import multiprocessing
import re
import glob
import os
from functools import partial
import vtk
from vtk.util import numpy_support
import h5py
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 Result:
"""
Read and write to DADF5 files.
DADF5 (DAKMASK HDF5) files contain DAMASK results.
"""
def __init__(self,fname):
"""
Opens an existing DADF5 file.
Parameters
----------
fname : str
name of the DADF5 file to be openend.
"""
with h5py.File(fname,'r') as f:
try:
self.version_major = f.attrs['DADF5_version_major']
self.version_minor = f.attrs['DADF5_version_minor']
except KeyError:
self.version_major = f.attrs['DADF5-major']
self.version_minor = f.attrs['DADF5-minor']
if self.version_major != 0 or not 2 <= self.version_minor <= 6:
raise TypeError('Unsupported DADF5 version {}.{} '.format(self.version_major,
self.version_minor))
self.structured = 'grid' in f['geometry'].attrs.keys()
if self.structured:
self.grid = f['geometry'].attrs['grid']
self.size = f['geometry'].attrs['size']
self.origin = f['geometry'].attrs['origin'] if self.version_major == 0 and self.version_minor >= 5 else \
np.zeros(3)
r=re.compile('inc[0-9]+')
increments_unsorted = {int(i[3:]):i for i in f.keys() if r.match(i)}
self.increments = [increments_unsorted[i] for i in sorted(increments_unsorted)]
self.times = [round(f[i].attrs['time/s'],12) for i in self.increments]
self.Nmaterialpoints, self.Nconstituents = np.shape(f['mapping/cellResults/constituent'])
self.materialpoints = [m.decode() for m in np.unique(f['mapping/cellResults/materialpoint']['Name'])]
self.constituents = [c.decode() for c in np.unique(f['mapping/cellResults/constituent'] ['Name'])]
self.con_physics = []
for c in self.constituents:
self.con_physics += f['/'.join([self.increments[0],'constituent',c])].keys()
self.con_physics = list(set(self.con_physics)) # make unique
self.mat_physics = []
for m in self.materialpoints:
self.mat_physics += f['/'.join([self.increments[0],'materialpoint',m])].keys()
self.mat_physics = list(set(self.mat_physics)) # make unique
self.selection= {'increments': self.increments,
'constituents': self.constituents,
'materialpoints': self.materialpoints,
'con_physics': self.con_physics,
'mat_physics': self.mat_physics}
self.fname = fname
def _manage_selection(self,action,what,datasets):
"""
Manages the visibility of the groups.
Parameters
----------
action : str
select from 'set', 'add', and 'del'
what : str
attribute to change (must be from self.selection)
datasets : list of str or Boolean
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
"""
# allow True/False and string arguments
if datasets is True:
datasets = ['*']
elif datasets is False:
datasets = []
choice = datasets if hasattr(datasets,'__iter__') and not isinstance(datasets,str) else \
[datasets]
if what == 'increments':
choice = [c if isinstance(c,str) and c.startswith('inc') else
'inc{}'.format(c) for c in choice]
elif what == 'times':
what = 'increments'
if choice == ['*']:
choice = self.increments
else:
iterator = map(float,choice)
choice = []
for c in iterator:
idx=np.searchsorted(self.times,c)
if np.isclose(c,self.times[idx]):
choice.append(self.increments[idx])
elif np.isclose(c,self.times[idx+1]):
choice.append(self.increments[idx+1])
valid = [e for e_ in [glob.fnmatch.filter(getattr(self,what),s) for s in choice] for e in e_]
existing = set(self.selection[what])
if action == 'set':
self.selection[what] = valid
elif action == 'add':
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':
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 incs_in_range(self,start,end):
selected = []
for i,inc in enumerate([int(i[3:]) for i in self.increments]):
s,e = map(lambda x: int(x[3:] if isinstance(x,str) and x.startswith('inc') else x), (start,end))
if s <= inc <= e:
selected.append(self.increments[i])
return selected
def times_in_range(self,start,end):
selected = []
for i,time in enumerate(self.times):
if start <= time <= end:
selected.append(self.increments[i])
return selected
def iter_selection(self,what):
"""
Iterate over selection items by setting each one selected.
Parameters
----------
what : str
attribute to change (must be from self.selection)
"""
datasets = self.selection[what]
last_datasets = datasets.copy()
for dataset in datasets:
if last_datasets != self.selection[what]:
self._manage_selection('set',what,datasets)
raise Exception
self._manage_selection('set',what,dataset)
last_datasets = self.selection[what]
yield dataset
self._manage_selection('set',what,datasets)
def pick(self,what,datasets):
"""
Set selection.
Parameters
----------
what : str
attribute to change (must be from self.selection)
datasets : list of str or Boolean
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
"""
self._manage_selection('set',what,datasets)
def pick_more(self,what,datasets):
"""
Add to selection.
Parameters
----------
what : str
attribute to change (must be from self.selection)
datasets : list of str or Boolean
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
"""
self._manage_selection('add',what,datasets)
def pick_less(self,what,datasets):
"""
Delete from selection.
Parameters
----------
what : str
attribute to change (must be from self.selection)
datasets : list of str or Boolean
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
"""
self._manage_selection('del',what,datasets)
def groups_with_datasets(self,datasets):
"""
Return groups that contain all requested datasets.
Only groups within
- inc?????/constituent/*_*/*
- inc?????/materialpoint/*_*/*
- inc?????/geometry/*
are considered as they contain user-relevant data.
Single strings will be treated as list with one entry.
Wild card matching is allowed, but the number of arguments need to fit.
Parameters
----------
datasets : iterable or str or Boolean
Examples
--------
datasets = False matches no group
datasets = True matches all groups
datasets = ['F','P'] matches a group with ['F','P','sigma']
datasets = ['*','P'] matches a group with ['F','P']
datasets = ['*'] does not match a group with ['F','P','sigma']
datasets = ['*','*'] does not match a group with ['F','P','sigma']
datasets = ['*','*','*'] matches a group with ['F','P','sigma']
"""
if datasets is False: return []
sets = datasets if isinstance(datasets,bool) or (hasattr(datasets,'__iter__') and not isinstance(datasets,str)) else \
[datasets]
groups = []
with h5py.File(self.fname,'r') as f:
for i in self.iter_selection('increments'):
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
for oo in self.iter_selection(o):
for pp in self.iter_selection(p):
group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue
if sets is True:
groups.append(group)
else:
match = [e for e_ in [glob.fnmatch.filter(f[group].keys(),s) for s in sets] for e in e_]
if len(set(match)) == len(sets) : groups.append(group)
return groups
def list_data(self):
"""Return information on all active datasets in the file."""
message = ''
with h5py.File(self.fname,'r') as f:
for i in self.iter_selection('increments'):
message+='\n{} ({}s)\n'.format(i,self.times[self.increments.index(i)])
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
for oo in self.iter_selection(o):
message+=' {}\n'.format(oo)
for pp in self.iter_selection(p):
message+=' {}\n'.format(pp)
group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue
for d in f[group].keys():
try:
dataset = f['/'.join([group,d])]
message+=' {} / ({}): {}\n'.\
format(d,dataset.attrs['Unit'].decode(),dataset.attrs['Description'].decode())
except KeyError:
pass
return message
def get_dataset_location(self,label):
"""Return the location of all active datasets with given label."""
path = []
with h5py.File(self.fname,'r') as f:
for i in self.iter_selection('increments'):
k = '/'.join([i,'geometry',label])
try:
f[k]
path.append(k)
except KeyError as e:
pass
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
for oo in self.iter_selection(o):
for pp in self.iter_selection(p):
k = '/'.join([i,o[:-1],oo,pp,label])
try:
f[k]
path.append(k)
except KeyError as e:
pass
return path
def get_constituent_ID(self,c=0):
"""Pointwise constituent ID."""
with h5py.File(self.fname,'r') as f:
names = f['/mapping/cellResults/constituent']['Name'][:,c].astype('str')
return np.array([int(n.split('_')[0]) for n in names.tolist()],dtype=np.int32)
def get_crystal_structure(self): # ToDo: extension to multi constituents/phase
"""Info about the crystal structure."""
with h5py.File(self.fname,'r') as f:
return f[self.get_dataset_location('orientation')[0]].attrs['Lattice'].astype('str') # np.bytes_ to string
def read_dataset(self,path,c=0,plain=False):
"""
Dataset for all points/cells.
If more than one path is given, the dataset is composed of the individual contributions.
"""
with h5py.File(self.fname,'r') as f:
shape = (self.Nmaterialpoints,) + np.shape(f[path[0]])[1:]
if len(shape) == 1: shape = shape +(1,)
dataset = np.full(shape,np.nan,dtype=np.dtype(f[path[0]]))
for pa in path:
label = pa.split('/')[2]
if (pa.split('/')[1] == 'geometry'):
dataset = np.array(f[pa])
continue
p = np.where(f['mapping/cellResults/constituent'][:,c]['Name'] == str.encode(label))[0]
if len(p)>0:
u = (f['mapping/cellResults/constituent']['Position'][p,c])
a = np.array(f[pa])
if len(a.shape) == 1:
a=a.reshape([a.shape[0],1])
dataset[p,:] = a[u,:]
p = np.where(f['mapping/cellResults/materialpoint']['Name'] == str.encode(label))[0]
if len(p)>0:
u = (f['mapping/cellResults/materialpoint']['Position'][p.tolist()])
a = np.array(f[pa])
if len(a.shape) == 1:
a=a.reshape([a.shape[0],1])
dataset[p,:] = a[u,:]
if plain and dataset.dtype.names is not None:
return dataset.view(('float64',len(dataset.dtype.names)))
else:
return dataset
def cell_coordinates(self):
"""Return initial coordinates of the cell centers."""
if self.structured:
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'][()]
@staticmethod
def _add_absolute(x):
return {
'data': np.abs(x['data']),
'label': '|{}|'.format(x['label']),
'meta': {
'Unit': x['meta']['Unit'],
'Description': 'Absolute value of {} ({})'.format(x['label'],x['meta']['Description']),
'Creator': 'result.py:add_abs v{}'.format(version)
}
}
def add_absolute(self,x):
"""
Add absolute value.
Parameters
----------
x : str
Label of scalar, vector, or tensor dataset to take absolute value of.
"""
self._add_generic_pointwise(self._add_absolute,{'x':x})
@staticmethod
def _add_calculation(**kwargs):
formula = kwargs['formula']
for d in re.findall(r'#(.*?)#',formula):
formula = formula.replace('#{}#'.format(d),"kwargs['{}']['data']".format(d))
return {
'data': eval(formula),
'label': kwargs['label'],
'meta': {
'Unit': kwargs['unit'],
'Description': '{} (formula: {})'.format(kwargs['description'],kwargs['formula']),
'Creator': 'result.py:add_calculation v{}'.format(version)
}
}
def add_calculation(self,label,formula,unit='n/a',description=None,vectorized=True):
"""
Add result of a general formula.
Parameters
----------
label : str
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.
vectorized : bool, optional
Indicate whether the formula can be used in vectorized form. Defaults to ‘True’.
"""
if not vectorized:
raise NotImplementedError
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(self._add_calculation,dataset_mapping,args)
@staticmethod
def _add_Cauchy(P,F):
return {
'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 {} ({})'.format(F['label'],F['meta']['Description']),
'Creator': 'result.py:add_Cauchy v{}'.format(version)
}
}
def add_Cauchy(self,P='P',F='F'):
"""
Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
Parameters
----------
P : str, optional
Label of the dataset containing the first Piola-Kirchhoff stress. Defaults to ‘P’.
F : str, optional
Label of the dataset containing the deformation gradient. Defaults to ‘F’.
"""
self._add_generic_pointwise(self._add_Cauchy,{'P':P,'F':F})
@staticmethod
def _add_determinant(T):
return {
'data': np.linalg.det(T['data']),
'label': 'det({})'.format(T['label']),
'meta': {
'Unit': T['meta']['Unit'],
'Description': 'Determinant of tensor {} ({})'.format(T['label'],T['meta']['Description']),
'Creator': 'result.py:add_determinant v{}'.format(version)
}
}
def add_determinant(self,T):
"""
Add the determinant of a tensor.
Parameters
----------
T : str
Label of tensor dataset.
"""
self._add_generic_pointwise(self._add_determinant,{'T':T})
@staticmethod
def _add_deviator(T):
if not T['data'].shape[1:] == (3,3):
raise ValueError
return {
'data': mechanics.deviatoric_part(T['data']),
'label': 's_{}'.format(T['label']),
'meta': {
'Unit': T['meta']['Unit'],
'Description': 'Deviator of tensor {} ({})'.format(T['label'],T['meta']['Description']),
'Creator': 'result.py:add_deviator v{}'.format(version)
}
}
def add_deviator(self,T):
"""
Add the deviatoric part of a tensor.
Parameters
----------
T : str
Label of tensor dataset.
"""
self._add_generic_pointwise(self._add_deviator,{'T':T})
@staticmethod
def _add_eigenvalue(T_sym):
return {
'data': mechanics.eigenvalues(T_sym['data']),
'label': 'lambda({})'.format(T_sym['label']),
'meta' : {
'Unit': T_sym['meta']['Unit'],
'Description': 'Eigenvalues of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
'Creator': 'result.py:add_eigenvalues v{}'.format(version)
}
}
def add_eigenvalues(self,T_sym):
"""
Add eigenvalues of symmetric tensor.
Parameters
----------
T_sym : str
Label of symmetric tensor dataset.
"""
self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym})
@staticmethod
def _add_eigenvector(T_sym):
return {
'data': mechanics.eigenvectors(T_sym['data']),
'label': 'v({})'.format(T_sym['label']),
'meta' : {
'Unit': '1',
'Description': 'Eigenvectors of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
'Creator': 'result.py:add_eigenvectors v{}'.format(version)
}
}
def add_eigenvectors(self,T_sym):
"""
Add eigenvectors of symmetric tensor.
Parameters
----------
T_sym : str
Label of symmetric tensor dataset.
"""
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym})
@staticmethod
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': 'result.py:add_IPFcolor v{}'.format(version)
}
}
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.
"""
self._add_generic_pointwise(self._add_IPFcolor,{'q':q},{'l':l})
@staticmethod
def _add_maximum_shear(T_sym):
return {
'data': mechanics.maximum_shear(T_sym['data']),
'label': 'max_shear({})'.format(T_sym['label']),
'meta': {
'Unit': T_sym['meta']['Unit'],
'Description': 'Maximum shear component of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
'Creator': 'result.py:add_maximum_shear v{}'.format(version)
}
}
def add_maximum_shear(self,T_sym):
"""
Add maximum shear components of symmetric tensor.
Parameters
----------
T_sym : str
Label of symmetric tensor dataset.
"""
self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym})
@staticmethod
def _add_Mises(T_sym):
t = 'strain' if T_sym['meta']['Unit'] == '1' else \
'stress'
return {
'data': mechanics.Mises_strain(T_sym['data']) if t=='strain' else mechanics.Mises_stress(T_sym['data']),
'label': '{}_vM'.format(T_sym['label']),
'meta': {
'Unit': T_sym['meta']['Unit'],
'Description': 'Mises equivalent {} of {} ({})'.format(t,T_sym['label'],T_sym['meta']['Description']),
'Creator': 'result.py:add_Mises v{}'.format(version)
}
}
def add_Mises(self,T_sym):
"""
Add the equivalent Mises stress or strain of a symmetric tensor.
Parameters
----------
T_sym : str
Label of symmetric tensorial stress or strain dataset.
"""
self._add_generic_pointwise(self._add_Mises,{'T_sym':T_sym})
@staticmethod
def _add_norm(x,ord):
o = ord
if len(x['data'].shape) == 2:
axis = 1
t = 'vector'
if o is None: o = 2
elif len(x['data'].shape) == 3:
axis = (1,2)
t = 'tensor'
if o is None: o = 'fro'
else:
raise ValueError
return {
'data': np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True),
'label': '|{}|_{}'.format(x['label'],o),
'meta': {
'Unit': x['meta']['Unit'],
'Description': '{}-norm of {} {} ({})'.format(o,t,x['label'],x['meta']['Description']),
'Creator': 'result.py:add_norm v{}'.format(version)
}
}
def add_norm(self,x,ord=None):
"""
Add the norm of vector or tensor.
Parameters
----------
x : str
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.
"""
self._add_generic_pointwise(self._add_norm,{'x':x},{'ord':ord})
@staticmethod
def _add_PK2(P,F):
return {
'data': mechanics.PK2(P['data'],F['data']),
'label': 'S',
'meta': {
'Unit': P['meta']['Unit'],
'Description': '2. Kirchhoff stress calculated from {} ({}) '.format(P['label'],
P['meta']['Description'])+\
'and {} ({})'.format(F['label'],F['meta']['Description']),
'Creator': 'result.py:add_PK2 v{}'.format(version)
}
}
def add_PK2(self,P='P',F='F'):
"""
Add second Piola-Kirchhoff calculated from first Piola-Kirchhoff stress and deformation gradient.
Parameters
----------
P : str, optional
Label first Piola-Kirchhoff stress dataset. Defaults to ‘P’.
F : str, optional
Label of deformation gradient dataset. Defaults to ‘F’.
"""
self._add_generic_pointwise(self._add_PK2,{'P':P,'F':F})
@staticmethod
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' : 'result.py:add_pole v{}'.format(version)
}
}
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.
"""
self._add_generic_pointwise(self._add_pole,{'q':q},{'p':p,'polar':polar})
@staticmethod
def _add_rotational_part(F):
if not F['data'].shape[1:] == (3,3):
raise ValueError
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': 'result.py:add_rotational_part v{}'.format(version)
}
}
def add_rotational_part(self,F):
"""
Add rotational part of a deformation gradient.
Parameters
----------
F : str, optional
Label of deformation gradient dataset.
"""
self._add_generic_pointwise(self._add_rotational_part,{'F':F})
@staticmethod
def _add_spherical(T):
if not T['data'].shape[1:] == (3,3):
raise ValueError
return {
'data': mechanics.spherical_part(T['data']),
'label': 'p_{}'.format(T['label']),
'meta': {
'Unit': T['meta']['Unit'],
'Description': 'Spherical component of tensor {} ({})'.format(T['label'],T['meta']['Description']),
'Creator': 'result.py:add_spherical v{}'.format(version)
}
}
def add_spherical(self,T):
"""
Add the spherical (hydrostatic) part of a tensor.
Parameters
----------
T : str
Label of tensor dataset.
"""
self._add_generic_pointwise(self._add_spherical,{'T':T})
@staticmethod
def _add_strain_tensor(F,t,m):
if not F['data'].shape[1:] == (3,3):
raise ValueError
return {
'data': mechanics.strain_tensor(F['data'],t,m),
'label': 'epsilon_{}^{}({})'.format(t,m,F['label']),
'meta': {
'Unit': F['meta']['Unit'],
'Description': 'Strain tensor of {} ({})'.format(F['label'],F['meta']['Description']),
'Creator': 'result.py:add_strain_tensor v{}'.format(version)
}
}
def add_strain_tensor(self,F='F',t='V',m=0.0):
"""
Add strain tensor of a deformation gradient.
For details refer to damask.mechanics.strain_tensor
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’.
m : float, optional
Order of the strain calculation. Defaults to ‘0.0’.
"""
self._add_generic_pointwise(self._add_strain_tensor,{'F':F},{'t':t,'m':m})
@staticmethod
def _add_stretch_tensor(F,t):
if not F['data'].shape[1:] == (3,3):
raise ValueError
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': 'result.py:add_stretch_tensor v{}'.format(version)
}
}
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’.
"""
self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
def _job(self,group,func,datasets,args,lock):
"""Execute job for _add_generic_pointwise."""
try:
datasets_in = {}
lock.acquire()
with h5py.File(self.fname,'r') as f:
for arg,label in datasets.items():
loc = f[group+'/'+label]
datasets_in[arg]={'data' :loc[()],
'label':label,
'meta': {k:v.decode() for k,v in loc.attrs.items()}}
lock.release()
r = func(**datasets_in,**args)
return [group,r]
except Exception as err:
print('Error during calculation: {}.'.format(err))
return None
def _add_generic_pointwise(self,func,datasets,args={}):
"""
General function to add pointwise data.
Parameters
----------
func : function
Callback function that calculates a new dataset from one or more datasets per HDF5 group.
datasets : dictionary
Details of the datasets to be used: label (in HDF5 file) and arg (argument to which the data is parsed in func).
args : dictionary, optional
Arguments parsed to func.
"""
pool = multiprocessing.Pool(int(Environment().options['DAMASK_NUM_THREADS']))
lock = multiprocessing.Manager().Lock()
groups = self.groups_with_datasets(datasets.values())
default_arg = partial(self._job,func=func,datasets=datasets,args=args,lock=lock)
for result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):
if not result:
continue
lock.acquire()
with h5py.File(self.fname, 'a') as f:
try:
dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'])
for l,v in result[1]['meta'].items():
dataset.attrs[l]=v.encode()
except OSError as err:
print('Could not add dataset: {}.'.format(err))
lock.release()
pool.close()
pool.join()
def to_vtk(self,labels,mode='cell'):
"""
Export to vtk cell/point data.
Parameters
----------
labels : str or list of
Labels of the datasets to be exported.
mode : str, either 'cell' or 'point'
Export in cell format or point format.
Defaults to 'cell'.
"""
if mode.lower()=='cell':
if self.structured:
coordArray = [vtk.vtkDoubleArray(),vtk.vtkDoubleArray(),vtk.vtkDoubleArray()]
for dim in [0,1,2]:
for c in np.linspace(0,self.size[dim],1+self.grid[dim]):
coordArray[dim].InsertNextValue(c)
vtk_geom = vtk.vtkRectilinearGrid()
vtk_geom.SetDimensions(*(self.grid+1))
vtk_geom.SetXCoordinates(coordArray[0])
vtk_geom.SetYCoordinates(coordArray[1])
vtk_geom.SetZCoordinates(coordArray[2])
else:
nodes = vtk.vtkPoints()
with h5py.File(self.fname,'r') as f:
nodes.SetData(numpy_support.numpy_to_vtk(f['/geometry/x_n'][()],deep=True))
vtk_geom = vtk.vtkUnstructuredGrid()
vtk_geom.SetPoints(nodes)
vtk_geom.Allocate(f['/geometry/T_c'].shape[0])
if self.version_major == 0 and self.version_minor <= 5:
vtk_type = vtk.VTK_HEXAHEDRON
n_nodes = 8
else:
if f['/geometry/T_c'].attrs['VTK_TYPE'] == b'TRIANGLE':
vtk_type = vtk.VTK_TRIANGLE
n_nodes = 3
elif f['/geometry/T_c'].attrs['VTK_TYPE'] == b'QUAD':
vtk_type = vtk.VTK_QUAD
n_nodes = 4
elif f['/geometry/T_c'].attrs['VTK_TYPE'] == b'TETRA': # not tested
vtk_type = vtk.VTK_TETRA
n_nodes = 4
elif f['/geometry/T_c'].attrs['VTK_TYPE'] == b'HEXAHEDRON':
vtk_type = vtk.VTK_HEXAHEDRON
n_nodes = 8
for i in f['/geometry/T_c']:
vtk_geom.InsertNextCell(vtk_type,n_nodes,i-1)
elif mode.lower()=='point':
Points = vtk.vtkPoints()
Vertices = vtk.vtkCellArray()
for c in self.cell_coordinates():
pointID = Points.InsertNextPoint(c)
Vertices.InsertNextCell(1)
Vertices.InsertCellPoint(pointID)
vtk_geom = vtk.vtkPolyData()
vtk_geom.SetPoints(Points)
vtk_geom.SetVerts(Vertices)
vtk_geom.Modified()
N_digits = int(np.floor(np.log10(int(self.increments[-1][3:]))))+1
for i,inc in enumerate(self.iter_selection('increments')):
vtk_data = []
materialpoints_backup = self.selection['materialpoints'].copy()
self.pick('materialpoints',False)
for label in (labels if isinstance(labels,list) else [labels]):
for p in self.iter_selection('con_physics'):
if p != 'generic':
for c in self.iter_selection('constituents'):
x = self.get_dataset_location(label)
if len(x) == 0:
continue
array = self.read_dataset(x,0)
shape = [array.shape[0],np.product(array.shape[1:])]
vtk_data.append(numpy_support.numpy_to_vtk(num_array=array.reshape(shape),deep=True))
vtk_data[-1].SetName('1_'+x[0].split('/',1)[1]) #ToDo: hard coded 1!
vtk_geom.GetCellData().AddArray(vtk_data[-1])
else:
x = self.get_dataset_location(label)
if len(x) == 0:
continue
array = self.read_dataset(x,0)
shape = [array.shape[0],np.product(array.shape[1:])]
vtk_data.append(numpy_support.numpy_to_vtk(num_array=array.reshape(shape),deep=True))
ph_name = re.compile(r'(?<=(constituent\/))(.*?)(?=(generic))') # identify phase name
dset_name = '1_' + re.sub(ph_name,r'',x[0].split('/',1)[1]) # removing phase name
vtk_data[-1].SetName(dset_name)
vtk_geom.GetCellData().AddArray(vtk_data[-1])
self.pick('materialpoints',materialpoints_backup)
constituents_backup = self.selection['constituents'].copy()
self.pick('constituents',False)
for label in (labels if isinstance(labels,list) else [labels]):
for p in self.iter_selection('mat_physics'):
if p != 'generic':
for m in self.iter_selection('materialpoints'):
x = self.get_dataset_location(label)
if len(x) == 0:
continue
array = self.read_dataset(x,0)
shape = [array.shape[0],np.product(array.shape[1:])]
vtk_data.append(numpy_support.numpy_to_vtk(num_array=array.reshape(shape),deep=True))
vtk_data[-1].SetName('1_'+x[0].split('/',1)[1]) #ToDo: why 1_?
vtk_geom.GetCellData().AddArray(vtk_data[-1])
else:
x = self.get_dataset_location(label)
if len(x) == 0:
continue
array = self.read_dataset(x,0)
shape = [array.shape[0],np.product(array.shape[1:])]
vtk_data.append(numpy_support.numpy_to_vtk(num_array=array.reshape(shape),deep=True))
vtk_data[-1].SetName('1_'+x[0].split('/',1)[1])
vtk_geom.GetCellData().AddArray(vtk_data[-1])
self.pick('constituents',constituents_backup)
if mode.lower()=='cell':
writer = vtk.vtkXMLRectilinearGridWriter() if self.structured else \
vtk.vtkXMLUnstructuredGridWriter()
x = self.get_dataset_location('u_n')
vtk_data.append(numpy_support.numpy_to_vtk(num_array=self.read_dataset(x,0),deep=True))
vtk_data[-1].SetName('u')
vtk_geom.GetPointData().AddArray(vtk_data[-1])
elif mode.lower()=='point':
writer = vtk.vtkXMLPolyDataWriter()
file_out = '{}_inc{}.{}'.format(os.path.splitext(os.path.basename(self.fname))[0],
inc[3:].zfill(N_digits),
writer.GetDefaultFileExtension())
writer.SetCompressorTypeToZLib()
writer.SetDataModeToBinary()
writer.SetFileName(file_out)
writer.SetInputData(vtk_geom)
writer.Write()
###################################################################################################
# BEGIN DEPRECATED
iter_visible = iter_selection
def _time_to_inc(self,start,end):
selected = []
for i,time in enumerate(self.times):
if start <= time <= end:
selected.append(self.increments[i])
return selected
def set_by_time(self,start,end):
"""
Set active increments based on start and end time.
Parameters
----------
start : float
start time (included)
end : float
end time (included)
"""
self._manage_selection('set','increments',self._time_to_inc(start,end))
def set_by_increment(self,start,end):
"""
Set active time increments based on start and end increment.
Parameters
----------
start : int
start increment (included)
end : int
end increment (included)
"""
if self.version_minor >= 4:
self._manage_selection('set','increments',[ 'inc{}'.format(i) for i in range(start,end+1)])
else:
self._manage_selection('set','increments',['inc{:05d}'.format(i) for i in range(start,end+1)])