] = {'data': data, 'meta' : meta, 'label' : d['label']}
datasets_in[arg] = {'data': data, 'meta': meta, 'label': label}
todo.append({'in':{**datasets_in,**extra_args},'func':func,'group':group,'results':results})
todo.append({'in':{**datasets_in,**args},'func':func,'group':group,'results':results})
pool.map(job, todo[:N_added]) # initialize
@ -850,7 +1017,7 @@ class DADF5():
pool.wait_completion()
def to_vtk(self,labels,mode='Cell'):
def to_vtk(self,labels,mode='cell'):
"""
Export to vtk cell/point data.
@ -858,12 +1025,12 @@ class DADF5():
----------
labels : str or list of
Labels of the datasets to be exported.
mode : str, either 'Cell' or 'Point'
mode : str, either 'cell' or 'point'
Export in cell format or point format.
Default value is 'Cell'.
Defaults to 'cell'.
"""
if mode=='Cell':
if mode.lower()=='cell':
if self.structured:
@ -908,7 +1075,7 @@ class DADF5():
for i in f['/geometry/T_c']:
vtk_geom.InsertNextCell(vtk_type,n_nodes,i-1)
elif mode == 'Point':
elif mode.lower()=='point':
Points = vtk.vtkPoints()
Vertices = vtk.vtkCellArray()
for c in self.cell_coordinates():
@ -926,7 +1093,7 @@ class DADF5():
for i,inc in enumerate(self.iter_visible('increments')):
vtk_data = []
materialpoints_backup = self.visible['materialpoints'].copy()
materialpoints_backup = self.selection['materialpoints'].copy()
self.set_visible('materialpoints',False)
for label in (labels if isinstance(labels,list) else [labels]):
for p in self.iter_visible('con_physics'):
@ -957,7 +1124,7 @@ class DADF5():
self.set_visible('materialpoints',materialpoints_backup)
constituents_backup = self.visible['constituents'].copy()
constituents_backup = self.selection['constituents'].copy()
self.set_visible('constituents',False)
for label in (labels if isinstance(labels,list) else [labels]):
for p in self.iter_visible('mat_physics'):
@ -984,7 +1151,7 @@ class DADF5():
vtk_geom.GetCellData().AddArray(vtk_data[-1])
self.set_visible('constituents',constituents_backup)
if mode=='Cell':
if mode.lower()=='cell':
writer = vtk.vtkXMLRectilinearGridWriter() if self.structured else \
vtk.vtkXMLUnstructuredGridWriter()
x = self.get_dataset_location('u_n')
@ -992,7 +1159,7 @@ class DADF5():
deep=True,array_type=vtk.VTK_DOUBLE))
vtk_data[-1].SetName('u')
vtk_geom.GetPointData().AddArray(vtk_data[-1])
elif mode == 'Point':
elif mode.lower()=='point':
writer = vtk.vtkXMLPolyDataWriter()

4
python/damask/environment.py
View File

@ -8,7 +8,7 @@ class Environment():
def __init__(self):
"""Read and provide values of DAMASK configuration."""
self.options = {}
self.get_options()
self.__get_options()
def relPath(self,relative = '.'):
return os.path.join(self.rootDir(),relative)
@ -16,7 +16,7 @@ class Environment():
def rootDir(self):
return os.path.normpath(os.path.join(os.path.realpath(__file__),'../../../'))
def get_options(self):
def __get_options(self):
for item in ['DAMASK_NUM_THREADS',
'MSC_ROOT',
'MARC_VERSION',

326
python/damask/mechanics.py
View File

@ -1,8 +1,8 @@
import numpy as np
def Cauchy(F,P):
def Cauchy(P,F):
"""
Return Cauchy stress calculated from 1. Piola-Kirchhoff stress and deformation gradient.
Return Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
Resulting tensor is symmetrized as the Cauchy stress needs to be symmetric.
@ -21,23 +21,179 @@ def Cauchy(F,P):
return symmetric(sigma)
def PK2(F,P):
def deviatoric_part(x):
"""
Return 2. Piola-Kirchhoff stress calculated from 1. Piola-Kirchhoff stress and deformation gradient.
Return deviatoric part of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the deviatoric part is computed.
"""
return x - np.eye(3)*spherical_part(x) if np.shape(x) == (3,3) else \
x - np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),[x.shape[0],3,3]),spherical_part(x))
def eigenvalues(x):
"""
Return the eigenvalues, i.e. principal components, of a symmetric tensor.
The eigenvalues are sorted in ascending order, each repeated according to
its multiplicity.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Symmetric tensor of which the eigenvalues are computed.
"""
return np.linalg.eigvalsh(symmetric(x))
def eigenvectors(x,RHS=False):
"""
Return eigenvectors of a symmetric tensor.
The eigenvalues are sorted in ascending order of their associated eigenvalues.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Symmetric tensor of which the eigenvectors are computed.
RHS: bool, optional
Enforce right-handed coordinate system. Default is False.
"""
(u,v) = np.linalg.eigh(symmetric(x))
if RHS:
if np.shape(x) == (3,3):
if np.linalg.det(v) < 0.0: v[:,2] *= -1.0
else:
v[np.linalg.det(v) < 0.0,:,2] *= -1.0
return v
def left_stretch(x):
"""
Return the left stretch of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the left stretch is computed.
"""
return __polar_decomposition(x,'V')[0]
def maximum_shear(x):
"""
Return the maximum shear component of a symmetric tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Symmetric tensor of which the maximum shear is computed.
"""
w = eigenvalues(x)
return (w[0] - w[2])*0.5 if np.shape(x) == (3,3) else \
(w[:,0] - w[:,2])*0.5
def Mises_strain(epsilon):
"""
Return the Mises equivalent of a strain tensor.
Parameters
----------
epsilon : numpy.array of shape (:,3,3) or (3,3)
Symmetric strain tensor of which the von Mises equivalent is computed.
"""
return __Mises(epsilon,2.0/3.0)
def Mises_stress(sigma):
"""
Return the Mises equivalent of a stress tensor.
Parameters
----------
sigma : numpy.array of shape (:,3,3) or (3,3)
Symmetric stress tensor of which the von Mises equivalent is computed.
"""
return __Mises(sigma,3.0/2.0)
def PK2(P,F):
"""
Calculate second Piola-Kirchhoff stress from first Piola-Kirchhoff stress and deformation gradient.
Parameters
----------
F : numpy.array of shape (:,3,3) or (3,3)
Deformation gradient.
P : numpy.array of shape (:,3,3) or (3,3)
1. Piola-Kirchhoff stress.
F : numpy.array of shape (:,3,3) or (3,3)
Deformation gradient.
"""
if np.shape(F) == np.shape(P) == (3,3):
S = np.dot(np.linalg.inv(F),P)
else:
S = np.einsum('ijk,ikl->ijl',np.linalg.inv(F),P)
return S
return symmetric(S)
def right_stretch(x):
"""
Return the right stretch of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the right stretch is computed.
"""
return __polar_decomposition(x,'U')[0]
def rotational_part(x):
"""
Return the rotational part of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the rotational part is computed.
"""
return __polar_decomposition(x,'R')[0]
def spherical_part(x,tensor=False):
"""
Return spherical (hydrostatic) part of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the hydrostatic part is computed.
tensor : bool, optional
Map spherical part onto identity tensor. Default is false
"""
if x.shape == (3,3):
sph = np.trace(x)/3.0
return sph if not tensor else np.eye(3)*sph
else:
sph = np.trace(x,axis1=1,axis2=2)/3.0
if not tensor:
return sph
else:
return np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),(x.shape[0],3,3)),sph)
def strain_tensor(F,t,m):
@ -78,73 +234,6 @@ def strain_tensor(F,t,m):
eps
def deviatoric_part(x):
"""
Return deviatoric part of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the deviatoric part is computed.
"""
return x - np.eye(3)*spherical_part(x) if np.shape(x) == (3,3) else \
x - np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),[x.shape[0],3,3]),spherical_part(x))
def spherical_part(x,tensor=False):
"""
Return spherical (hydrostatic) part of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the hydrostatic part is computed.
tensor : bool, optional
Map spherical part onto identity tensor. Default is false
"""
if x.shape == (3,3):
sph = np.trace(x)/3.0
return sph if not tensor else np.eye(3)*sph
else:
sph = np.trace(x,axis1=1,axis2=2)/3.0
if not tensor:
return sph
else:
return np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),(x.shape[0],3,3)),sph)
def Mises_stress(sigma):
"""
Return the Mises equivalent of a stress tensor.
Parameters
----------
sigma : numpy.array of shape (:,3,3) or (3,3)
Symmetric stress tensor of which the von Mises equivalent is computed.
"""
s = deviatoric_part(sigma)
return np.sqrt(3.0/2.0*(np.sum(s**2.0))) if np.shape(sigma) == (3,3) else \
np.sqrt(3.0/2.0*np.einsum('ijk->i',s**2.0))
def Mises_strain(epsilon):
"""
Return the Mises equivalent of a strain tensor.
Parameters
----------
epsilon : numpy.array of shape (:,3,3) or (3,3)
Symmetric strain tensor of which the von Mises equivalent is computed.
"""
s = deviatoric_part(epsilon)
return np.sqrt(2.0/3.0*(np.sum(s**2.0))) if np.shape(epsilon) == (3,3) else \
np.sqrt(2.0/3.0*np.einsum('ijk->i',s**2.0))
def symmetric(x):
"""
Return the symmetrized tensor.
@ -158,39 +247,6 @@ def symmetric(x):
return (x+transpose(x))*0.5
def maximum_shear(x):
"""
Return the maximum shear component of a symmetric tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Symmetric tensor of which the maximum shear is computed.
"""
w = np.linalg.eigvalsh(symmetric(x)) # eigenvalues in ascending order
return (w[2] - w[0])*0.5 if np.shape(x) == (3,3) else \
(w[:,2] - w[:,0])*0.5
def principal_components(x):
"""
Return the principal components of a symmetric tensor.
The principal components (eigenvalues) are sorted in descending order, each repeated according to
its multiplicity.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Symmetric tensor of which the principal compontents are computed.
"""
w = np.linalg.eigvalsh(symmetric(x)) # eigenvalues in ascending order
return w[::-1] if np.shape(x) == (3,3) else \
w[:,::-1]
def transpose(x):
"""
Return the transpose of a tensor.
@ -205,45 +261,6 @@ def transpose(x):
np.transpose(x,(0,2,1))
def rotational_part(x):
"""
Return the rotational part of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the rotational part is computed.
"""
return __polar_decomposition(x,'R')[0]
def left_stretch(x):
"""
Return the left stretch of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the left stretch is computed.
"""
return __polar_decomposition(x,'V')[0]
def right_stretch(x):
"""
Return the right stretch of a tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Tensor of which the right stretch is computed.
"""
return __polar_decomposition(x,'U')[0]
def __polar_decomposition(x,requested):
"""
Singular value decomposition.
@ -270,3 +287,20 @@ def __polar_decomposition(x,requested):
output.append(np.dot(R.T,x) if np.shape(x) == (3,3) else np.einsum('ikj,ikl->ijl',R,x))
return tuple(output)
def __Mises(x,s):
"""
Base equation for Mises equivalent of a stres or strain tensor.
Parameters
----------
x : numpy.array of shape (:,3,3) or (3,3)
Symmetric tensor of which the von Mises equivalent is computed.
s : float
Scaling factor (2/3 for strain, 3/2 for stress).
"""
d = deviatoric_part(x)
return np.sqrt(s*(np.sum(d**2.0))) if np.shape(x) == (3,3) else \
np.sqrt(s*np.einsum('ijk->i',d**2.0))

22
python/damask/util.py
View File

@ -3,10 +3,14 @@ import time
import os
import subprocess
import shlex
from fractions import Fraction
from functools import reduce
from optparse import Option
from queue import Queue
from threading import Thread
import numpy as np
class bcolors:
"""
ASCII Colors (Blender code).
@ -161,6 +165,24 @@ def progressBar(iteration, total, prefix='', bar_length=50):
sys.stderr.flush()
def scale_to_coprime(v):
"""Scale vector to co-prime (relatively prime) integers."""
MAX_DENOMINATOR = 1000
def get_square_denominator(x):
"""Denominator of the square of a number."""
return Fraction(x ** 2).limit_denominator(MAX_DENOMINATOR).denominator
def lcm(a, b):
"""Least common multiple."""
return a * b // np.gcd(a, b)
denominators = [int(get_square_denominator(i)) for i in v]
s = reduce(lcm, denominators) ** 0.5
m = (np.array(v)*s).astype(np.int)
return m//reduce(np.gcd,m)
class return_message():
"""Object with formatted return message."""

99
python/tests/test_DADF5.py
View File

@ -40,7 +40,7 @@ class TestDADF5:
assert np.allclose(in_memory,in_file)
def test_add_calculation(self,default):
default.add_calculation('2.0*np.abs(#F#)-1.0','x','-','test')
default.add_calculation('x','2.0*np.abs(#F#)-1.0','-','my notes')
loc = {'F': default.get_dataset_location('F'),
'x': default.get_dataset_location('x')}
in_memory = 2.0*np.abs(default.read_dataset(loc['F'],0))-1.0
@ -52,8 +52,8 @@ class TestDADF5:
loc = {'F': default.get_dataset_location('F'),
'P': default.get_dataset_location('P'),
'sigma':default.get_dataset_location('sigma')}
in_memory = mechanics.Cauchy(default.read_dataset(loc['F'],0),
default.read_dataset(loc['P'],0))
in_memory = mechanics.Cauchy(default.read_dataset(loc['P'],0),
default.read_dataset(loc['F'],0))
in_file = default.read_dataset(loc['sigma'],0)
assert np.allclose(in_memory,in_file)
@ -73,6 +73,54 @@ class TestDADF5:
in_file = default.read_dataset(loc['s_P'],0)
assert np.allclose(in_memory,in_file)
def test_add_eigenvalues(self,default):
default.add_Cauchy('P','F')
default.add_eigenvalues('sigma')
loc = {'sigma' :default.get_dataset_location('sigma'),
'lambda(sigma)':default.get_dataset_location('lambda(sigma)')}
in_memory = mechanics.eigenvalues(default.read_dataset(loc['sigma'],0))
in_file = default.read_dataset(loc['lambda(sigma)'],0)
assert np.allclose(in_memory,in_file)
def test_add_eigenvectors(self,default):
default.add_Cauchy('P','F')
default.add_eigenvectors('sigma')
loc = {'sigma' :default.get_dataset_location('sigma'),
'v(sigma)':default.get_dataset_location('v(sigma)')}
in_memory = mechanics.eigenvectors(default.read_dataset(loc['sigma'],0))
in_file = default.read_dataset(loc['v(sigma)'],0)
assert np.allclose(in_memory,in_file)
def test_add_maximum_shear(self,default):
default.add_Cauchy('P','F')
default.add_maximum_shear('sigma')
loc = {'sigma' :default.get_dataset_location('sigma'),
'max_shear(sigma)':default.get_dataset_location('max_shear(sigma)')}
in_memory = mechanics.maximum_shear(default.read_dataset(loc['sigma'],0)).reshape(-1,1)
in_file = default.read_dataset(loc['max_shear(sigma)'],0)
assert np.allclose(in_memory,in_file)
def test_add_Mises_strain(self,default):
t = ['V','U'][np.random.randint(0,2)]
m = np.random.random()*2.0 - 1.0
default.add_strain_tensor('F',t,m)
label = 'epsilon_{}^{}(F)'.format(t,m)
default.add_Mises(label)
loc = {label :default.get_dataset_location(label),
label+'_vM':default.get_dataset_location(label+'_vM')}
in_memory = mechanics.Mises_strain(default.read_dataset(loc[label],0)).reshape(-1,1)
in_file = default.read_dataset(loc[label+'_vM'],0)
assert np.allclose(in_memory,in_file)
def test_add_Mises_stress(self,default):
default.add_Cauchy('P','F')
default.add_Mises('sigma')
loc = {'sigma' :default.get_dataset_location('sigma'),
'sigma_vM':default.get_dataset_location('sigma_vM')}
in_memory = mechanics.Mises_stress(default.read_dataset(loc['sigma'],0)).reshape(-1,1)
in_file = default.read_dataset(loc['sigma_vM'],0)
assert np.allclose(in_memory,in_file)
def test_add_norm(self,default):
default.add_norm('F',1)
loc = {'F': default.get_dataset_location('F'),
@ -81,6 +129,24 @@ class TestDADF5:
in_file = default.read_dataset(loc['|F|_1'],0)
assert np.allclose(in_memory,in_file)
def test_add_PK2(self,default):
default.add_PK2('P','F')
loc = {'F':default.get_dataset_location('F'),
'P':default.get_dataset_location('P'),
'S':default.get_dataset_location('S')}
in_memory = mechanics.PK2(default.read_dataset(loc['P'],0),
default.read_dataset(loc['F'],0))
in_file = default.read_dataset(loc['S'],0)
assert np.allclose(in_memory,in_file)
def test_add_rotational_part(self,default):
default.add_rotational_part('F')
loc = {'F': default.get_dataset_location('F'),
'R(F)': default.get_dataset_location('R(F)')}
in_memory = mechanics.rotational_part(default.read_dataset(loc['F'],0))
in_file = default.read_dataset(loc['R(F)'],0)
assert np.allclose(in_memory,in_file)
def test_add_spherical(self,default):
default.add_spherical('P')
loc = {'P': default.get_dataset_location('P'),
@ -88,3 +154,30 @@ class TestDADF5:
in_memory = mechanics.spherical_part(default.read_dataset(loc['P'],0)).reshape(-1,1)
in_file = default.read_dataset(loc['p_P'],0)
assert np.allclose(in_memory,in_file)
def test_add_strain(self,default):
t = ['V','U'][np.random.randint(0,2)]
m = np.random.random()*2.0 - 1.0
default.add_strain_tensor('F',t,m)
label = 'epsilon_{}^{}(F)'.format(t,m)
loc = {'F': default.get_dataset_location('F'),
label: default.get_dataset_location(label)}
in_memory = mechanics.strain_tensor(default.read_dataset(loc['F'],0),t,m)
in_file = default.read_dataset(loc[label],0)
assert np.allclose(in_memory,in_file)
def test_add_stretch_right(self,default):
default.add_stretch_tensor('F','U')
loc = {'F': default.get_dataset_location('F'),
'U(F)': default.get_dataset_location('U(F)')}
in_memory = mechanics.right_stretch(default.read_dataset(loc['F'],0))
in_file = default.read_dataset(loc['U(F)'],0)
assert np.allclose(in_memory,in_file)
def test_add_stretch_left(self,default):
default.add_stretch_tensor('F','V')
loc = {'F': default.get_dataset_location('F'),
'V(F)': default.get_dataset_location('V(F)')}
in_memory = mechanics.left_stretch(default.read_dataset(loc['F'],0))
in_file = default.read_dataset(loc['V(F)'],0)
assert np.allclose(in_memory,in_file)

155
python/tests/test_mechanics.py
View File

@ -10,9 +10,64 @@ class TestMechanics:
def test_vectorize_Cauchy(self):
P = np.random.random((self.n,3,3))
F = np.random.random((self.n,3,3))
assert np.allclose(mechanics.Cauchy(F,P)[self.c],
mechanics.Cauchy(F[self.c],P[self.c]))
assert np.allclose(mechanics.Cauchy(P,F)[self.c],
mechanics.Cauchy(P[self.c],F[self.c]))
def test_vectorize_deviatoric_part(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.deviatoric_part(x)[self.c],
mechanics.deviatoric_part(x[self.c]))
def test_vectorize_eigenvalues(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.eigenvalues(x)[self.c],
mechanics.eigenvalues(x[self.c]))
def test_vectorize_eigenvectors(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.eigenvectors(x)[self.c],
mechanics.eigenvectors(x[self.c]))
def test_vectorize_left_stretch(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.left_stretch(x)[self.c],
mechanics.left_stretch(x[self.c]))
def test_vectorize_maximum_shear(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.maximum_shear(x)[self.c],
mechanics.maximum_shear(x[self.c]))
def test_vectorize_Mises_strain(self):
epsilon = np.random.random((self.n,3,3))
assert np.allclose(mechanics.Mises_strain(epsilon)[self.c],
mechanics.Mises_strain(epsilon[self.c]))
def test_vectorize_Mises_stress(self):
sigma = np.random.random((self.n,3,3))
assert np.allclose(mechanics.Mises_stress(sigma)[self.c],
mechanics.Mises_stress(sigma[self.c]))
def test_vectorize_PK2(self):
F = np.random.random((self.n,3,3))
P = np.random.random((self.n,3,3))
assert np.allclose(mechanics.PK2(P,F)[self.c],
mechanics.PK2(P[self.c],F[self.c]))
def test_vectorize_right_stretch(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.right_stretch(x)[self.c],
mechanics.right_stretch(x[self.c]))
def test_vectorize_rotational_part(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.rotational_part(x)[self.c],
mechanics.rotational_part(x[self.c]))
def test_vectorize_spherical_part(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.spherical_part(x,True)[self.c],
mechanics.spherical_part(x[self.c],True))
def test_vectorize_strain_tensor(self):
F = np.random.random((self.n,3,3))
@ -21,79 +76,24 @@ class TestMechanics:
assert np.allclose(mechanics.strain_tensor(F,t,m)[self.c],
mechanics.strain_tensor(F[self.c],t,m))
def test_vectorize_deviatoric_part(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.deviatoric_part(x)[self.c],
mechanics.deviatoric_part(x[self.c]))
def test_vectorize_spherical_part(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.spherical_part(x,True)[self.c],
mechanics.spherical_part(x[self.c],True))
def test_vectorize_Mises_stress(self):
sigma = np.random.random((self.n,3,3))
assert np.allclose(mechanics.Mises_stress(sigma)[self.c],
mechanics.Mises_stress(sigma[self.c]))
def test_vectorize_Mises_strain(self):
epsilon = np.random.random((self.n,3,3))
assert np.allclose(mechanics.Mises_strain(epsilon)[self.c],
mechanics.Mises_strain(epsilon[self.c]))
def test_vectorize_symmetric(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.symmetric(x)[self.c],
mechanics.symmetric(x[self.c]))
def test_vectorize_maximum_shear(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.maximum_shear(x)[self.c],
mechanics.maximum_shear(x[self.c]))
def test_vectorize_principal_components(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.principal_components(x)[self.c],
mechanics.principal_components(x[self.c]))
def test_vectorize_transpose(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.transpose(x)[self.c],
mechanics.transpose(x[self.c]))
def test_vectorize_rotational_part(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.rotational_part(x)[self.c],
mechanics.rotational_part(x[self.c]))
def test_vectorize_left_stretch(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.left_stretch(x)[self.c],
mechanics.left_stretch(x[self.c]))
def test_vectorize_right_stretch(self):
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.right_stretch(x)[self.c],
mechanics.right_stretch(x[self.c]))
def test_Cauchy(self):
"""Ensure Cauchy stress is symmetrized 1. Piola-Kirchhoff stress for no deformation."""
P = np.random.random((self.n,3,3))
assert np.allclose(mechanics.Cauchy(np.broadcast_to(np.eye(3),(self.n,3,3)),P),
assert np.allclose(mechanics.Cauchy(P,np.broadcast_to(np.eye(3),(self.n,3,3))),
mechanics.symmetric(P))
def test_polar_decomposition(self):
"""F = RU = VR."""
F = np.broadcast_to(np.eye(3),[self.n,3,3])*np.random.random((self.n,3,3))
@ -104,6 +104,13 @@ class TestMechanics:
np.matmul(V,R))
def test_PK2(self):
"""Ensure 2. Piola-Kirchhoff stress is symmetrized 1. Piola-Kirchhoff stress for no deformation."""
P = np.random.random((self.n,3,3))
assert np.allclose(mechanics.PK2(P,np.broadcast_to(np.eye(3),(self.n,3,3))),
mechanics.symmetric(P))
def test_strain_tensor_no_rotation(self):
"""Ensure that left and right stretch give same results for no rotation."""
F = np.broadcast_to(np.eye(3),[self.n,3,3])*np.random.random((self.n,3,3))
@ -186,3 +193,33 @@ class TestMechanics:
x = np.random.random((self.n,3,3))
assert np.allclose(mechanics.Mises_stress(x)/mechanics.Mises_strain(x),
1.5)
def test_eigenvalues(self):
"""Ensure that the characteristic polynomial can be solved."""
A = mechanics.symmetric(np.random.random((self.n,3,3)))
lambd = mechanics.eigenvalues(A)
s = np.random.randint(self.n)
for i in range(3):
assert np.allclose(np.linalg.det(A[s]-lambd[s,i]*np.eye(3)),.0)
def test_eigenvalues_and_vectors(self):
"""Ensure that eigenvalues and -vectors are the solution to the characteristic polynomial."""
A = mechanics.symmetric(np.random.random((self.n,3,3)))
lambd = mechanics.eigenvalues(A)
x = mechanics.eigenvectors(A)
s = np.random.randint(self.n)
for i in range(3):
assert np.allclose(np.dot(A[s]-lambd[s,i]*np.eye(3),x[s,:,i]),.0)
def test_eigenvectors_RHS(self):
"""Ensure that RHS coordinate system does only change sign of determinant."""
A = mechanics.symmetric(np.random.random((self.n,3,3)))
LRHS = np.linalg.det(mechanics.eigenvectors(A,RHS=False))
RHS = np.linalg.det(mechanics.eigenvectors(A,RHS=True))
assert np.allclose(np.abs(LRHS),RHS)
def test_spherical_no_shear(self):
"""Ensure that sherical stress has max shear of 0.0."""
A = mechanics.spherical_part(mechanics.symmetric(np.random.random((self.n,3,3))),True)
assert np.allclose(mechanics.maximum_shear(A),0.0)