DAMASK_EICMD/python/damask/_result.py

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import multiprocessing as mp
import re
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import fnmatch
import os
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import copy
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import datetime
import warnings
import xml.etree.ElementTree as ET
import xml.dom.minidom
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from pathlib import Path
from functools import partial
from collections import defaultdict
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from collections.abc import Iterable
import h5py
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import numpy as np
import numpy.ma as ma
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import damask
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from . import VTK
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from . import Orientation
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from . import grid_filters
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from . import mechanics
from . import tensor
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from . import util
h5py3 = h5py.__version__[0] == '3'
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chunk_size = 1024**2//8 # for compression in HDF5
def _view_transition(what,datasets,increments,times,phases,homogenizations,fields):
if (datasets is not None and what is None) or (what is not None and datasets is None):
raise ValueError('"what" and "datasets" need to be used as a pair')
if datasets is not None or what is not None:
warnings.warn('Arguments "what" and "datasets" will be removed in DAMASK v3.0.0-alpha7', DeprecationWarning,2)
return what,datasets
if sum(1 for _ in filter(None.__ne__, [increments,times,phases,homogenizations,fields])) > 1:
raise ValueError('Only one out of "increments", "times", "phases", "homogenizations", and "fields" can be used')
else:
if increments is not None: return "increments", increments
if times is not None: return "times", times
if phases is not None: return "phases", phases
if homogenizations is not None: return "homogenizations", homogenizations
if fields is not None: return "fields", fields
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def _read(dataset):
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"""Read a dataset and its metadata into a numpy.ndarray."""
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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)
return np.array(dataset,dtype=dtype)
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def _match(requested,existing):
"""Find matches among two sets of labels."""
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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]
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return sorted(set(flatten_list([fnmatch.filter(existing,r) for r in requested_])),
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key=util.natural_sort)
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def _empty_like(dataset,N_materialpoints,fill_float,fill_int):
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"""Create empty numpy.ma.MaskedArray."""
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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)
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class Result:
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"""
Add data to and export data from a DADF5 file.
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A DADF5 (DAMASK HDF5) file contains DAMASK results.
Its group/folder structure reflects the layout in material.yaml.
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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
--------
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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_Cauchy()
>>> r.add_equivalent_Mises('sigma')
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>>> r.export_VTK()
>>> r_last = r.view(increments=-1)
>>> sigma_vM_last = r_last.get('sigma_vM')
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"""
def __init__(self,fname):
"""
New result view bound to a HDF5 file.
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Parameters
----------
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fname : str or pathlib.Path
Name of the DADF5 file to be opened.
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"""
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:
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raise TypeError(f'Unsupported DADF5 version {self.version_major}.{self.version_minor}')
if self.version_major == 0 and self.version_minor < 14:
self.export_setup = None
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self.structured = 'cells' in f['geometry'].attrs.keys()
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if self.structured:
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self.cells = f['geometry'].attrs['cells']
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self.size = f['geometry'].attrs['size']
self.origin = f['geometry'].attrs['origin']
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else:
self.add_curl = self.add_divergence = self.add_gradient = None
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r=re.compile('increment_[0-9]+')
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self.increments = sorted([i for i in f.keys() if r.match(i)],key=util.natural_sort)
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self.times = [round(f[i].attrs['t/s'],12) for i in self.increments]
if len(self.increments) == 0:
raise ValueError('incomplete DADF5 file')
self.N_materialpoints, self.N_constituents = np.shape(f['cell_to/phase'])
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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)
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self.fields = []
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()
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self.fields = sorted(set(self.fields),key=util.natural_sort) # make unique
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self.visible = {'increments': self.increments,
'phases': self.phases,
'homogenizations': self.homogenizations,
'fields': self.fields,
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}
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self.fname = Path(fname).absolute()
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self._protected = True
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def __copy__(self):
"""Create deep copy."""
return copy.deepcopy(self)
copy = __copy__
def __repr__(self):
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"""Show summary of file content."""
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visible_increments = self.visible['increments']
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first = self.view(increments=visible_increments[0:1]).list_data()
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last = '' if len(visible_increments) < 2 else \
self.view(increments=visible_increments[-1:]).list_data()
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in_between = '' if len(visible_increments) < 3 else \
''.join([f'\n{inc}\n ...\n' for inc in visible_increments[1:-1]])
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return util.srepr(first + in_between + last)
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def _manage_view(self,action,what,datasets):
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"""
Manages the visibility of the groups.
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Parameters
----------
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action : str
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Select from 'set', 'add', and 'del'.
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what : str
Attribute to change (must be from self.visible).
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datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool
Name of datasets; supports '?' and '*' wildcards.
True is equivalent to '*', False is equivalent to [].
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Returns
-------
view : damask.Result
Modified or new view on the DADF5 file.
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"""
# allow True/False and string arguments
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if datasets is True:
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datasets = '*'
elif datasets is False or datasets is None:
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datasets = []
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choice = list(datasets).copy() if hasattr(datasets,'__iter__') and not isinstance(datasets,str) else \
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[datasets]
what_ = what if what.endswith('s') else what+'s'
if what_ == 'increments':
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choice = [c if isinstance(c,str) and c.startswith('increment_') else
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self.increments[c] if isinstance(c,int) and c<0 else
f'increment_{c}' for c in choice]
elif what_ == 'times':
what_ = 'increments'
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if choice == ['*']:
choice = self.increments
else:
iterator = map(float,choice)
choice = []
for c in iterator:
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idx = np.searchsorted(self.times,c)
if idx >= len(self.times): continue
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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 = _match(choice,getattr(self,what_))
existing = set(self.visible[what_])
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dup = self.copy()
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if action == 'set':
dup.visible[what_] = sorted(set(valid), key=util.natural_sort)
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elif action == 'add':
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add = existing.union(valid)
dup.visible[what_] = sorted(add, key=util.natural_sort)
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elif action == 'del':
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diff = existing.difference(valid)
dup.visible[what_] = sorted(diff, key=util.natural_sort)
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return dup
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def increments_in_range(self,start,end):
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"""
Get all increments within a given range.
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Parameters
----------
start : int or str
Start increment.
end : int or str
End increment.
Returns
-------
increments : list of ints
Increment number of all increments within the given bounds.
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"""
selected = []
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for i,inc in enumerate([int(i[10:]) for i in self.increments]):
s,e = map(lambda x: int(x[10:] 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):
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"""
Get all increments within a given time range.
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Parameters
----------
start : float
Time of start increment.
end : float
Time of end increment.
Returns
-------
times : list of float
Simulation time of all increments within the given bounds.
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"""
selected = []
for i,time in enumerate(self.times):
if start <= time <= end:
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selected.append(self.times[i])
return selected
def view(self,what=None,datasets=None,*,
increments=None,
times=None,
phases=None,
homogenizations=None,
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fields=None,
protected=None):
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"""
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Set view.
Wildcard matching with '?' and '*' is supported.
True is equivalent to '*', False is equivalent to [].
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Parameters
----------
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what : {'increments', 'times', 'phases', 'homogenizations', 'fields'}
Attribute to change. DEPRECATED.
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datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool
Name of datasets; supports '?' and '*' wildcards. DEPRECATED.
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True is equivalent to '*', False is equivalent to [].
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.
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protected: bool, optional.
Protection status of existing data.
Returns
-------
view : damask.Result
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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(increment=0)
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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))
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"""
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v = _view_transition(what,datasets,increments,times,phases,homogenizations,fields)
if protected is not None:
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if v is None:
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dup = self.copy()
else:
what_,datasets_ = v
dup = self._manage_view('set',what_,datasets_)
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if not protected:
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print(util.warn('Warning: Modification of existing datasets allowed!'))
dup._protected = protected
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else:
what_,datasets_ = v
dup = self._manage_view('set',what_,datasets_)
return dup
def view_more(self,what=None,datasets=None,*,
increments=None,
times=None,
phases=None,
homogenizations=None,
fields=None):
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"""
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Add to view.
Wildcard matching with '?' and '*' is supported.
True is equivalent to '*', False is equivalent to [].
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Parameters
----------
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what : {'increments', 'times', 'phases', 'homogenizations', 'fields'}
Attribute to change. DEPRECATED.
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datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool
Name of datasets; supports '?' and '*' wildcards. DEPRECATED.
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True is equivalent to '*', False is equivalent to [].
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
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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)
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"""
what_, datasets_ = _view_transition(what,datasets,increments,times,phases,homogenizations,fields)
return self._manage_view('add',what_,datasets_)
def view_less(self,what=None,datasets=None,*,
increments=None,
times=None,
phases=None,
homogenizations=None,
fields=None):
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"""
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Remove from view.
Wildcard matching with '?' and '*' is supported.
True is equivalent to '*', False is equivalent to [].
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Parameters
----------
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what : {'increments', 'times', 'phases', 'homogenizations', 'fields'}
Attribute to change. DEPRECATED.
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datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool
Name of datasets; supports '?' and '*' wildcards. DEPRECATED.
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True is equivalent to '*', False is equivalent to [].
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.
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Returns
-------
modified_view : damask.Result
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View with fewer visible attributes.
Examples
--------
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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)
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"""
what_, datasets_ = _view_transition(what,datasets,increments,times,phases,homogenizations,fields)
return self._manage_view('del',what_,datasets_)
def rename(self,name_src,name_dst):
"""
Rename/move datasets (within the same group/folder).
This operation is discouraged because the history of the
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data becomes untraceable and data integrity is not ensured.
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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')
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>>> r_unprotected = r.view(protected=False)
>>> r_unprotected.rename('F','def_grad')
"""
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if self._protected:
raise PermissionError('Renaming datasets not permitted')
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with h5py.File(self.fname,'a') as f:
for inc in self.visible['increments']:
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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):
"""
Remove/delete datasets.
This operation is discouraged because the history of the
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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')
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>>> r_unprotected = r.view(protected=False)
>>> r_unprotected.remove('F')
"""
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if self._protected:
raise PermissionError('Removing datasets not permitted')
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]
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def list_data(self):
"""Return information on all active datasets in the file."""
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msg = ''
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with h5py.File(self.fname,'r') as f:
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for inc in self.visible['increments']:
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msg = ''.join([msg,f'\n{inc} ({self.times[self.increments.index(inc)]}s)\n'])
for ty in ['phase','homogenization']:
msg = ' '.join([msg,f'{ty}\n'])
for label in self.visible[ty+'s']:
msg = ' '.join([msg,f'{label}\n'])
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
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msg = ' '.join([msg,f'{field}\n'])
for d in f['/'.join([inc,ty,label,field])].keys():
dataset = f['/'.join([inc,ty,label,field,d])]
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unit = f' / {dataset.attrs["unit"]}' if h5py3 else \
f' / {dataset.attrs["unit"].decode()}'
description = dataset.attrs['description'] if h5py3 else \
dataset.attrs['description'].decode()
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msg = ' '.join([msg,f'{d}{unit}: {description}\n'])
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return msg
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def enable_user_function(self,func):
globals()[func.__name__]=func
print(f'Function {func.__name__} enabled in add_calculation.')
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@property
def coordinates0_point(self):
"""Initial/undeformed cell center coordinates."""
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if self.structured:
return grid_filters.coordinates0_point(self.cells,self.size,self.origin).reshape(-1,3,order='F')
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else:
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with h5py.File(self.fname,'r') as f:
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return f['geometry/x_p'][()]
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@property
def coordinates0_node(self):
"""Initial/undeformed nodal coordinates."""
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if self.structured:
return grid_filters.coordinates0_node(self.cells,self.size,self.origin).reshape(-1,3,order='F')
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else:
with h5py.File(self.fname,'r') as f:
return f['geometry/x_n'][()]
@property
def geometry0(self):
"""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())
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@staticmethod
def _add_absolute(x):
return {
'data': np.abs(x['data']),
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'label': f'|{x["label"]}|',
'meta': {
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'unit': x['meta']['unit'],
'description': f"absolute value of {x['label']} ({x['meta']['description']})",
'creator': 'add_absolute'
}
}
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def add_absolute(self,x):
"""
Add absolute value.
Parameters
----------
x : str
Name of scalar, vector, or tensor dataset to take absolute value of.
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"""
self._add_generic_pointwise(self._add_absolute,{'x':x})
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@staticmethod
def _add_calculation(**kwargs):
formula = kwargs['formula']
for d in re.findall(r'#(.*?)#',formula):
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formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
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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']))
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return {
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'data': data,
'label': kwargs['label'],
'meta': {
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'unit': kwargs['unit'],
'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
'creator': 'add_calculation'
}
}
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def add_calculation(self,formula,name,unit='n/a',description=None):
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"""
Add result of a general formula.
Parameters
----------
formula : str
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Formula to calculate resulting dataset.
Existing datasets are referenced by '#TheirName#'.
name : str
Name of resulting dataset.
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unit : str, optional
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Physical unit of the result.
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description : str, optional
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Human-readable description of the result.
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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')
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>>> r.add_calculation('np.sum(#rho_mob#,axis=1)','rho_mob_total',
... '1/m²','total mobile dislocation density')
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>>> r.add_calculation('np.sum(#rho_dip#,axis=1)','rho_dip_total',
... '1/m²','total dislocation dipole density')
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>>> 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)
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>>> r.add_calculation('equivalent_stress(#F#,#P#)','sigma_vM','Pa',
... 'Mises equivalent of the Cauchy stress')
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"""
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
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def _add_stress_Cauchy(P,F):
return {
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'data': mechanics.stress_Cauchy(P['data'],F['data']),
'label': 'sigma',
'meta': {
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'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'
}
}
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def add_stress_Cauchy(self,P='P',F='F'):
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"""
Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
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Parameters
----------
P : str, optional
Name of the dataset containing the first Piola-Kirchhoff stress. Defaults to 'P'.
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F : str, optional
Name of the dataset containing the deformation gradient. Defaults to 'F'.
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"""
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self._add_generic_pointwise(self._add_stress_Cauchy,{'P':P,'F':F})
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@staticmethod
def _add_determinant(T):
return {
'data': np.linalg.det(T['data']),
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'label': f"det({T['label']})",
'meta': {
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'unit': T['meta']['unit'],
'description': f"determinant of tensor {T['label']} ({T['meta']['description']})",
'creator': 'add_determinant'
}
}
def add_determinant(self,T):
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"""
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')
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"""
self._add_generic_pointwise(self._add_determinant,{'T':T})
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@staticmethod
def _add_deviator(T):
return {
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'data': tensor.deviatoric(T['data']),
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'label': f"s_{T['label']}",
'meta': {
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'unit': T['meta']['unit'],
'description': f"deviator of tensor {T['label']} ({T['meta']['description']})",
'creator': 'add_deviator'
}
}
def add_deviator(self,T):
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"""
Add the deviatoric part of a tensor.
Parameters
----------
T : str
Name of tensor dataset.
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Examples
--------
Add the deviatoric part of Cauchy stress 'sigma':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_deviator('sigma')
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"""
self._add_generic_pointwise(self._add_deviator,{'T':T})
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@staticmethod
def _add_eigenvalue(T_sym,eigenvalue):
if eigenvalue == 'max':
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label,p = 'maximum',2
elif eigenvalue == 'mid':
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label,p = 'intermediate',1
elif eigenvalue == 'min':
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label,p = 'minimum',0
return {
'data': tensor.eigenvalues(T_sym['data'])[:,p],
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'label': f"lambda_{eigenvalue}({T_sym['label']})",
'meta' : {
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'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'):
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"""
Add eigenvalues of symmetric tensor.
Parameters
----------
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T_sym : str
Name of symmetric tensor dataset.
eigenvalue : {'max', 'mid', 'min'}
Eigenvalue. Defaults to 'max'.
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Examples
--------
Add the minimum eigenvalue of Cauchy stress 'sigma':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.add_eigenvalue('sigma','min')
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"""
self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
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@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],
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'label': f"v_{eigenvalue}({T_sym['label']})",
'meta' : {
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'unit': '1',
'description': f"eigenvector corresponding to {label} eigenvalue"
f" of {T_sym['label']} ({T_sym['meta']['description']})",
'creator': 'add_eigenvector'
}
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}
def add_eigenvector(self,T_sym,eigenvalue='max'):
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"""
Add eigenvector of symmetric tensor.
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Parameters
----------
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T_sym : str
Name of symmetric tensor dataset.
eigenvalue : {'max', 'mid', 'min'}
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Eigenvalue to which the eigenvector corresponds.
Defaults to 'max'.
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"""
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
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@staticmethod
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def _add_IPF_color(l,q):
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' : {
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'unit': '8-bit RGB',
'lattice': q['meta']['lattice'],
'description': 'Inverse Pole Figure (IPF) colors along sample direction ({} {} {})'.format(*m),
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'creator': 'add_IPF_color'
}
}
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def add_IPF_color(self,l,q='O'):
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"""
Add RGB color tuple of inverse pole figure (IPF) color.
Parameters
----------
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l : numpy.array of shape (3)
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Lab frame direction for inverse pole figure.
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q : str
Name of the dataset containing the crystallographic orientation as quaternions.
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Defaults to 'O'.
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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]))
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"""
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self._add_generic_pointwise(self._add_IPF_color,{'q':q},{'l':l})
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@staticmethod
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def _add_maximum_shear(T_sym):
return {
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'data': mechanics.maximum_shear(T_sym['data']),
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'label': f"max_shear({T_sym['label']})",
'meta': {
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'unit': T_sym['meta']['unit'],
'description': f"maximum shear component of {T_sym['label']} ({T_sym['meta']['description']})",
'creator': 'add_maximum_shear'
}
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}
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def add_maximum_shear(self,T_sym):
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"""
Add maximum shear components of symmetric tensor.
Parameters
----------
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T_sym : str
Name of symmetric tensor dataset.
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"""
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self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym})
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@staticmethod
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def _add_equivalent_Mises(T_sym,kind):
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k = kind
if k is None:
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if T_sym['meta']['unit'] == '1':
k = 'strain'
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elif T_sym['meta']['unit'] == 'Pa':
k = 'stress'
if k not in ['stress', 'strain']:
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raise ValueError(f'invalid von Mises kind {kind}')
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return {
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'data': (mechanics.equivalent_strain_Mises if k=='strain' else \
mechanics.equivalent_stress_Mises)(T_sym['data']),
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'label': f"{T_sym['label']}_vM",
'meta': {
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'unit': T_sym['meta']['unit'],
'description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['description']})",
'creator': 'add_Mises'
}
}
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def add_equivalent_Mises(self,T_sym,kind=None):
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"""
Add the equivalent Mises stress or strain of a symmetric tensor.
Parameters
----------
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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
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it is selected based on the unit of the dataset ('1' -> strain, 'Pa' -> stress).
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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)')
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"""
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self._add_generic_pointwise(self._add_equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
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@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(f'invalid norm order {ord}')
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return {
'data': np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True),
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'label': f"|{x['label']}|_{o}",
'meta': {
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'unit': x['meta']['unit'],
'description': f"{o}-norm of {t} {x['label']} ({x['meta']['description']})",
'creator': 'add_norm'
}
}
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def add_norm(self,x,ord=None):
"""
Add the norm of vector or tensor.
Parameters
----------
x : str
Name of vector or tensor dataset.
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ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
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Order of the norm. inf means NumPys inf object. For details refer to numpy.linalg.norm.
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"""
self._add_generic_pointwise(self._add_norm,{'x':x},{'ord':ord})
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@staticmethod
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def _add_stress_second_Piola_Kirchhoff(P,F):
return {
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'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
'label': 'S',
'meta': {
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'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'
}
}
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def add_stress_second_Piola_Kirchhoff(self,P='P',F='F'):
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"""
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Add second Piola-Kirchhoff stress calculated from first Piola-Kirchhoff stress and deformation gradient.
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Parameters
----------
P : str, optional
Name of first Piola-Kirchhoff stress dataset. Defaults to 'P'.
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F : str, optional
Name of deformation gradient dataset. Defaults to 'F'.
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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.
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"""
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self._add_generic_pointwise(self._add_stress_second_Piola_Kirchhoff,{'P':P,'F':F})
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@staticmethod
def _add_pole(q,uvw,hkl,with_symmetry):
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c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1
pole = Orientation(q['data'],lattice=q['meta']['lattice'],a=1,c=c).to_pole(uvw=uvw,hkl=hkl,with_symmetry=with_symmetry)
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return {
'data': pole,
'label': 'p^[{} {} {}]'.format(*uvw) if uvw else 'p^({} {} {})'.format(*hkl),
'meta' : {
'unit': '1',
'description': 'lab frame vector along lattice ' \
+ ('direction' if uvw else 'plane') \
+ ('s' if with_symmetry else ''),
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'creator': 'add_pole'
}
}
def add_pole(self,q='O',*,uvw=None,hkl=None,with_symmetry=False):
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"""
Add lab frame vector along lattice direction [uvw] or plane normal (hkl).
Parameters
----------
q : str
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.
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"""
self._add_generic_pointwise(self._add_pole,{'q':q},{'uvw':uvw,'hkl':hkl,'with_symmetry':with_symmetry})
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@staticmethod
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def _add_rotation(F):
return {
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'data': mechanics.rotation(F['data']).as_matrix(),
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'label': f"R({F['label']})",
'meta': {
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'unit': F['meta']['unit'],
'description': f"rotational part of {F['label']} ({F['meta']['description']})",
'creator': 'add_rotation'
}
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}
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def add_rotation(self,F):
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"""
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')
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"""
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self._add_generic_pointwise(self._add_rotation,{'F':F})
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@staticmethod
def _add_spherical(T):
return {
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'data': tensor.spherical(T['data'],False),
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'label': f"p_{T['label']}",
'meta': {
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'unit': T['meta']['unit'],
'description': f"spherical component of tensor {T['label']} ({T['meta']['description']})",
'creator': 'add_spherical'
}
}
def add_spherical(self,T):
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"""
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')
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"""
self._add_generic_pointwise(self._add_spherical,{'T':T})
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@staticmethod
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def _add_strain(F,t,m):
return {
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'data': mechanics.strain(F['data'],t,m),
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'label': f"epsilon_{t}^{m}({F['label']})",
'meta': {
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'unit': F['meta']['unit'],
'description': f"strain tensor of {F['label']} ({F['meta']['description']})",
'creator': 'add_strain'
}
}
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def add_strain(self,F='F',t='V',m=0.0):
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"""
Add strain tensor of a deformation gradient.
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For details, see damask.mechanics.strain.
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Parameters
----------
F : str, optional
Name of deformation gradient dataset. Defaults to 'F'.
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t : {'V', 'U'}, optional
Type of the polar decomposition, 'V' for left stretch tensor and 'U' for right stretch tensor.
Defaults to 'V'.
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m : float, optional
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Order of the strain calculation. Defaults to 0.0.
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Examples
--------
Add the Biot strain based on the deformation gradient 'F':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.strain(t='U',m=0.5)
Add the plastic Euler-Almansi strain based on the
plastic deformation gradient 'F_p':
>>> import damask
>>> r = damask.Result('my_file.hdf5')
>>> r.strain('F_p','V',-1)
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"""
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self._add_generic_pointwise(self._add_strain,{'F':F},{'t':t,'m':m})
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@staticmethod
def _add_stretch_tensor(F,t):
return {
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
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'label': f"{t}({F['label']})",
'meta': {
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'unit': F['meta']['unit'],
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'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
+f"of {F['label']} ({F['meta']['description']})", # noqa
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'creator': 'add_stretch_tensor'
}
}
def add_stretch_tensor(self,F='F',t='V'):
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"""
Add stretch tensor of a deformation gradient.
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Parameters
----------
F : str, optional
Name of deformation gradient dataset. Defaults to 'F'.
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t : {'V', 'U'}, optional
Type of the polar decomposition, 'V' for left stretch tensor and 'U' for right stretch tensor.
Defaults to 'V'.
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"""
self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
@staticmethod
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def _add_curl(f,size):
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'
}
}
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def add_curl(self,f):
"""
Add curl of a field.
Parameters
----------
f : str
Name of vector or tensor field dataset.
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Notes
-----
This function is only available for structured grids,
i.e. results from the grid solver.
"""
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self._add_generic_grid(self._add_curl,{'f':f},{'size':self.size})
@staticmethod
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def _add_divergence(f,size):
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'
}
}
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def add_divergence(self,f):
"""
Add divergence of a field.
Parameters
----------
f : str
Name of vector or tensor field dataset.
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Notes
-----
This function is only available for structured grids,
i.e. results from the grid solver.
"""
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self._add_generic_grid(self._add_divergence,{'f':f},{'size':self.size})
@staticmethod
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def _add_gradient(f,size):
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'
}
}
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def add_gradient(self,f):
"""
Add gradient of a field.
Parameters
----------
f : str
Name of scalar or vector field dataset.
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Notes
-----
This function is only available for structured grids,
i.e. results from the grid solver.
"""
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self._add_generic_grid(self._add_gradient,{'f':f},{'size':self.size})
def _add_generic_grid(self,func,datasets,args={},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.
"""
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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()
with h5py.File(self.fname, 'a') as f:
for increment in self.place(datasets.values(),False).items():
for ty in increment[1].items():
for field in ty[1].items():
d = 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]])
dataset = f[path].create_dataset(r['label'],data=result1)
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 r['meta'].items():
dataset.attrs[l.lower()]=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()
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def _job_pointwise(self,group,func,datasets,args,lock):
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"""Execute job for _add_generic_pointwise."""
try:
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datasets_in = {}
lock.acquire()
with h5py.File(self.fname,'r') as f:
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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()}}
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lock.release()
r = func(**datasets_in,**args)
return [group,r]
except Exception as err:
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print(f'Error during calculation: {err}.')
return [None,None]
def _add_generic_pointwise(self,func,datasets,args={}):
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"""
General function to add pointwise data.
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Parameters
----------
func : function
Callback function that calculates a new dataset from one or
more datasets per HDF5 group.
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datasets : dictionary
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Details of the datasets to be used:
{arg (name to which the data is passed in func): label (in HDF5 file)}.
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args : dictionary, optional
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Arguments parsed to func.
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"""
pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',4)))
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lock = mp.Manager().Lock()
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groups = []
with h5py.File(self.fname,'r') as f:
for inc in self.visible['increments']:
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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()):
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group = '/'.join([inc,ty,label,field])
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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
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default_arg = partial(self._job_pointwise,func=func,datasets=datasets,args=args,lock=lock)
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for group,result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):
if not result:
continue
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lock.acquire()
with h5py.File(self.fname, 'a') as f:
try:
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if not self._protected and '/'.join([group,result['label']]) in f:
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dataset = f['/'.join([group,result['label']])]
dataset[...] = result['data']
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dataset.attrs['overwritten'] = True
else:
shape = result['data'].shape
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if result['data'].size >= chunk_size*2:
chunks = (chunk_size//np.prod(shape[1:]),)+shape[1:]
compression = ('gzip',6)
else:
chunks = shape
compression = (None,None)
dataset = f[group].create_dataset(result['label'],data=result['data'],
maxshape=shape, chunks=chunks,
compression=compression[0], compression_opts=compression[1],
shuffle=True,fletcher32=True)
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now = datetime.datetime.now().astimezone()
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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()
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for l,v in result['meta'].items():
dataset.attrs[l.lower()]=v.encode() if not h5py3 and type(v) is str else v
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creator = dataset.attrs['creator'] if h5py3 else \
dataset.attrs['creator'].decode()
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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:
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print(f'Could not add dataset: {err}.')
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lock.release()
pool.close()
pool.join()
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def export_XDMF(self,output='*'):
"""
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.
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For other cases use `export_VTK`.
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Parameters
----------
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output : (list of) str
Names of the datasets included in the XDMF file.
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Defaults to '*', in which case all datasets are considered.
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"""
if self.N_constituents != 1 or len(self.phases) != 1 or not self.structured:
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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'
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xdmf = ET.Element('Xdmf')
xdmf.attrib={'Version': '2.0',
'xmlns:xi': 'http://www.w3.org/2001/XInclude'}
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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')
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times = [self.times[self.increments.index(i)] for i in self.visible['increments']]
time_data.attrib={'Format': 'XML',
'NumberType': 'Float',
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'Dimensions': f'{len(times)}'}
time_data.text = ' '.join(map(str,times))
attributes = []
data_items = []
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with h5py.File(self.fname,'r') as f:
for inc in self.visible['increments']:
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grid = ET.SubElement(collection,'Grid')
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grid.attrib = {'GridType': 'Uniform',
'Name': inc}
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topology = ET.SubElement(grid, 'Topology')
topology.attrib = {'TopologyType': '3DCoRectMesh',
'Dimensions': '{} {} {}'.format(*(self.cells[::-1]+1))}
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geometry = ET.SubElement(grid, 'Geometry')
geometry.attrib = {'GeometryType':'Origin_DxDyDz'}
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origin = ET.SubElement(geometry, 'DataItem')
origin.attrib = {'Format': 'XML',
'NumberType': 'Float',
'Dimensions': '3'}
origin.text = "{} {} {}".format(*self.origin[::-1])
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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'))
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attributes[-1].attrib = {'Name': 'u / m',
'Center': 'Node',
'AttributeType': 'Vector'}
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
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data_items[-1].attrib = {'Format': 'HDF',
'Precision': '8',
'Dimensions': '{} {} {} 3'.format(*(self.cells[::-1]+1))}
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data_items[-1].text = f'{os.path.split(self.fname)[1]}:/{inc}/geometry/u_n'
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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()):
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name = '/'.join([inc,ty,label,field,out])
shape = f[name].shape[1:]
dtype = f[name].dtype
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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}',
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'Center': 'Cell',
'AttributeType': attribute_type_map[shape]}
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
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data_items[-1].attrib = {'Format': 'HDF',
'NumberType': number_type_map(dtype),
'Precision': f'{dtype.itemsize}',
'Dimensions': '{} {} {} {}'.format(*self.cells[::-1],1 if shape == () else
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np.prod(shape))}
data_items[-1].text = f'{os.path.split(self.fname)[1]}:{name}'
with open(self.fname.with_suffix('.xdmf').name,'w',newline='\n') as f:
f.write(xml.dom.minidom.parseString(ET.tostring(xdmf).decode()).toprettyxml())
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def _mappings(self):
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"""Mappings to place data spatially."""
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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] \
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for label in self.visible['phases']})
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in_data_ph.append({label: f['/'.join(['cell_to','phase'])]['entry'][at_cell_ph[c][label]][:,c] \
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for label in self.visible['phases']})
at_cell_ho = {label: np.where(self.homogenization[:] == label)[0] \
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for label in self.visible['homogenizations']}
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in_data_ho = {label: f['/'.join(['cell_to','homogenization'])]['entry'][at_cell_ho[label]] \
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for label in self.visible['homogenizations']}
return at_cell_ph,in_data_ph,at_cell_ho,in_data_ho
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def export_VTK(self,output='*',mode='cell',constituents=None,fill_float=np.nan,fill_int=0,parallel=True):
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"""
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Export to VTK cell/point data.
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One VTK file per visible increment is created.
For point data, the VTK format is poly data (.vtp).
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For cell data, either an image (.vti) or unstructured (.vtu) dataset
is written for grid-based or mesh-based simulations, respectively.
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Parameters
----------
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output : (list of) str, optional
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Names of the datasets to export to the VTK file.
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Defaults to '*', in which case all datasets are exported.
mode : {'cell', 'point'}
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Export in cell format or point format.
Defaults to 'cell'.
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constituents : (list of) int, optional
Constituents to consider.
Defaults to None, in which case all constituents are considered.
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fill_float : float
Fill value for non-existent entries of floating point type.
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Defaults to NaN.
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fill_int : int
Fill value for non-existent entries of integer type.
Defaults to 0.
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parallel : bool
Write VTK files in parallel in a separate background process.
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Defaults to True.
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"""
if mode.lower()=='cell':
v = self.geometry0
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elif mode.lower()=='point':
v = VTK.from_poly_data(self.coordinates0_point)
else:
raise ValueError(f'invalid mode {mode}')
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v.set_comments(util.execution_stamp('Result','export_VTK'))
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N_digits = int(np.floor(np.log10(max(1,int(self.increments[-1][10:])))))+1
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constituents_ = constituents if isinstance(constituents,Iterable) else \
(range(self.N_constituents) if constituents is None else [constituents])
suffixes = [''] if self.N_constituents == 1 or isinstance(constituents,int) else \
[f'#{c}' for c in constituents_]
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at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
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with h5py.File(self.fname,'r') as f:
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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.add_comments(f'{creator} ({created})')
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for inc in util.show_progress(self.visible['increments']):
u = _read(f['/'.join([inc,'geometry','u_n' if mode.lower() == 'cell' else 'u_p'])])
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v.add(u,'u')
for ty in ['phase','homogenization']:
for field in self.visible['fields']:
outs = {}
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for label in self.visible[ty+'s']:
if field not in f['/'.join([inc,ty,label])].keys(): continue
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for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
data = ma.array(_read(f['/'.join([inc,ty,label,field,out])]))
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if ty == 'phase':
if out+suffixes[0] not in outs.keys():
for c,suffix in zip(constituents_,suffixes):
outs[out+suffix] = \
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_empty_like(data,self.N_materialpoints,fill_float,fill_int)
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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():
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outs[out] = _empty_like(data,self.N_materialpoints,fill_float,fill_int)
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outs[out][at_cell_ho[label]] = data[in_data_ho[label]]
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for label,dataset in outs.items():
v.add(dataset,' / '.join(['/'.join([ty,field,label]),dataset.dtype.metadata['unit']]))
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v.save(f'{self.fname.stem}_inc{inc[10:].zfill(N_digits)}',parallel=parallel)
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def get(self,output='*',flatten=True,prune=True):
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"""
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Collect data per phase/homogenization reflecting the group/folder structure in the DADF5 file.
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Parameters
----------
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output : (list of) str
Names of the datasets to read.
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Defaults to '*', in which case all datasets are read.
flatten : bool
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
Remove branches with no data. Defaults to True.
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Returns
-------
data : dict of numpy.ndarray
Datasets structured by phase/homogenization and according to selected view.
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"""
r = {}
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with h5py.File(self.fname,'r') as f:
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for inc in util.show_progress(self.visible['increments']):
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r[inc] = {'phase':{},'homogenization':{},'geometry':{}}
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for out in _match(output,f['/'.join([inc,'geometry'])].keys()):
r[inc]['geometry'][out] = _read(f['/'.join([inc,'geometry',out])])
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for ty in ['phase','homogenization']:
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for label in self.visible[ty+'s']:
r[inc][ty][label] = {}
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
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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])])
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if prune: r = util.dict_prune(r)
if flatten: r = util.dict_flatten(r)
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return None if (type(r) == dict and r == {}) else r
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def place(self,output='*',flatten=True,prune=True,constituents=None,fill_float=np.nan,fill_int=0):
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"""
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Merge data into spatial order that is compatible with the damask.VTK geometry representation.
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The returned data structure reflects the group/folder structure
in the DADF5 file.
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Multi-phase data is fused into a single output.
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`place` is equivalent to `get` if only one phase/homogenization
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and one constituent is present.
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Parameters
----------
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output : (list of) str, optional
Names of the datasets to read.
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Defaults to '*', in which case all datasets are placed.
flatten : bool
Remove singular levels of the folder hierarchy.
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This might be beneficial in case of single increment or field.
Defaults to True.
prune : bool
Remove branches with no data. Defaults to True.
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constituents : (list of) int, optional
Constituents to consider.
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Defaults to None, in which case all constituents are considered.
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fill_float : float
Fill value for non-existent entries of floating point type.
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Defaults to NaN.
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fill_int : int
Fill value for non-existent entries of integer type.
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Defaults to 0.
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Returns
-------
data : dict of numpy.ma.MaskedArray
Datasets structured by spatial position and according to selected view.
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"""
r = {}
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constituents_ = constituents if isinstance(constituents,Iterable) else \
(range(self.N_constituents) if constituents is None else [constituents])
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suffixes = [''] if self.N_constituents == 1 or isinstance(constituents,int) else \
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[f'#{c}' for c in constituents_]
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at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
with h5py.File(self.fname,'r') as f:
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for inc in util.show_progress(self.visible['increments']):
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r[inc] = {'phase':{},'homogenization':{},'geometry':{}}
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for out in _match(output,f['/'.join([inc,'geometry'])].keys()):
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r[inc]['geometry'][out] = ma.array(_read(f['/'.join([inc,'geometry',out])]),fill_value = fill_float)
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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()):
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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])]))
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if ty == 'phase':
if out+suffixes[0] not in r[inc][ty][field].keys():
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for c,suffix in zip(constituents_,suffixes):
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r[inc][ty][field][out+suffix] = \
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_empty_like(data,self.N_materialpoints,fill_float,fill_int)
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for c,suffix in zip(constituents_,suffixes):
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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] = \
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_empty_like(data,self.N_materialpoints,fill_float,fill_int)
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r[inc][ty][field][out][at_cell_ho[label]] = data[in_data_ho[label]]
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if prune: r = util.dict_prune(r)
if flatten: r = util.dict_flatten(r)
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return None if (type(r) == dict and r == {}) else r
def export_setup(self,output='*',overwrite=False):
"""
Export configuration files.
Parameters
----------
output : (list of) str, optional
Names of the datasets to export to the file.
Defaults to '*', in which case all datasets are exported.
overwrite : boolean, optional
Overwrite existing configuration files.
Defaults to False.
"""
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def export(name,obj,output,overwrite):
if type(obj) == h5py.Dataset and _match(output,[name]):
d = obj.attrs['description'] if h5py3 else obj.attrs['description'].decode()
if not Path(name).exists() or overwrite:
with open(name,'w') as f_out: f_out.write(obj[0].decode())
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print(f"Exported {d} to '{name}'.")
else:
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print(f"'{name}' exists, {d} not exported.")
elif type(obj) == h5py.Group:
os.makedirs(name, exist_ok=True)
with h5py.File(self.fname,'r') as f_in:
f_in['setup'].visititems(partial(export,output=output,overwrite=overwrite))