import multiprocessing as mp
import re
import glob
import os
import datetime
import xml.etree.ElementTree as ET
import xml.dom.minidom
from pathlib import Path
from functools import partial
from collections import defaultdict
import h5py
import numpy as np
from numpy.lib import recfunctions as rfn
import damask
from . import VTK
from . import Table
from . import Orientation
from . import grid_filters
from . import mechanics
from . import tensor
from . import util
h5py3 = h5py.__version__[0] == '3'
class Result:
"""
Read and write to DADF5 files.
DADF5 (DAMASK HDF5) files contain DAMASK results.
"""
def __init__(self,fname):
"""
Open an existing DADF5 file.
Parameters
----------
fname : str or pathlib.Path
Name of the DADF5 file to be opened.
"""
with h5py.File(fname,'r') as f:
self.version_major = f.attrs['DADF5_version_major']
self.version_minor = f.attrs['DADF5_version_minor']
if self.version_major != 0 or not 7 <= self.version_minor <= 11:
raise TypeError(f'Unsupported DADF5 version {self.version_major}.{self.version_minor}')
self.structured = 'grid' in f['geometry'].attrs.keys() or \
'cells' in f['geometry'].attrs.keys()
if self.structured:
try:
self.cells = f['geometry'].attrs['cells']
except KeyError:
self.cells = f['geometry'].attrs['grid']
self.size = f['geometry'].attrs['size']
self.origin = f['geometry'].attrs['origin']
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.N_materialpoints, self.N_constituents = np.shape(f['mapping/phase'])
self.homogenizations = [m.decode() for m in np.unique(f['mapping/homogenization']['Name'])]
self.phases = [c.decode() for c in np.unique(f['mapping/phase']['Name'])]
self.out_type_ph = []
for c in self.phases:
self.out_type_ph += f['/'.join([self.increments[0],'phase',c])].keys()
self.out_type_ph = list(set(self.out_type_ph)) # make unique
self.out_type_ho = []
for m in self.homogenizations:
self.out_type_ho += f['/'.join([self.increments[0],'homogenization',m])].keys()
self.out_type_ho = list(set(self.out_type_ho)) # make unique
self.visible = {'increments': self.increments,
'phases': self.phases,
'homogenizations': self.homogenizations,
'out_type_ph': self.out_type_ph,
'out_type_ho': self.out_type_ho
}
self.fname = Path(fname).absolute()
self._allow_modification = False
def __repr__(self):
"""Show summary of file content."""
visible_increments = self.visible['increments']
self.view('increments',visible_increments[0:1])
first = self.list_data()
self.view('increments',visible_increments[-1:])
last = '' if len(visible_increments) < 2 else self.list_data()
self.view('increments',visible_increments)
in_between = '' if len(visible_increments) < 3 else \
''.join([f'\n{inc}\n ...\n' for inc in visible_increments[1:-2]])
return util.srepr(first + in_between + last)
def _manage_view(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 in self.visible).
datasets : list of str or bool
Name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
"""
def natural_sort(key):
convert = lambda text: int(text) if text.isdigit() else text
return [ convert(c) for c in re.split('([0-9]+)', key) ]
# 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
f'inc{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 idx >= len(self.times): continue
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.visible[what])
if action == 'set':
self.visible[what] = valid
elif action == 'add':
add = existing.union(valid)
add_sorted = sorted(add, key=natural_sort)
self.visible[what] = add_sorted
elif action == 'del':
diff = existing.difference(valid)
diff_sorted = sorted(diff, key=natural_sort)
self.visible[what] = diff_sorted
def _get_attribute(self,path,attr):
"""
Get the attribute of a dataset.
Parameters
----------
Path : str
Path to the dataset.
attr : str
Name of the attribute to get.
Returns
-------
attr at path, str or None.
The requested attribute, None if not found.
"""
with h5py.File(self.fname,'r') as f:
try:
return f[path].attrs[attr] if h5py3 else f[path].attrs[attr].decode()
except KeyError:
return None
def allow_modification(self):
"""Allow to overwrite existing data."""
print(util.warn('Warning: Modification of existing datasets allowed!'))
self._allow_modification = True
def disallow_modification(self):
"""Disllow to overwrite existing data (default case)."""
self._allow_modification = False
def incs_in_range(self,start,end):
"""
Select all increments within a given range.
Parameters
----------
start : int or str
Start increment.
end : int or str
End increment.
"""
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):
"""
Select all increments within a given time range.
Parameters
----------
start : float
Time of start increment.
end : float
Time of end increment.
"""
selected = []
for i,time in enumerate(self.times):
if start <= time <= end:
selected.append(self.times[i])
return selected
def iterate(self,what):
"""
Iterate over visible items and view them independently.
Parameters
----------
what : str
Attribute to change (must be from self.visible).
"""
datasets = self.visible[what]
last_view = datasets.copy()
for dataset in datasets:
if last_view != self.visible[what]:
self._manage_view('set',what,datasets)
raise Exception
self._manage_view('set',what,dataset)
last_view = self.visible[what]
yield dataset
self._manage_view('set',what,datasets)
def view(self,what,datasets):
"""
Set view.
Parameters
----------
what : str
attribute to change (must be from self.visible)
datasets : list of str or bool
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
"""
self._manage_view('set',what,datasets)
def view_more(self,what,datasets):
"""
Add to view.
Parameters
----------
what : str
attribute to change (must be from self.visible)
datasets : list of str or bool
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
"""
self._manage_view('add',what,datasets)
def view_less(self,what,datasets):
"""
Delete from view.
Parameters
----------
what : str
attribute to change (must be from self.visible)
datasets : list of str or bool
name of datasets as list, supports ? and * wildcards.
True is equivalent to [*], False is equivalent to []
"""
self._manage_view('del',what,datasets)
def rename(self,name_old,name_new):
"""
Rename datasets.
Parameters
----------
name_old : str
name of the datasets to be renamed
name_new : str
new name of the datasets
"""
if self._allow_modification:
with h5py.File(self.fname,'a') as f:
for path_old in self.get_dataset_location(name_old):
path_new = os.path.join(os.path.dirname(path_old),name_new)
f[path_new] = f[path_old]
f[path_new].attrs['Renamed'] = f'Original name: {name_old}' if h5py3 else \
f'Original name: {name_old}'.encode()
del f[path_old]
else:
raise PermissionError('Rename operation not permitted')
def place(self,datasets,constituent=0,tagged=False,split=True):
"""
Distribute datasets onto geometry and return Table or (split) dictionary of Tables.
Must not mix nodal end cell data.
Only data within
- inc*/phase/*/*
- inc*/homogenization/*/*
- inc*/geometry/*
are considered.
Parameters
----------
datasets : iterable or str
constituent : int
Constituent to consider for phase data
tagged : bool
tag Table.column name with '#constituent'
defaults to False
split : bool
split Table by increment and return dictionary of Tables
defaults to True
"""
sets = datasets if hasattr(datasets,'__iter__') and not isinstance(datasets,str) else \
[datasets]
tag = f'#{constituent}' if tagged else ''
tbl = {} if split else None
inGeom = {}
inData = {}
with h5py.File(self.fname,'r') as f:
for dataset in sets:
for group in self.groups_with_datasets(dataset):
path = os.path.join(group,dataset)
inc,prop,name,cat,item = (path.split('/') + ['']*5)[:5]
key = '/'.join([prop,name+tag])
if key not in inGeom:
if prop == 'geometry':
inGeom[key] = inData[key] = np.arange(self.N_materialpoints)
elif prop == 'phase':
inGeom[key] = np.where(f['mapping/phase'][:,constituent]['Name'] == str.encode(name))[0]
inData[key] = f['mapping/phase'][inGeom[key],constituent]['Position']
elif prop == 'homogenization':
inGeom[key] = np.where(f['mapping/homogenization']['Name'] == str.encode(name))[0]
inData[key] = f['mapping/homogenization'][inGeom[key].tolist()]['Position']
shape = np.shape(f[path])
data = np.full((self.N_materialpoints,) + (shape[1:] if len(shape)>1 else (1,)),
np.nan,
dtype=np.dtype(f[path]))
data[inGeom[key]] = (f[path] if len(shape)>1 else np.expand_dims(f[path],1))[inData[key]]
path = (os.path.join(*([prop,name]+([cat] if cat else [])+([item] if item else []))) if split else path)+tag
if split:
try:
tbl[inc].add(path,data)
except KeyError:
tbl[inc] = Table(data.reshape(self.N_materialpoints,-1),{path:data.shape[1:]})
else:
try:
tbl.add(path,data)
except AttributeError:
tbl = Table(data.reshape(self.N_materialpoints,-1),{path:data.shape[1:]})
return tbl
def groups_with_datasets(self,datasets):
"""
Return groups that contain all requested datasets.
Only groups within
- inc*/phase/*/*
- inc*/homogenization/*/*
- 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 bool
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.iterate('increments'):
for o,p in zip(['phases','homogenizations'],['out_type_ph','out_type_ho']):
for oo in self.iterate(o):
for pp in self.iterate(p):
group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue
if sets is True:
groups.append(group)
else:
if group in f.keys():
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.iterate('increments'):
message += f'\n{i} ({self.times[self.increments.index(i)]}s)\n'
for o,p in zip(['phases','homogenizations'],['out_type_ph','out_type_ho']):
message += f' {o[:-1]}\n'
for oo in self.iterate(o):
message += f' {oo}\n'
for pp in self.iterate(p):
message += f' {pp}\n'
group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue
for d in f[group].keys():
try:
dataset = f['/'.join([group,d])]
if 'Unit' in dataset.attrs:
unit = f" / {dataset.attrs['Unit']}" if h5py3 else \
f" / {dataset.attrs['Unit'].decode()}"
else:
unit = ''
description = dataset.attrs['Description'] if h5py3 else \
dataset.attrs['Description'].decode()
message += f' {d}{unit}: {description}\n'
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.iterate('increments'):
k = '/'.join([i,'geometry',label])
try:
f[k]
path.append(k)
except KeyError:
pass
for o,p in zip(['phases','homogenizations'],['out_type_ph','out_type_ho']):
for oo in self.iterate(o):
for pp in self.iterate(p):
k = '/'.join([i,o[:-1],oo,pp,label])
try:
f[k]
path.append(k)
except KeyError:
pass
return path
def enable_user_function(self,func):
globals()[func.__name__]=func
print(f'Function {func.__name__} enabled in add_calculation.')
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.
Parameters
----------
path : list of strings
The name of the datasets to consider.
c : int, optional
The constituent to consider. Defaults to 0.
plain: boolean, optional
Convert into plain numpy datatype.
Only relevant for compound datatype, e.g. the orientation.
Defaults to False.
"""
with h5py.File(self.fname,'r') as f:
shape = (self.N_materialpoints,) + 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/phase'][:,c]['Name'] == str.encode(label))[0]
if len(p)>0:
u = (f['mapping/phase']['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/homogenization']['Name'] == str.encode(label))[0]
if len(p)>0:
u = (f['mapping/homogenization']['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
@property
def coordinates0_point(self):
"""Return initial coordinates of the cell centers."""
if self.structured:
return grid_filters.coordinates0_point(self.cells,self.size,self.origin).reshape(-1,3,order='F')
else:
with h5py.File(self.fname,'r') as f:
return f['geometry/x_c'][()]
@property
def coordinates0_node(self):
"""Return initial coordinates of the cell centers."""
if self.structured:
return grid_filters.coordinates0_node(self.cells,self.size,self.origin).reshape(-1,3,order='F')
else:
with h5py.File(self.fname,'r') as f:
return f['geometry/x_n'][()]
@staticmethod
def _add_absolute(x):
return {
'data': np.abs(x['data']),
'label': f'|{x["label"]}|',
'meta': {
'Unit': x['meta']['Unit'],
'Description': f"Absolute value of {x['label']} ({x['meta']['Description']})",
'Creator': 'add_absolute'
}
}
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(f'#{d}#',f"kwargs['{d}']['data']")
return {
'data': eval(formula),
'label': kwargs['label'],
'meta': {
'Unit': kwargs['unit'],
'Description': f"{kwargs['description']} (formula: {kwargs['formula']})",
'Creator': 'add_calculation'
}
}
def add_calculation(self,label,formula,unit='n/a',description=None):
"""
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.
"""
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_stress_Cauchy(P,F):
return {
'data': mechanics.stress_Cauchy(P['data'],F['data']),
'label': 'sigma',
'meta': {
'Unit': P['meta']['Unit'],
'Description': "Cauchy stress calculated "
f"from {P['label']} ({P['meta']['Description']})"
f" and {F['label']} ({F['meta']['Description']})",
'Creator': 'add_stress_Cauchy'
}
}
def add_stress_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_stress_Cauchy,{'P':P,'F':F})
@staticmethod
def _add_determinant(T):
return {
'data': np.linalg.det(T['data']),
'label': f"det({T['label']})",
'meta': {
'Unit': T['meta']['Unit'],
'Description': f"Determinant of tensor {T['label']} ({T['meta']['Description']})",
'Creator': 'add_determinant'
}
}
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):
return {
'data': tensor.deviatoric(T['data']),
'label': f"s_{T['label']}",
'meta': {
'Unit': T['meta']['Unit'],
'Description': f"Deviator of tensor {T['label']} ({T['meta']['Description']})",
'Creator': 'add_deviator'
}
}
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,eigenvalue):
if eigenvalue == 'max':
label,p = 'Maximum',2
elif eigenvalue == 'mid':
label,p = 'Intermediate',1
elif eigenvalue == 'min':
label,p = 'Minimum',0
return {
'data': tensor.eigenvalues(T_sym['data'])[:,p],
'label': f"lambda_{eigenvalue}({T_sym['label']})",
'meta' : {
'Unit': T_sym['meta']['Unit'],
'Description': f"{label} eigenvalue of {T_sym['label']} ({T_sym['meta']['Description']})",
'Creator': 'add_eigenvalue'
}
}
def add_eigenvalue(self,T_sym,eigenvalue='max'):
"""
Add eigenvalues of symmetric tensor.
Parameters
----------
T_sym : str
Label of symmetric tensor dataset.
eigenvalue : str, optional
Eigenvalue. Select from ‘max’, ‘mid’, ‘min’. Defaults to ‘max’.
"""
self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
@staticmethod
def _add_eigenvector(T_sym,eigenvalue):
if eigenvalue == 'max':
label,p = 'maximum',2
elif eigenvalue == 'mid':
label,p = 'intermediate',1
elif eigenvalue == 'min':
label,p = 'minimum',0
return {
'data': tensor.eigenvectors(T_sym['data'])[:,p],
'label': f"v_{eigenvalue}({T_sym['label']})",
'meta' : {
'Unit': '1',
'Description': f"Eigenvector corresponding to {label} eigenvalue"
f" of {T_sym['label']} ({T_sym['meta']['Description']})",
'Creator': 'add_eigenvector'
}
}
def add_eigenvector(self,T_sym,eigenvalue='max'):
"""
Add eigenvector of symmetric tensor.
Parameters
----------
T_sym : str
Label of symmetric tensor dataset.
eigenvalue : str, optional
Eigenvalue to which the eigenvector corresponds. Select from
‘max’, ‘mid’, ‘min’. Defaults to ‘max’.
"""
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
@staticmethod
def _add_IPF_color(l,q):
m = util.scale_to_coprime(np.array(l))
try:
lattice = {'fcc':'cF','bcc':'cI','hex':'hP'}[q['meta']['Lattice']]
except KeyError:
lattice = q['meta']['Lattice']
try:
o = Orientation(rotation = (rfn.structured_to_unstructured(q['data'])),lattice=lattice)
except ValueError:
o = Orientation(rotation = q['data'],lattice=lattice)
return {
'data': np.uint8(o.IPF_color(l)*255),
'label': 'IPFcolor_[{} {} {}]'.format(*m),
'meta' : {
'Unit': '8-bit RGB',
'Lattice': q['meta']['Lattice'],
'Description': 'Inverse Pole Figure (IPF) colors along sample direction [{} {} {}]'.format(*m),
'Creator': 'add_IPF_color'
}
}
def add_IPF_color(self,l,q='O'):
"""
Add RGB color tuple of inverse pole figure (IPF) color.
Parameters
----------
l : numpy.array of shape (3)
Lab frame direction for inverse pole figure.
q : str
Label of the dataset containing the crystallographic orientation as quaternions.
Defaults to 'O'.
"""
self._add_generic_pointwise(self._add_IPF_color,{'q':q},{'l':l})
@staticmethod
def _add_maximum_shear(T_sym):
return {
'data': mechanics.maximum_shear(T_sym['data']),
'label': f"max_shear({T_sym['label']})",
'meta': {
'Unit': T_sym['meta']['Unit'],
'Description': f"Maximum shear component of {T_sym['label']} ({T_sym['meta']['Description']})",
'Creator': 'add_maximum_shear'
}
}
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_equivalent_Mises(T_sym,kind):
k = kind
if k is None:
if T_sym['meta']['Unit'] == '1':
k = 'strain'
elif T_sym['meta']['Unit'] == 'Pa':
k = 'stress'
if k not in ['stress', 'strain']:
raise ValueError('invalid von Mises kind {kind}')
return {
'data': (mechanics.equivalent_strain_Mises if k=='strain' else \
mechanics.equivalent_stress_Mises)(T_sym['data']),
'label': f"{T_sym['label']}_vM",
'meta': {
'Unit': T_sym['meta']['Unit'],
'Description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['Description']})",
'Creator': 'add_Mises'
}
}
def add_equivalent_Mises(self,T_sym,kind=None):
"""
Add the equivalent Mises stress or strain of a symmetric tensor.
Parameters
----------
T_sym : str
Label of symmetric tensorial stress or strain dataset.
kind : {'stress', 'strain', None}, optional
Kind of the von Mises equivalent. Defaults to None, in which case
it is selected based on the unit of the dataset ('1' -> strain, 'Pa' -> stress').
"""
self._add_generic_pointwise(self._add_equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
@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': f"|{x['label']}|_{o}",
'meta': {
'Unit': x['meta']['Unit'],
'Description': f"{o}-norm of {t} {x['label']} ({x['meta']['Description']})",
'Creator': 'add_norm'
}
}
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_stress_second_Piola_Kirchhoff(P,F):
return {
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
'label': 'S',
'meta': {
'Unit': P['meta']['Unit'],
'Description': "2. Piola-Kirchhoff stress calculated "
f"from {P['label']} ({P['meta']['Description']})"
f" and {F['label']} ({F['meta']['Description']})",
'Creator': 'add_stress_second_Piola_Kirchhoff'
}
}
def add_stress_second_Piola_Kirchhoff(self,P='P',F='F'):
"""
Add second Piola-Kirchhoff stress calculated from first Piola-Kirchhoff stress and deformation gradient.
Parameters
----------
P : str, optional
Label of 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_stress_second_Piola_Kirchhoff,{'P':P,'F':F})
# The add_pole functionality needs discussion.
# The new Crystal object can perform such a calculation but the outcome depends on the lattice parameters
# as well as on whether a direction or plane is concerned (see the DAMASK_examples/pole_figure notebook).
# Below code appears to be too simplistic.
# @staticmethod
# def _add_pole(q,p,polar):
# pole = np.array(p)
# unit_pole = pole/np.linalg.norm(pole)
# m = util.scale_to_coprime(pole)
# rot = Rotation(q['data'].view(np.double).reshape(-1,4))
#
# rotatedPole = rot @ np.broadcast_to(unit_pole,rot.shape+(3,)) # rotate pole according to crystal orientation
# xy = rotatedPole[:,0:2]/(1.+abs(unit_pole[2])) # stereographic projection
# coords = xy if not polar else \
# np.block([np.sqrt(xy[:,0:1]*xy[:,0:1]+xy[:,1:2]*xy[:,1:2]),np.arctan2(xy[:,1:2],xy[:,0:1])])
# 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': 'add_pole'
# }
# }
# 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_rotation(F):
return {
'data': mechanics.rotation(F['data']).as_matrix(),
'label': f"R({F['label']})",
'meta': {
'Unit': F['meta']['Unit'],
'Description': f"Rotational part of {F['label']} ({F['meta']['Description']})",
'Creator': 'add_rotation'
}
}
def add_rotation(self,F):
"""
Add rotational part of a deformation gradient.
Parameters
----------
F : str, optional
Label of deformation gradient dataset.
"""
self._add_generic_pointwise(self._add_rotation,{'F':F})
@staticmethod
def _add_spherical(T):
return {
'data': tensor.spherical(T['data'],False),
'label': f"p_{T['label']}",
'meta': {
'Unit': T['meta']['Unit'],
'Description': f"Spherical component of tensor {T['label']} ({T['meta']['Description']})",
'Creator': 'add_spherical'
}
}
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(F,t,m):
return {
'data': mechanics.strain(F['data'],t,m),
'label': f"epsilon_{t}^{m}({F['label']})",
'meta': {
'Unit': F['meta']['Unit'],
'Description': f"Strain tensor of {F['label']} ({F['meta']['Description']})",
'Creator': 'add_strain'
}
}
def add_strain(self,F='F',t='V',m=0.0):
"""
Add strain tensor of a deformation gradient.
For details refer to damask.mechanics.strain
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,{'F':F},{'t':t,'m':m})
@staticmethod
def _add_stretch_tensor(F,t):
return {
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
'label': f"{t}({F['label']})",
'meta': {
'Unit': F['meta']['Unit'],
'Description': '{} stretch tensor of {} ({})'.format('Left' if t.upper() == 'V' else 'Right',
F['label'],F['meta']['Description']),
'Creator': 'add_stretch_tensor'
}
}
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 if h5py3 else v.decode()) for k,v in loc.attrs.items()}}
lock.release()
r = func(**datasets_in,**args)
return [group,r]
except Exception as err:
print(f'Error during calculation: {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.
"""
chunk_size = 1024**2//8
pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',1)))
lock = mp.Manager().Lock()
groups = self.groups_with_datasets(datasets.values())
if len(groups) == 0:
print('No matching dataset found, no data was added.')
return
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:
if self._allow_modification and result[0]+'/'+result[1]['label'] in f:
dataset = f[result[0]+'/'+result[1]['label']]
dataset[...] = result[1]['data']
dataset.attrs['Overwritten'] = 'Yes' if h5py3 else \
'Yes'.encode()
else:
if result[1]['data'].size >= chunk_size*2:
shape = result[1]['data'].shape
chunks = (chunk_size//np.prod(shape[1:]),)+shape[1:]
dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'],
maxshape=shape, chunks=chunks,
compression='gzip', compression_opts=6,
shuffle=True,fletcher32=True)
else:
dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'])
now = datetime.datetime.now().astimezone()
dataset.attrs['Created'] = now.strftime('%Y-%m-%d %H:%M:%S%z') if h5py3 else \
now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
for l,v in result[1]['meta'].items():
dataset.attrs[l]=v if h5py3 else v.encode()
creator = dataset.attrs['Creator'] if h5py3 else \
dataset.attrs['Creator'].decode()
dataset.attrs['Creator'] = f"damask.Result.{creator} v{damask.version}" if h5py3 else \
f"damask.Result.{creator} v{damask.version}".encode()
except (OSError,RuntimeError) as err:
print(f'Could not add dataset: {err}.')
lock.release()
pool.close()
pool.join()
def save_XDMF(self):
"""
Write XDMF file to directly visualize data in DADF5 file.
The view is not taken into account, i.e. the content of the
whole file will be included.
"""
if self.N_constituents != 1 or len(self.phases) != 1 or not self.structured:
raise TypeError('XDMF output requires homogeneous grid')
attribute_type_map = defaultdict(lambda:'Matrix', ( ((),'Scalar'), ((3,),'Vector'), ((3,3),'Tensor')) )
def number_type_map(dtype):
if dtype in np.sctypes['int']: return 'Int'
if dtype in np.sctypes['uint']: return 'UInt'
if dtype in np.sctypes['float']: return 'Float'
xdmf=ET.Element('Xdmf')
xdmf.attrib={'Version': '2.0',
'xmlns:xi': 'http://www.w3.org/2001/XInclude'}
domain=ET.SubElement(xdmf, 'Domain')
collection = ET.SubElement(domain, 'Grid')
collection.attrib={'GridType': 'Collection',
'CollectionType': 'Temporal'}
time = ET.SubElement(collection, 'Time')
time.attrib={'TimeType': 'List'}
time_data = ET.SubElement(time, 'DataItem')
time_data.attrib={'Format': 'XML',
'NumberType': 'Float',
'Dimensions': f'{len(self.times)}'}
time_data.text = ' '.join(map(str,self.times))
attributes = []
data_items = []
for inc in self.increments:
grid=ET.SubElement(collection,'Grid')
grid.attrib = {'GridType': 'Uniform',
'Name': inc}
topology=ET.SubElement(grid, 'Topology')
topology.attrib={'TopologyType': '3DCoRectMesh',
'Dimensions': '{} {} {}'.format(*self.cells+1)}
geometry=ET.SubElement(grid, 'Geometry')
geometry.attrib={'GeometryType':'Origin_DxDyDz'}
origin=ET.SubElement(geometry, 'DataItem')
origin.attrib={'Format': 'XML',
'NumberType': 'Float',
'Dimensions': '3'}
origin.text="{} {} {}".format(*self.origin)
delta=ET.SubElement(geometry, 'DataItem')
delta.attrib={'Format': 'XML',
'NumberType': 'Float',
'Dimensions': '3'}
delta.text="{} {} {}".format(*(self.size/self.cells))
with h5py.File(self.fname,'r') as f:
attributes.append(ET.SubElement(grid, 'Attribute'))
attributes[-1].attrib={'Name': 'u / m',
'Center': 'Node',
'AttributeType': 'Vector'}
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
data_items[-1].attrib={'Format': 'HDF',
'Precision': '8',
'Dimensions': '{} {} {} 3'.format(*(self.cells+1))}
data_items[-1].text=f'{os.path.split(self.fname)[1]}:/{inc}/geometry/u_n'
for o,p in zip(['phases','homogenizations'],['out_type_ph','out_type_ho']):
for oo in getattr(self,o):
for pp in getattr(self,p):
g = '/'.join([inc,o[:-1],oo,pp])
for l in f[g]:
name = '/'.join([g,l])
shape = f[name].shape[1:]
dtype = f[name].dtype
if dtype not in np.sctypes['int']+np.sctypes['uint']+np.sctypes['float']: continue
unit = f[name].attrs['Unit'] if h5py3 else f[name].attrs['Unit'].decode()
attributes.append(ET.SubElement(grid, 'Attribute'))
attributes[-1].attrib={'Name': name.split('/',2)[2]+f' / {unit}',
'Center': 'Cell',
'AttributeType': attribute_type_map[shape]}
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
data_items[-1].attrib={'Format': 'HDF',
'NumberType': number_type_map(dtype),
'Precision': f'{dtype.itemsize}',
'Dimensions': '{} {} {} {}'.format(*self.cells,1 if shape == () else
np.prod(shape))}
data_items[-1].text=f'{os.path.split(self.fname)[1]}:{name}'
with open(self.fname.with_suffix('.xdmf').name,'w') as f:
f.write(xml.dom.minidom.parseString(ET.tostring(xdmf).decode()).toprettyxml())
def save_VTK(self,labels=[],mode='cell'):
"""
Export to vtk cell/point data.
Parameters
----------
labels : str or list of, optional
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:
v = VTK.from_rectilinear_grid(self.cells,self.size,self.origin)
else:
with h5py.File(self.fname,'r') as f:
v = VTK.from_unstructured_grid(f['/geometry/x_n'][()],
f['/geometry/T_c'][()]-1,
f['/geometry/T_c'].attrs['VTK_TYPE'] if h5py3 else \
f['/geometry/T_c'].attrs['VTK_TYPE'].decode())
elif mode.lower()=='point':
v = VTK.from_poly_data(self.coordinates0_point)
N_digits = int(np.floor(np.log10(max(1,int(self.increments[-1][3:])))))+1
for inc in util.show_progress(self.iterate('increments'),len(self.visible['increments'])):
viewed_backup_ho = self.visible['homogenizations'].copy()
self.view('homogenizations',False)
for label in (labels if isinstance(labels,list) else [labels]):
for o in self.iterate('out_type_ph'):
for c in range(self.N_constituents):
prefix = '' if self.N_constituents == 1 else f'constituent{c}/'
if o != 'mechanics':
for _ in self.iterate('phases'):
path = self.get_dataset_location(label)
if len(path) == 0:
continue
array = self.read_dataset(path,c)
v.add(array,prefix+path[0].split('/',1)[1]+f' / {self._get_attribute(path[0],"Unit")}')
else:
paths = self.get_dataset_location(label)
if len(paths) == 0:
continue
array = self.read_dataset(paths,c)
ph_name = re.compile(r'(?<=(phase\/))(.*?)(?=(mechanics))') # identify phase name
dset_name = prefix+re.sub(ph_name,r'',paths[0].split('/',1)[1]) # remove phase name
v.add(array,dset_name+f' / {self._get_attribute(paths[0],"Unit")}')
self.view('homogenizations',viewed_backup_ho)
viewed_backup_ph = self.visible['phases'].copy()
self.view('phases',False)
for label in (labels if isinstance(labels,list) else [labels]):
for _ in self.iterate('out_type_ho'):
paths = self.get_dataset_location(label)
if len(paths) == 0:
continue
array = self.read_dataset(paths)
v.add(array,paths[0].split('/',1)[1]+f' / {self._get_attribute(paths[0],"Unit")}')
self.view('phases',viewed_backup_ph)
u = self.read_dataset(self.get_dataset_location('u_n' if mode.lower() == 'cell' else 'u_p'))
v.add(u,'u')
v.save(f'{self.fname.stem}_inc{inc[3:].zfill(N_digits)}')