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DAMASK_EICMD
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DAMASK_EICMD/python/damask/_result.py

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import multiprocessing as mp
from multiprocessing.synchronize import Lock
import re
import fnmatch
import os
import copy
import datetime
import xml.etree.ElementTree as ET # noqa
import xml.dom.minidom
from pathlib import Path
from functools import partial
from collections import defaultdict
from collections.abc import Iterable
from typing import Optional, Union, Callable, Any, Sequence, Literal, Dict, List, Tuple
import h5py
import numpy as np
import numpy.ma as ma
import damask
from . import VTK
from . import Orientation
from . import Rotation
from . import grid_filters
from . import mechanics
from . import tensor
from . import util
from ._typehints import FloatSequence, IntSequence
h5py3 = h5py.__version__[0] == '3'
chunk_size = 1024**2//8 # for compression in HDF5
prefix_inc = 'increment_'
def _read(dataset: h5py._hl.dataset.Dataset) -> np.ndarray:
"""Read a dataset and its metadata into a numpy.ndarray."""
metadata = {k:(v.decode() if not h5py3 and type(v) is bytes else v) for k,v in dataset.attrs.items()}
dtype = np.dtype(dataset.dtype,metadata=metadata) # type: ignore
return np.array(dataset,dtype=dtype)
def _match(requested,
existing: h5py._hl.base.KeysViewHDF5) -> List[Any]:
"""Find matches among two sets of labels."""
def flatten_list(list_of_lists):
return [e for e_ in list_of_lists for e in e_]
if requested is True:
requested = '*'
elif requested is False or requested is None:
requested = []
requested_ = requested if hasattr(requested,'__iter__') and not isinstance(requested,str) else \
[requested]
return sorted(set(flatten_list([fnmatch.filter(existing,r) for r in requested_])),
key=util.natural_sort)
def _empty_like(dataset: np.ma.core.MaskedArray,
N_materialpoints: int,
fill_float: float,
fill_int: int) -> np.ma.core.MaskedArray:
"""Create empty numpy.ma.MaskedArray."""
return ma.array(np.empty((N_materialpoints,)+dataset.shape[1:],dataset.dtype),
fill_value = fill_float if dataset.dtype in np.sctypes['float'] else fill_int,
mask = True)
class AttributeManagerNullterm(h5py.AttributeManager):
"""
Attribute management for DREAM.3D hdf5 files.
String attribute values are stored as fixed-length string with NULLTERM
References
----------
https://stackoverflow.com/questions/38267076
https://stackoverflow.com/questions/52750232
"""
def create(self, name, data, shape=None, dtype=None):
if isinstance(data,str):
tid = h5py.h5t.C_S1.copy()
tid.set_size(len(data + ' '))
super().create(name=name,data=data+' ',dtype = h5py.Datatype(tid))
else:
super().create(name=name,data=data,shape=shape,dtype=dtype)
h5py._hl.attrs.AttributeManager = AttributeManagerNullterm # 'Monkey patch'
class Result:
"""
Add data to and export data from a DADF5 file.
A DADF5 (DAMASK HDF5) file contains DAMASK results.
Its group/folder structure reflects the layout in material.yaml.
This class provides a customizable view on the DADF5 file.
Upon initialization, all attributes are visible.
Derived quantities are added to the file and existing data is
exported based on the current view.
Examples
--------
Open 'my_file.hdf5', which is assumed to contain deformation gradient 'F'
and first Piola-Kirchhoff stress 'P', add the Mises equivalent of the
Cauchy stress, and export it to VTK (file) and numpy.ndarray (memory).
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_stress_Cauchy()
>>> r.add_equivalent_Mises('sigma')
>>> r.export_VTK()
>>> r_last = r.view(increments=-1)
>>> sigma_vM_last = r_last.get('sigma_vM')
"""
def __init__(self, fname: Union[str, Path]):
"""
New result view bound to a HDF5 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 12 <= self.version_minor <= 14) and self.version_major != 1:
raise TypeError(f'unsupported DADF5 version "{self.version_major}.{self.version_minor}"')
if self.version_major == 0 and self.version_minor < 14:
self.export_simulation_setup = None # type: ignore
self.structured = 'cells' in f['geometry'].attrs.keys()
if self.structured:
self.cells = f['geometry'].attrs['cells']
self.size = f['geometry'].attrs['size']
self.origin = f['geometry'].attrs['origin']
else:
self.add_curl = self.add_divergence = self.add_gradient = None # type: ignore
r = re.compile(rf'{prefix_inc}([0-9]+)')
self.increments = sorted([i for i in f.keys() if r.match(i)],key=util.natural_sort)
self.times = np.around([f[i].attrs['t/s'] for i in self.increments],12)
if len(self.increments) == 0:
raise ValueError('incomplete DADF5 file')
self.N_materialpoints, self.N_constituents = np.shape(f['cell_to/phase'])
self.homogenization = f['cell_to/homogenization']['label'].astype('str')
self.homogenizations = sorted(np.unique(self.homogenization),key=util.natural_sort)
self.phase = f['cell_to/phase']['label'].astype('str')
self.phases = sorted(np.unique(self.phase),key=util.natural_sort)
self.fields: List[str] = []
for c in self.phases:
self.fields += f['/'.join([self.increments[0],'phase',c])].keys()
for m in self.homogenizations:
self.fields += f['/'.join([self.increments[0],'homogenization',m])].keys()
self.fields = sorted(set(self.fields),key=util.natural_sort) # make unique
self.visible = {'increments': self.increments,
'phases': self.phases,
'homogenizations': self.homogenizations,
'fields': self.fields,
}
self.fname = Path(fname).expanduser().absolute()
self._protected = True
def __copy__(self) -> "Result":
"""
Return deepcopy(self).
Create deep copy.
"""
return copy.deepcopy(self)
copy = __copy__
def __repr__(self) -> str:
"""
Return repr(self).
Give short human-readable summary.
"""
with h5py.File(self.fname,'r') as f:
header = [f'Created by {f.attrs["creator"]}',
f' on {f.attrs["created"]}',
f' executing "{f.attrs["call"]}"']
visible_increments = self.visible['increments']
first = self.view(increments=visible_increments[0:1]).list_data()
last = [] if len(visible_increments) < 2 else \
self.view(increments=visible_increments[-1:]).list_data()
in_between = [] if len(visible_increments) < 3 else \
[f'\n{inc}\n ...' for inc in visible_increments[1:-1]]
return util.srepr([util.deemph(header)] + first + in_between + last)
def _manage_view(self,
action: Literal['set', 'add', 'del'],
increments: Union[None, int, Sequence[int], str, Sequence[str], bool] = None,
times: Union[None, float, Sequence[float], str, Sequence[str], bool] = None,
phases: Union[None, str, Sequence[str], bool] = None,
homogenizations: Union[None, str, Sequence[str], bool] = None,
fields: Union[None, str, Sequence[str], bool] = None) -> "Result":
"""
Manages the visibility of the groups.
Parameters
----------
action : str
Select from 'set', 'add', and 'del'.
Returns
-------
view : damask.Result
Modified or new view on the DADF5 file.
"""
if increments is not None and times is not None:
raise ValueError('"increments" and "times" are mutually exclusive')
dup = self.copy()
for what,datasets in zip(['increments','times','phases','homogenizations','fields'],
[ increments, times, phases, homogenizations, fields ]):
if datasets is None:
continue
# allow True/False and string arguments
elif datasets is True:
datasets = '*'
elif datasets is False:
datasets = []
choice = [datasets] if not hasattr(datasets,'__iter__') or isinstance(datasets,str) else list(datasets) # type: ignore
if what == 'increments':
choice = [c if isinstance(c,str) and c.startswith(prefix_inc) else
self.increments[c] if isinstance(c,int) and c<0 else
f'{prefix_inc}{c}' for c in choice]
elif what == 'times':
atol = 1e-2 * np.min(np.diff(self.times))
what = 'increments'
if choice == ['*']:
choice = self.increments
else:
iterator = np.array(choice).astype(float)
choice = []
for c in iterator:
idx = np.searchsorted(self.times,c,side='left')
if idx<len(self.times) and np.isclose(c,self.times[idx],rtol=0,atol=atol):
choice.append(self.increments[idx])
elif idx>0 and np.isclose(c,self.times[idx-1],rtol=0,atol=atol):
choice.append(self.increments[idx-1])
valid = _match(choice,getattr(self,what))
existing = set(self.visible[what])
if action == 'set':
dup.visible[what] = sorted(set(valid), key=util.natural_sort)
elif action == 'add':
dup.visible[what] = sorted(existing.union(valid), key=util.natural_sort)
elif action == 'del':
dup.visible[what] = sorted(existing.difference(valid), key=util.natural_sort)
return dup
def increments_in_range(self,
start: Union[None, str, int] = None,
end: Union[None, str, int] = None) -> Sequence[int]:
"""
Get all increments within a given range.
Parameters
----------
start : int or str, optional
Start increment. Defaults to first.
end : int or str, optional
End increment. Defaults to last.
Returns
-------
increments : list of ints
Increment number of all increments within the given bounds.
"""
s,e = map(lambda x: int(x.split(prefix_inc)[-1] if isinstance(x,str) and x.startswith(prefix_inc) else x),
(self.incs[ 0] if start is None else start,
self.incs[-1] if end is None else end))
return [i for i in self.incs if s <= i <= e]
def times_in_range(self,
start: Optional[float] = None,
end: Optional[float] = None) -> Sequence[float]:
"""
Get times of all increments within a given time range.
Parameters
----------
start : float, optional
Time of start increment. Defaults to time of first.
end : float, optional
Time of end increment. Defaults to time of last.
Returns
-------
times : list of float
Time of each increment within the given bounds.
"""
s,e = (self.times[ 0] if start is None else start,
self.times[-1] if end is None else end)
return [t for t in self.times if s <= t <= e]
def view(self,*,
increments: Union[None, int, Sequence[int], str, Sequence[str], bool] = None,
times: Union[None, float, Sequence[float], str, Sequence[str], bool] = None,
phases: Union[None, str, Sequence[str], bool] = None,
homogenizations: Union[None, str, Sequence[str], bool] = None,
fields: Union[None, str, Sequence[str], bool] = None,
protected: Optional[bool] = None) -> "Result":
"""
Set view.
Wildcard matching with '?' and '*' is supported.
True is equivalent to '*', False is equivalent to [].
Parameters
----------
increments: (list of) int, (list of) str, or bool, optional.
Number(s) of increments to select.
times: (list of) float, (list of) str, or bool, optional.
Simulation time(s) of increments to select.
phases: (list of) str, or bool, optional.
Name(s) of phases to select.
homogenizations: (list of) str, or bool, optional.
Name(s) of homogenizations to select.
fields: (list of) str, or bool, optional.
Name(s) of fields to select.
protected: bool, optional.
Protection status of existing data.
Returns
-------
view : damask.Result
View with only the selected attributes being visible.
Examples
--------
Get a view that shows only results from the initial configuration:
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r_first = r.view(increments=0)
Get a view that shows all results between simulation times of 10 to 40:
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r_t10to40 = r.view(times=r.times_in_range(10.0,40.0))
"""
dup = self._manage_view('set',increments,times,phases,homogenizations,fields)
if protected is not None:
if not protected:
print(util.warn('Warning: Modification of existing datasets allowed!'))
dup._protected = protected
return dup
def view_more(self,*,
increments: Union[None, int, Sequence[int], str, Sequence[str], bool] = None,
times: Union[None, float, Sequence[float], str, Sequence[str], bool] = None,
phases: Union[None, str, Sequence[str], bool] = None,
homogenizations: Union[None, str, Sequence[str], bool] = None,
fields: Union[None, str, Sequence[str], bool] = None) -> "Result":
"""
Add to view.
Wildcard matching with '?' and '*' is supported.
True is equivalent to '*', False is equivalent to [].
Parameters
----------
increments: (list of) int, (list of) str, or bool, optional.
Number(s) of increments to select.
times: (list of) float, (list of) str, or bool, optional.
Simulation time(s) of increments to select.
phases: (list of) str, or bool, optional.
Name(s) of phases to select.
homogenizations: (list of) str, or bool, optional.
Name(s) of homogenizations to select.
fields: (list of) str, or bool, optional.
Name(s) of fields to select.
Returns
-------
modified_view : damask.Result
View with additional visible attributes.
Examples
--------
Get a view that shows only results from first and last increment:
>>> import damask
>>> r_empty = damask.Result('my_file.hdf5').view(increments=False)
>>> r_first = r_empty.view_more(increments=0)
>>> r_first_and_last = r.first.view_more(increments=-1)
"""
return self._manage_view('add',increments,times,phases,homogenizations,fields)
def view_less(self,*,
increments: Union[None, int, Sequence[int], str, Sequence[str], bool] = None,
times: Union[None, float, Sequence[float], str, Sequence[str], bool] = None,
phases: Union[None, str, Sequence[str], bool] = None,
homogenizations: Union[None, str, Sequence[str], bool] = None,
fields: Union[None, str, Sequence[str], bool] = None) -> "Result":
"""
Remove from view.
Wildcard matching with '?' and '*' is supported.
True is equivalent to '*', False is equivalent to [].
Parameters
----------
increments: (list of) int, (list of) str, or bool, optional.
Number(s) of increments to select.
times: (list of) float, (list of) str, or bool, optional.
Simulation time(s) of increments to select.
phases: (list of) str, or bool, optional.
Name(s) of phases to select.
homogenizations: (list of) str, or bool, optional.
Name(s) of homogenizations to select.
fields: (list of) str, or bool, optional.
Name(s) of fields to select.
Returns
-------
modified_view : damask.Result
View with fewer visible attributes.
Examples
--------
Get a view that omits the undeformed configuration:
>>> import damask
>>> r_all = damask.Result('my_file.hdf5')
>>> r_deformed = r_all.view_less(increments=0)
"""
return self._manage_view('del',increments,times,phases,homogenizations,fields)
def rename(self,
name_src: str,
name_dst: str):
"""
Rename/move datasets (within the same group/folder).
This operation is discouraged because the history of the
data becomes untraceable and data integrity is not ensured.
Parameters
----------
name_src : str
Name of the datasets to be renamed.
name_dst : str
New name of the datasets.
Examples
--------
Rename datasets containing the deformation gradient from 'F' to 'def_grad':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r_unprotected = r.view(protected=False)
>>> r_unprotected.rename('F','def_grad')
"""
if self._protected:
raise PermissionError('rename datasets')
with h5py.File(self.fname,'a') as f:
for inc in self.visible['increments']:
for ty in ['phase','homogenization']:
for label in self.visible[ty+'s']:
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
path_src = '/'.join([inc,ty,label,field,name_src])
path_dst = '/'.join([inc,ty,label,field,name_dst])
if path_src in f.keys():
f[path_dst] = f[path_src]
f[path_dst].attrs['renamed'] = f'original name: {name_src}' if h5py3 else \
f'original name: {name_src}'.encode()
del f[path_src]
def remove(self, name: str):
"""
Remove/delete datasets.
This operation is discouraged because the history of the
data becomes untraceable and data integrity is not ensured.
Parameters
----------
name : str
Name of the datasets to be deleted.
Examples
--------
Delete the deformation gradient 'F':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r_unprotected = r.view(protected=False)
>>> r_unprotected.remove('F')
"""
if self._protected:
raise PermissionError('delete datasets')
with h5py.File(self.fname,'a') as f:
for inc in self.visible['increments']:
for ty in ['phase','homogenization']:
for label in self.visible[ty+'s']:
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
path = '/'.join([inc,ty,label,field,name])
if path in f.keys(): del f[path]
def list_data(self) -> List[str]:
"""
Collect information on all active datasets in the file.
Returns
-------
data : list of str
Line-formatted information about active datasets.
"""
msg = []
with h5py.File(self.fname,'r') as f:
for inc in self.visible['increments']:
msg += [f'\n{inc} ({self.times[self.increments.index(inc)]} s)']
for ty in ['phase','homogenization']:
msg += [f' {ty}']
for label in self.visible[ty+'s']:
msg += [f' {label}']
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
msg += [f' {field}']
for d in f['/'.join([inc,ty,label,field])].keys():
dataset = f['/'.join([inc,ty,label,field,d])]
unit = dataset.attrs["unit"] if h5py3 else \
dataset.attrs["unit"].decode()
description = dataset.attrs['description'] if h5py3 else \
dataset.attrs['description'].decode()
msg += [f' {d} / {unit}: {description}']
return msg
def enable_user_function(self,
func: Callable):
globals()[func.__name__]=func
print(f'Function {func.__name__} enabled in add_calculation.')
@property
def simulation_setup_files(self):
"""Simulation setup files used to generate the Result object."""
files = []
with h5py.File(self.fname,'r') as f_in:
f_in['setup'].visititems(lambda name,obj: files.append(name) if isinstance(obj,h5py.Dataset) else None)
return files
@property
def incs(self):
return [int(i.split(prefix_inc)[-1]) for i in self.increments]
@property
def coordinates0_point(self) -> np.ndarray:
"""Initial/undeformed cell center coordinates."""
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_p'][()]
@property
def coordinates0_node(self) -> np.ndarray:
"""Initial/undeformed nodal coordinates."""
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'][()]
@property
def geometry0(self) -> VTK:
"""Initial/undeformed geometry."""
if self.structured:
return VTK.from_image_data(self.cells,self.size,self.origin)
else:
with h5py.File(self.fname,'r') as f:
return 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())
@staticmethod
def _add_absolute(x: Dict[str, Any]) -> Dict[str, Any]:
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: str):
"""
Add absolute value.
Parameters
----------
x : str
Name 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) -> Dict[str, Any]:
formula = kwargs['formula']
for d in re.findall(r'#(.*?)#',formula):
formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
data = eval(formula)
if not hasattr(data,'shape') or data.shape[0] != kwargs[d]['data'].shape[0]:
raise ValueError('"{}" results in invalid shape'.format(kwargs['formula']))
return {
'data': data,
'label': kwargs['label'],
'meta': {
'unit': kwargs['unit'],
'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
'creator': 'add_calculation'
}
}
def add_calculation(self,
formula: str,
name: str,
unit: str = 'n/a',
description: Optional[str] = None):
"""
Add result of a general formula.
Parameters
----------
formula : str
Formula to calculate resulting dataset.
Existing datasets are referenced by '#TheirName#'.
name : str
Name of resulting dataset.
unit : str, optional
Physical unit of the result.
description : str, optional
Human-readable description of the result.
Examples
--------
Add total dislocation density, i.e. the sum of mobile dislocation
density 'rho_mob' and dislocation dipole density 'rho_dip' over
all slip systems:
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_calculation('np.sum(#rho_mob#,axis=1)','rho_mob_total',
... '1/m²','total mobile dislocation density')
>>> r.add_calculation('np.sum(#rho_dip#,axis=1)','rho_dip_total',
... '1/m²','total dislocation dipole density')
>>> r.add_calculation('#rho_dip_total#+#rho_mob_total','rho_total',
... '1/m²','total dislocation density')
Add Mises equivalent of the Cauchy stress without storage of
intermediate results. Define a user function for better readability:
>>> import damask
>>> def equivalent_stress(F,P):
... sigma = damask.mechanics.stress_Cauchy(F=F,P=P)
... return damask.mechanics.equivalent_stress_Mises(sigma)
>>> r = damask.Result('my_file.hdf5')
>>> r.enable_user_function(equivalent_stress)
>>> r.add_calculation('equivalent_stress(#F#,#P#)','sigma_vM','Pa',
... 'Mises equivalent of the Cauchy stress')
"""
dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
args = {'formula':formula,'label':name,'unit':unit,'description':description}
self._add_generic_pointwise(self._add_calculation,dataset_mapping,args)
@staticmethod
def _add_stress_Cauchy(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
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: str = 'P',
F: str = 'F'):
"""
Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
Parameters
----------
P : str, optional
Name of the dataset containing the first Piola-Kirchhoff stress. Defaults to 'P'.
F : str, optional
Name 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: Dict[str, Any]) -> Dict[str, Any]:
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: str):
"""
Add the determinant of a tensor.
Parameters
----------
T : str
Name of tensor dataset.
Examples
--------
Add the determinant of plastic deformation gradient 'F_p':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_determinant('F_p')
"""
self._add_generic_pointwise(self._add_determinant,{'T':T})
@staticmethod
def _add_deviator(T: Dict[str, Any]) -> Dict[str, Any]:
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: str):
"""
Add the deviatoric part of a tensor.
Parameters
----------
T : str
Name of tensor dataset.
Examples
--------
Add the deviatoric part of Cauchy stress 'sigma':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_deviator('sigma')
"""
self._add_generic_pointwise(self._add_deviator,{'T':T})
@staticmethod
def _add_eigenvalue(T_sym: Dict[str, Any], eigenvalue: Literal['max, mid, min']) -> Dict[str, Any]:
if eigenvalue == 'max':
label,p = 'maximum',2
elif eigenvalue == 'mid':
label,p = 'intermediate',1
elif eigenvalue == 'min':
label,p = 'minimum',0
else:
raise ValueError(f'invalid eigenvalue: {eigenvalue}')
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: str,
eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
"""
Add eigenvalues of symmetric tensor.
Parameters
----------
T_sym : str
Name of symmetric tensor dataset.
eigenvalue : {'max', 'mid', 'min'}, optional
Eigenvalue. Defaults to 'max'.
Examples
--------
Add the minimum eigenvalue of Cauchy stress 'sigma':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_eigenvalue('sigma','min')
"""
self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
@staticmethod
def _add_eigenvector(T_sym: Dict[str, Any], eigenvalue: Literal['max', 'mid', 'min']) -> Dict[str, Any]:
if eigenvalue == 'max':
label,p = 'maximum',2
elif eigenvalue == 'mid':
label,p = 'intermediate',1
elif eigenvalue == 'min':
label,p = 'minimum',0
else:
raise ValueError(f'invalid eigenvalue: {eigenvalue}')
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: str,
eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
"""
Add eigenvector of symmetric tensor.
Parameters
----------
T_sym : str
Name of symmetric tensor dataset.
eigenvalue : {'max', 'mid', 'min'}, optional
Eigenvalue to which the eigenvector corresponds.
Defaults to 'max'.
"""
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
@staticmethod
def _add_IPF_color(l: FloatSequence, q: Dict[str, Any]) -> Dict[str, Any]:
m = util.scale_to_coprime(np.array(l))
lattice = q['meta']['lattice']
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: FloatSequence,
q: str = '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, optional
Name of the dataset containing the crystallographic orientation as quaternions.
Defaults to 'O'.
Examples
--------
Add the IPF color along [0,1,1] for orientation 'O':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_IPF_color(np.array([0,1,1]))
"""
self._add_generic_pointwise(self._add_IPF_color,{'q':q},{'l':l})
@staticmethod
def _add_maximum_shear(T_sym: Dict[str, Any]) -> Dict[str, Any]:
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: str):
"""
Add maximum shear components of symmetric tensor.
Parameters
----------
T_sym : str
Name of symmetric tensor dataset.
"""
self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym})
@staticmethod
def _add_equivalent_Mises(T_sym: Dict[str, Any], kind: str) -> Dict[str, Any]:
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(f'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: str,
kind: Optional[str] = None):
"""
Add the equivalent Mises stress or strain of a symmetric tensor.
Parameters
----------
T_sym : str
Name 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).
Examples
--------
Add the Mises equivalent of the Cauchy stress 'sigma':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_equivalent_Mises('sigma')
Add the Mises equivalent of the spatial logarithmic strain 'epsilon_V^0.0(F)':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_equivalent_Mises('epsilon_V^0.0(F)')
"""
self._add_generic_pointwise(self._add_equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
@staticmethod
def _add_norm(x: Dict[str, Any], ord: Union[int, float, Literal['fro', 'nuc']]) -> Dict[str, Any]:
o = ord
if len(x['data'].shape) == 2:
axis: Union[int, Tuple[int, int]] = 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(f'invalid shape of {x["label"]}')
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: str,
ord: Union[None, int, float, Literal['fro', 'nuc']] = None):
"""
Add the norm of vector or tensor.
Parameters
----------
x : str
Name 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: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
return {
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
'label': 'S',
'meta': {
'unit': P['meta']['unit'],
'description': "second 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: str = 'P',
F: str = 'F'):
"""
Add second Piola-Kirchhoff stress calculated from first Piola-Kirchhoff stress and deformation gradient.
Parameters
----------
P : str, optional
Name of first Piola-Kirchhoff stress dataset. Defaults to 'P'.
F : str, optional
Name of deformation gradient dataset. Defaults to 'F'.
Notes
-----
The definition of the second Piola-Kirchhoff stress (S = [F^-1 P]_sym)
follows the standard definition in nonlinear continuum mechanics.
As such, no intermediate configuration, for instance that reached by F_p,
is taken into account.
"""
self._add_generic_pointwise(self._add_stress_second_Piola_Kirchhoff,{'P':P,'F':F})
@staticmethod
def _add_pole(q: Dict[str, Any],
uvw: FloatSequence,
hkl: FloatSequence,
with_symmetry: bool,
normalize: bool) -> Dict[str, Any]:
c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1
brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2]
label = 'p^' + '{}{} {} {}{}'.format(brackets[0],
*(uvw if uvw else hkl),
brackets[-1],)
ori = Orientation(q['data'],lattice=q['meta']['lattice'],a=1,c=c)
return {
'data': ori.to_pole(uvw=uvw,hkl=hkl,with_symmetry=with_symmetry,normalize=normalize),
'label': label,
'meta' : {
'unit': '1',
'description': f'{"normalized " if normalize else ""}lab frame vector along lattice ' \
+ ('direction' if uvw is not None else 'plane') \
+ ('s' if with_symmetry else ''),
'creator': 'add_pole'
}
}
def add_pole(self,
q: str = 'O',
*,
uvw: Optional[FloatSequence] = None,
hkl: Optional[FloatSequence] = None,
with_symmetry: bool = False,
normalize: bool = True):
"""
Add lab frame vector along lattice direction [uvw] or plane normal (hkl).
Parameters
----------
q : str, optional
Name of the dataset containing the crystallographic orientation as quaternions.
Defaults to 'O'.
uvw|hkl : numpy.ndarray of shape (3)
Miller indices of crystallographic direction or plane normal.
with_symmetry : bool, optional
Calculate all N symmetrically equivalent vectors.
Defaults to True.
normalize : bool, optional
Normalize output vector.
Defaults to True.
"""
self._add_generic_pointwise(self._add_pole,
{'q':q},
{'uvw':uvw,'hkl':hkl,'with_symmetry':with_symmetry,'normalize':normalize})
@staticmethod
def _add_rotation(F: Dict[str, Any]) -> Dict[str, Any]:
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: str):
"""
Add rotational part of a deformation gradient.
Parameters
----------
F : str
Name of deformation gradient dataset.
Examples
--------
Add the rotational part of deformation gradient 'F':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_rotation('F')
"""
self._add_generic_pointwise(self._add_rotation,{'F':F})
@staticmethod
def _add_spherical(T: Dict[str, Any]) -> Dict[str, Any]:
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: str):
"""
Add the spherical (hydrostatic) part of a tensor.
Parameters
----------
T : str
Name of tensor dataset.
Examples
--------
Add the hydrostatic part of the Cauchy stress 'sigma':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_spherical('sigma')
"""
self._add_generic_pointwise(self._add_spherical,{'T':T})
@staticmethod
def _add_strain(F: Dict[str, Any], t: Literal['V', 'U'], m: float) -> Dict[str, Any]:
side = 'left' if t == 'V' else 'right'
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 order {m} based on {side} stretch tensor '+\
f"of {F['label']} ({F['meta']['description']})",
'creator': 'add_strain'
}
}
def add_strain(self,
F: str = 'F',
t: Literal['V', 'U'] = 'V',
m: float = 0.0):
"""
Add strain tensor of a deformation gradient.
For details, see damask.mechanics.strain.
Parameters
----------
F : str, optional
Name 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.
Examples
--------
Add the Euler-Almansi strain:
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_strain(t='V',m=-1.0)
Add the plastic Biot strain:
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_strain('F_p','U',0.5)
Notes
-----
The incoporation of rotational parts into the elastic and plastic
deformation gradient requires it to use material/Lagragian strain measures
(based on 'U') for plastic strains and spatial/Eulerian strain measures
(based on 'V') for elastic strains when calculating averages.
"""
self._add_generic_pointwise(self._add_strain,{'F':F},{'t':t,'m':m})
@staticmethod
def _add_stretch_tensor(F: Dict[str, Any], t: str) -> Dict[str, Any]:
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': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
+f"of {F['label']} ({F['meta']['description']})", # noqa
'creator': 'add_stretch_tensor'
}
}
def add_stretch_tensor(self,
F: str = 'F',
t: Literal['V', 'U'] = 'V'):
"""
Add stretch tensor of a deformation gradient.
Parameters
----------
F : str, optional
Name 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})
@staticmethod
def _add_curl(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
return {
'data': grid_filters.curl(size,f['data']),
'label': f"curl({f['label']})",
'meta': {
'unit': f['meta']['unit']+'/m',
'description': f"curl of {f['label']} ({f['meta']['description']})",
'creator': 'add_curl'
}
}
def add_curl(self, f: str):
"""
Add curl of a field.
Parameters
----------
f : str
Name of vector or tensor field dataset.
Notes
-----
This function is only available for structured grids,
i.e. results from the grid solver.
"""
self._add_generic_grid(self._add_curl,{'f':f},{'size':self.size})
@staticmethod
def _add_divergence(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
return {
'data': grid_filters.divergence(size,f['data']),
'label': f"divergence({f['label']})",
'meta': {
'unit': f['meta']['unit']+'/m',
'description': f"divergence of {f['label']} ({f['meta']['description']})",
'creator': 'add_divergence'
}
}
def add_divergence(self, f: str):
"""
Add divergence of a field.
Parameters
----------
f : str
Name of vector or tensor field dataset.
Notes
-----
This function is only available for structured grids,
i.e. results from the grid solver.
"""
self._add_generic_grid(self._add_divergence,{'f':f},{'size':self.size})
@staticmethod
def _add_gradient(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
return {
'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
f['data'].reshape(f['data'].shape+(1,))),
'label': f"gradient({f['label']})",
'meta': {
'unit': f['meta']['unit']+'/m',
'description': f"gradient of {f['label']} ({f['meta']['description']})",
'creator': 'add_gradient'
}
}
def add_gradient(self, f: str):
"""
Add gradient of a field.
Parameters
----------
f : str
Name of scalar or vector field dataset.
Notes
-----
This function is only available for structured grids,
i.e. results from the grid solver.
"""
self._add_generic_grid(self._add_gradient,{'f':f},{'size':self.size})
def _add_generic_grid(self,
func: Callable,
datasets: Dict[str, str],
args: Dict[str, str] = {},
constituents = None):
"""
General function to add data on a regular grid.
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:
{arg (name to which the data is passed in func): label (in HDF5 file)}.
args : dictionary, optional
Arguments parsed to func.
"""
if len(datasets) != 1 or self.N_constituents != 1:
raise NotImplementedError
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
increments = self.place(list(datasets.values()),False)
if not increments: raise RuntimeError("received invalid dataset")
with h5py.File(self.fname, 'a') as f:
for increment in increments.items():
for ty in increment[1].items():
for field in ty[1].items():
d: np.ma.MaskedArray = list(field[1].values())[0]
if np.any(d.mask): continue
dataset = {'f':{'data':np.reshape(d.data,tuple(self.cells)+d.data.shape[1:]),
'label':list(datasets.values())[0],
'meta':d.data.dtype.metadata}}
r = func(**dataset,**args)
result = r['data'].reshape((-1,)+r['data'].shape[3:])
for x in self.visible[ty[0]+'s']:
if ty[0] == 'phase':
result1 = result[at_cell_ph[0][x]]
if ty[0] == 'homogenization':
result1 = result[at_cell_ho[x]]
path = '/'.join(['/',increment[0],ty[0],x,field[0]])
h5_dataset = f[path].create_dataset(r['label'],data=result1)
now = datetime.datetime.now().astimezone()
h5_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 r['meta'].items():
h5_dataset.attrs[l.lower()]=v if h5py3 else v.encode()
creator = h5_dataset.attrs['creator'] if h5py3 else \
h5_dataset.attrs['creator'].decode()
h5_dataset.attrs['creator'] = f'damask.Result.{creator} v{damask.version}' if h5py3 else \
f'damask.Result.{creator} v{damask.version}'.encode()
def _job_pointwise(self,
group: str,
callback: Callable,
datasets: Dict[str, str],
args: Dict[str, str],
lock: Lock) -> List[Union[None, Any]]:
"""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() if not h5py3 and type(v) is bytes else v) \
for k,v in loc.attrs.items()}}
lock.release()
r = callback(**datasets_in,**args)
return [group,r]
except Exception as err:
print(f'Error during calculation: {err}.')
return [None,None]
def _add_generic_pointwise(self,
func: Callable,
datasets: Dict[str, Any],
args: Dict[str, Any] = {}):
"""
General function to add pointwise data.
Parameters
----------
callback : 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:
{arg (name to which the data is passed in func): label (in HDF5 file)}.
args : dictionary, optional
Arguments parsed to func.
"""
pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',4)))
lock = mp.Manager().Lock()
groups = []
with h5py.File(self.fname,'r') as f:
for inc in self.visible['increments']:
for ty in ['phase','homogenization']:
for label in self.visible[ty+'s']:
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
group = '/'.join([inc,ty,label,field])
if set(datasets.values()).issubset(f[group].keys()): groups.append(group)
if len(groups) == 0:
print('No matching dataset found, no data was added.')
return
default_arg = partial(self._job_pointwise,callback=func,datasets=datasets,args=args,lock=lock)
for group,result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):# type: ignore
if not result:
continue
lock.acquire()
with h5py.File(self.fname, 'a') as f:
try:
if not self._protected and '/'.join([group,result['label']]) in f:
dataset = f['/'.join([group,result['label']])]
dataset[...] = result['data']
dataset.attrs['overwritten'] = True
else:
shape = result['data'].shape
if compress := (result['data'].size >= chunk_size*2):
chunks = (chunk_size//np.prod(shape[1:]),)+shape[1:]
else:
chunks = shape
dataset = f[group].create_dataset(result['label'],data=result['data'],
maxshape=shape, chunks=chunks,
compression = 'gzip' if compress else None,
compression_opts = 6 if compress else None,
shuffle=True,fletcher32=True)
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['meta'].items():
dataset.attrs[l.lower()]=v.encode() if not h5py3 and type(v) is str else v
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 _mappings(self):
"""Mappings to place data spatially."""
with h5py.File(self.fname,'r') as f:
at_cell_ph = []
in_data_ph = []
for c in range(self.N_constituents):
at_cell_ph.append({label: np.where(self.phase[:,c] == label)[0] \
for label in self.visible['phases']})
in_data_ph.append({label: f['/'.join(['cell_to','phase'])]['entry'][at_cell_ph[c][label]][:,c] \
for label in self.visible['phases']})
at_cell_ho = {label: np.where(self.homogenization[:] == label)[0] \
for label in self.visible['homogenizations']}
in_data_ho = {label: f['/'.join(['cell_to','homogenization'])]['entry'][at_cell_ho[label]] \
for label in self.visible['homogenizations']}
return at_cell_ph,in_data_ph,at_cell_ho,in_data_ho
def get(self,
output: Union[str, List[str]] = '*',
flatten: bool = True,
prune: bool = True) -> Optional[Dict[str,Any]]:
"""
Collect data per phase/homogenization reflecting the group/folder structure in the DADF5 file.
Parameters
----------
output : (list of) str, optional
Names of the datasets to read.
Defaults to '*', in which case all datasets are read.
flatten : bool, optional
Remove singular levels of the folder hierarchy.
This might be beneficial in case of single increment,
phase/homogenization, or field. Defaults to True.
prune : bool, optional
Remove branches with no data. Defaults to True.
Returns
-------
data : dict of numpy.ndarray
Datasets structured by phase/homogenization and according to selected view.
"""
r: Dict[str,Any] = {}
with h5py.File(self.fname,'r') as f:
for inc in util.show_progress(self.visible['increments']):
r[inc] = {'phase':{},'homogenization':{},'geometry':{}}
for out in _match(output,f['/'.join([inc,'geometry'])].keys()):
r[inc]['geometry'][out] = _read(f['/'.join([inc,'geometry',out])])
for ty in ['phase','homogenization']:
for label in self.visible[ty+'s']:
r[inc][ty][label] = {}
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
r[inc][ty][label][field] = {}
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
r[inc][ty][label][field][out] = _read(f['/'.join([inc,ty,label,field,out])])
if prune: r = util.dict_prune(r)
if flatten: r = util.dict_flatten(r)
return None if (type(r) == dict and r == {}) else r
def place(self,
output: Union[str, List[str]] = '*',
flatten: bool = True,
prune: bool = True,
constituents: Optional[IntSequence] = None,
fill_float: float = np.nan,
fill_int: int = 0) -> Optional[Dict[str,Any]]:
"""
Merge data into spatial order that is compatible with the damask.VTK geometry representation.
The returned data structure reflects the group/folder structure in the DADF5 file.
Multi-phase data is fused into a single output.
`place` is equivalent to `get` if only one phase/homogenization
and one constituent is present.
Parameters
----------
output : (list of) str, optional
Names of the datasets to read.
Defaults to '*', in which case all visible datasets are placed.
flatten : bool, optional
Remove singular levels of the folder hierarchy.
This might be beneficial in case of single increment or field.
Defaults to True.
prune : bool, optional
Remove branches with no data. Defaults to True.
constituents : (list of) int, optional
Constituents to consider.
Defaults to None, in which case all constituents are considered.
fill_float : float, optional
Fill value for non-existent entries of floating point type.
Defaults to NaN.
fill_int : int, optional
Fill value for non-existent entries of integer type.
Defaults to 0.
Returns
-------
data : dict of numpy.ma.MaskedArray
Datasets structured by spatial position and according to selected view.
"""
r: Dict[str,Any] = {}
constituents_ = map(int,constituents) if isinstance(constituents,Iterable) else \
(range(self.N_constituents) if constituents is None else [constituents]) # type: ignore
suffixes = [''] if self.N_constituents == 1 or isinstance(constituents,int) else \
[f'#{c}' for c in constituents_]
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
with h5py.File(self.fname,'r') as f:
for inc in util.show_progress(self.visible['increments']):
r[inc] = {'phase':{},'homogenization':{},'geometry':{}}
for out in _match(output,f['/'.join([inc,'geometry'])].keys()):
r[inc]['geometry'][out] = ma.array(_read(f['/'.join([inc,'geometry',out])]),fill_value = fill_float)
for ty in ['phase','homogenization']:
for label in self.visible[ty+'s']:
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
if field not in r[inc][ty].keys():
r[inc][ty][field] = {}
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
data = ma.array(_read(f['/'.join([inc,ty,label,field,out])]))
if ty == 'phase':
if out+suffixes[0] not in r[inc][ty][field].keys():
for c,suffix in zip(constituents_,suffixes):
r[inc][ty][field][out+suffix] = \
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
for c,suffix in zip(constituents_,suffixes):
r[inc][ty][field][out+suffix][at_cell_ph[c][label]] = data[in_data_ph[c][label]]
if ty == 'homogenization':
if out not in r[inc][ty][field].keys():
r[inc][ty][field][out] = \
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
r[inc][ty][field][out][at_cell_ho[label]] = data[in_data_ho[label]]
if prune: r = util.dict_prune(r)
if flatten: r = util.dict_flatten(r)
return None if (type(r) == dict and r == {}) else r
def export_XDMF(self,
output: Union[str, List[str]] = '*',
target_dir: Union[None, str, Path] = None,
absolute_path: bool = False):
"""
Write XDMF file to directly visualize data from DADF5 file.
The XDMF format is only supported for structured grids
with single phase and single constituent.
For other cases use `export_VTK`.
Parameters
----------
output : (list of) str, optional
Names of the datasets included in the XDMF file.
Defaults to '*', in which case all datasets are considered.
target_dir : str or pathlib.Path, optional
Directory to save XDMF file. Will be created if non-existent.
absolute_path : bool, optional
Store absolute (instead of relative) path to DADF5 file.
Defaults to False, i.e. the XDMF file expects the
DADF5 file at a stable relative path.
"""
if self.N_constituents != 1 or len(self.phases) != 1 or not self.structured:
raise TypeError('XDMF output requires structured grid with single phase and single constituent.')
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',
'Name': 'Increments'}
time = ET.SubElement(collection, 'Time')
time.attrib = {'TimeType': 'List'}
time_data = ET.SubElement(time, 'DataItem')
times = [self.times[self.increments.index(i)] for i in self.visible['increments']]
time_data.attrib = {'Format': 'XML',
'NumberType': 'Float',
'Dimensions': f'{len(times)}'}
time_data.text = ' '.join(map(str,times))
attributes = []
data_items = []
hdf5_name = self.fname.name
hdf5_dir = self.fname.parent
xdmf_dir = Path.cwd() if target_dir is None else Path(target_dir)
hdf5_link = (hdf5_dir if absolute_path else Path(os.path.relpath(hdf5_dir,xdmf_dir.resolve())))/hdf5_name
with h5py.File(self.fname,'r') as f:
for inc in self.visible['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]+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[::-1])
delta = ET.SubElement(geometry, 'DataItem')
delta.attrib = {'Format': 'XML',
'NumberType': 'Float',
'Dimensions': '3'}
delta.text="{} {} {}".format(*(self.size/self.cells)[::-1])
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]+1))}
data_items[-1].text = f'{hdf5_link}:/{inc}/geometry/u_n'
for ty in ['phase','homogenization']:
for label in self.visible[ty+'s']:
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
name = '/'.join([inc,ty,label,field,out])
shape = f[name].shape[1:]
dtype = f[name].dtype
unit = f[name].attrs['unit'] if h5py3 else \
f[name].attrs['unit'].decode()
attributes.append(ET.SubElement(grid, 'Attribute'))
attributes[-1].attrib = {'Name': '/'.join([ty,field,out])+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],1 if shape == () else
np.prod(shape))}
data_items[-1].text = f'{hdf5_link}:{name}'
xdmf_dir.mkdir(parents=True,exist_ok=True)
with util.open_text((xdmf_dir/hdf5_name).with_suffix('.xdmf'),'w') as f:
f.write(xml.dom.minidom.parseString(ET.tostring(xdmf).decode()).toprettyxml())
def export_VTK(self,
output: Union[str,List[str]] = '*',
mode: str = 'cell',
constituents: Optional[IntSequence] = None,
target_dir: Union[None, str, Path] = None,
fill_float: float = np.nan,
fill_int: int = 0,
parallel: bool = True):
"""
Export to VTK cell/point data.
One VTK file per visible increment is created.
For point data, the VTK format is poly data (.vtp).
For cell data, either an image (.vti) or unstructured (.vtu) dataset
is written for grid-based or mesh-based simulations, respectively.
Parameters
----------
output : (list of) str, optional
Names of the datasets to export to the VTK file.
Defaults to '*', in which case all visible datasets are exported.
mode : {'cell', 'point'}, optional
Export in cell format or point format.
Defaults to 'cell'.
constituents : (list of) int, optional
Constituents to consider.
Defaults to None, in which case all constituents are considered.
target_dir : str or pathlib.Path, optional
Directory to save VTK files. Will be created if non-existent.
fill_float : float, optional
Fill value for non-existent entries of floating point type.
Defaults to NaN.
fill_int : int, optional
Fill value for non-existent entries of integer type.
Defaults to 0.
parallel : bool, optional
Write VTK files in parallel in a separate background process.
Defaults to True.
"""
if mode.lower()=='cell':
v = self.geometry0
elif mode.lower()=='point':
v = VTK.from_poly_data(self.coordinates0_point)
else:
raise ValueError(f'invalid mode "{mode}"')
v.comments = [util.execution_stamp('Result','export_VTK')]
N_digits = int(np.floor(np.log10(max(1,self.incs[-1]))))+1
constituents_ = constituents if isinstance(constituents,Iterable) else \
(range(self.N_constituents) if constituents is None else [constituents]) # type: ignore
suffixes = [''] if self.N_constituents == 1 or isinstance(constituents,int) else \
[f'#{c}' for c in constituents_]
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
vtk_dir = Path.cwd() if target_dir is None else Path(target_dir)
vtk_dir.mkdir(parents=True,exist_ok=True)
with h5py.File(self.fname,'r') as f:
if self.version_minor >= 13:
creator = f.attrs['creator'] if h5py3 else f.attrs['creator'].decode()
created = f.attrs['created'] if h5py3 else f.attrs['created'].decode()
v.comments += [f'{creator} ({created})']
for inc in util.show_progress(self.visible['increments']):
u = _read(f['/'.join([inc,'geometry','u_n' if mode.lower() == 'cell' else 'u_p'])])
v = v.set('u',u)
for ty in ['phase','homogenization']:
for field in self.visible['fields']:
outs: Dict[str, np.ma.core.MaskedArray] = {}
for label in self.visible[ty+'s']:
if field not in f['/'.join([inc,ty,label])].keys(): continue
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
data = ma.array(_read(f['/'.join([inc,ty,label,field,out])]))
if ty == 'phase':
if out+suffixes[0] not in outs.keys():
for c,suffix in zip(constituents_,suffixes):
outs[out+suffix] = \
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
for c,suffix in zip(constituents_,suffixes):
outs[out+suffix][at_cell_ph[c][label]] = data[in_data_ph[c][label]]
if ty == 'homogenization':
if out not in outs.keys():
outs[out] = _empty_like(data,self.N_materialpoints,fill_float,fill_int)
outs[out][at_cell_ho[label]] = data[in_data_ho[label]]
for label,dataset in outs.items():
v = v.set(' / '.join(['/'.join([ty,field,label]),dataset.dtype.metadata['unit']]),dataset)
v.save(vtk_dir/f'{self.fname.stem}_inc{inc.split(prefix_inc)[-1].zfill(N_digits)}',
parallel=parallel)
def export_DREAM3D(self,
target_dir: Union[None, str, Path] = None):
"""
Export the visible components to DREAM3D compatible files.
One DREAM3D file per visible increment is created.
The DREAM3D file is based on HDF5 file format.
Without any regridding.
Considers the original grid from DAMASK.
Needs orientation data, O, present in the file.
Parameters
----------
target_dir : str or pathlib.Path, optional
Directory to save DREAM3D files. Will be created if non-existent.
"""
Crystal_structures = {'fcc': 1,
'bcc': 1,
'hcp': 0,
'bct': 7,
'ort': 6} #TODO: is bct Tetragonal low/Tetragonal high?
Phase_types = {'Primary': 0} #further additions to these can be done by looking at 'Create Ensemble Info' filter, other options could be 'Precipitate' and so on.
dx = self.size/self.cells
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
dream_dir = Path.cwd() if target_dir is None else Path(target_dir)
dream_dir.mkdir(parents=True,exist_ok=True)
with h5py.File(self.fname,'r') as f:
for inc in util.show_progress(self.visible['increments']):
cell_orientation_array = np.zeros((np.prod(self.cells),3))
phase_ID_array = np.zeros((np.prod(self.cells)),dtype=np.int32) #need to reshape it later
for c in range(self.N_constituents):
for count,label in enumerate(self.visible['phases']):
try:
data = ma.array(_read(f['/'.join([inc,'phase',label,'mechanical/O'])]))
cell_orientation_array[at_cell_ph[c][label],:] = \
Rotation(data[in_data_ph[c][label],:]).as_Euler_angles()
# Dream3D handles euler angles better
except ValueError: #check if the exception is correct
print("Orientation data is not present")
exit() # need to check if such a statement would really work.
phase_ID_array[at_cell_ph[c][label]] = count + 1
job_file_no_ext = self.fname.name
o = h5py.File(f'{dream_dir}/{job_file_no_ext}_{inc}.dream3D','w')
o.attrs['DADF5toDREAM3D'] = '1.0'
o.attrs['FileVersion'] = '7.0'
for g in ['DataContainerBundles','Pipeline']: # empty groups (needed)
o.create_group(g)
data_container_label = 'DataContainers/SyntheticVolumeDataContainer'
cell_data_label = data_container_label + '/CellData'
# Data phases
o[cell_data_label + '/Phases'] = np.reshape(phase_ID_array, \
tuple(np.flip(self.cells))+(1,))
# Data eulers
orientation_data = cell_orientation_array.astype(np.float32)
o[cell_data_label + '/Eulers'] = orientation_data.reshape(tuple(np.flip(self.cells))+(3,))
# Attributes to CellData group
o[cell_data_label].attrs['AttributeMatrixType'] = np.array([3],np.uint32)
o[cell_data_label].attrs['TupleDimensions'] = np.array(self.cells,np.uint64)
# Common Attributes for groups in CellData
for group in ['/Phases','/Eulers']:
o[cell_data_label + group].attrs['DataArrayVersion'] = np.array([2],np.int32)
o[cell_data_label + group].attrs['Tuple Axis Dimensions'] = 'x={},y={},z={}'.format(*np.array(self.cells))
# phase attributes
o[cell_data_label + '/Phases'].attrs['ComponentDimensions'] = np.array([1],np.uint64)
o[cell_data_label + '/Phases'].attrs['ObjectType'] = 'DataArray<int32_t>'
o[cell_data_label + '/Phases'].attrs['TupleDimensions'] = np.array(self.cells,np.uint64)
# Eulers attributes
o[cell_data_label + '/Eulers'].attrs['ComponentDimensions'] = np.array([3],np.uint64)
o[cell_data_label + '/Eulers'].attrs['ObjectType'] = 'DataArray<float>'
o[cell_data_label + '/Eulers'].attrs['TupleDimensions'] = np.array(self.cells,np.uint64)
# Create EnsembleAttributeMatrix
ensemble_label = data_container_label + '/CellEnsembleData'
# Data CrystalStructures
o[ensemble_label + '/CrystalStructures'] = np.uint32(np.array([999] + [1]*len(self.phases)))
# assuming only cubic crystal structures
# Damask can give the crystal structure info but need to look into dream3d which crystal structure corresponds to which number
o[ensemble_label + '/PhaseTypes'] = np.uint32(np.array([999] + [Phase_types['Primary']]*len(self.phases))).reshape((len(self.phases)+1,1))
# also assuming Primary phases
# there can be precipitates etc as well
# Attributes Ensemble Matrix
o[ensemble_label].attrs['AttributeMatrixType'] = np.array([11],np.uint32)
o[ensemble_label].attrs['TupleDimensions'] = np.array([len(self.phases) + 1], np.uint64)
# Attributes for data in Ensemble matrix
for group in ['CrystalStructures','PhaseTypes']: # 'PhaseName' not required MD: But would be nice to take the phase name mapping
o[ensemble_label+'/'+group].attrs['ComponentDimensions'] = np.array([1],np.uint64)
o[ensemble_label+'/'+group].attrs['Tuple Axis Dimensions'] = f'x={len(self.phases)+1}'
o[ensemble_label+'/'+group].attrs['DataArrayVersion'] = np.array([2],np.int32)
o[ensemble_label+'/'+group].attrs['ObjectType'] = 'DataArray<uint32_t>'
o[ensemble_label+'/'+group].attrs['TupleDimensions'] = np.array([len(self.phases) + 1],np.uint64)
# Create geometry info
geom_label = data_container_label + '/_SIMPL_GEOMETRY'
o[geom_label + '/DIMENSIONS'] = np.int64(np.array(self.cells))
o[geom_label + '/ORIGIN'] = np.float32(np.zeros(3))
o[geom_label + '/SPACING'] = np.float32(dx)
o[geom_label].attrs['GeometryName'] = 'ImageGeometry'
o[geom_label].attrs['GeometryTypeName'] = 'ImageGeometry'
o[geom_label].attrs['GeometryType'] = np.array([0],np.uint32)
o[geom_label].attrs['SpatialDimensionality'] = np.array([3],np.uint32)
o[geom_label].attrs['UnitDimensionality'] = np.array([3],np.uint32)
def export_DADF5(self,
fname,
output: Union[str, List[str]] = '*'):
"""
Export visible components into a new DADF5 file.
A DADF5 (DAMASK HDF5) file contains DAMASK results.
Its group/folder structure reflects the layout in material.yaml.
Parameters
----------
fname : str or pathlib.Path
Name of the DADF5 file to be created.
output : (list of) str, optional
Names of the datasets to export.
Defaults to '*', in which case all visible datasets are exported.
"""
if Path(fname).expanduser().absolute() == self.fname:
raise PermissionError(f'cannot overwrite {self.fname}')
with h5py.File(self.fname,'r') as f_in, h5py.File(fname,'w') as f_out:
for k,v in f_in.attrs.items():
f_out.attrs.create(k,v)
for g in ['setup','geometry','cell_to']:
f_in.copy(g,f_out)
for inc in util.show_progress(self.visible['increments']):
f_in.copy(inc,f_out,shallow=True)
for out in _match(output,f_in['/'.join([inc,'geometry'])].keys()):
f_in[inc]['geometry'].copy(out,f_out[inc]['geometry'])
for label in self.homogenizations:
f_in[inc]['homogenization'].copy(label,f_out[inc]['homogenization'],shallow=True)
for label in self.phases:
f_in[inc]['phase'].copy(label,f_out[inc]['phase'],shallow=True)
for ty in ['phase','homogenization']:
for label in self.visible[ty+'s']:
for field in _match(self.visible['fields'],f_in['/'.join([inc,ty,label])].keys()):
p = '/'.join([inc,ty,label,field])
for out in _match(output,f_in[p].keys()):
f_in[p].copy(out,f_out[p])
def export_simulation_setup(self,
output: Union[str, List[str]] = '*',
target_dir: Union[None, str, Path] = None,
overwrite: bool = False,
):
"""
Export original simulation setup of the Result object.
Parameters
----------
output : (list of) str, optional
Names of the datasets to export to the file.
Defaults to '*', in which case all setup files are exported.
target_dir : str or pathlib.Path, optional
Directory to save setup files. Will be created if non-existent.
overwrite : bool, optional
Overwrite any existing setup files.
Defaults to False.
"""
def export(name: str,
obj: Union[h5py.Dataset,h5py.Group],
output: Union[str,List[str]],
cfg_dir: Path,
overwrite: bool):
cfg = cfg_dir/name
if type(obj) == h5py.Dataset and _match(output,[name]):
if cfg.exists() and not overwrite:
raise PermissionError(f'"{cfg}" exists')
else:
cfg.parent.mkdir(parents=True,exist_ok=True)
with util.open_text(cfg,'w') as f_out: f_out.write(obj[0].decode())
cfg_dir = (Path.cwd() if target_dir is None else Path(target_dir))
with h5py.File(self.fname,'r') as f_in:
f_in['setup'].visititems(partial(export,
output=output,
cfg_dir=cfg_dir,
overwrite=overwrite))
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