DAMASK_EICMD/python/damask/_result.py

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import multiprocessing as mp
import re
import glob
import os
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import datetime
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
from numpy.lib import recfunctions as rfn
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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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def _read(handle,path):
metadata = {k:(v if h5py3 else v.decode()) for k,v in handle[path].attrs.items()}
dtype = np.dtype(handle[path].dtype,metadata=metadata)
return np.array(handle[path],dtype=dtype)
class Result:
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"""
Manipulate and read DADF5 files.
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DADF5 (DAMASK HDF5) files contain DAMASK results.
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The group/folder structure reflects the input data
in material.yaml.
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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']
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if self.version_major != 0 or not 7 <= self.version_minor <= 12:
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raise TypeError(f'Unsupported DADF5 version {self.version_major}.{self.version_minor}')
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self.structured = 'grid' in f['geometry'].attrs.keys() or \
'cells' in f['geometry'].attrs.keys()
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if self.structured:
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try:
self.cells = f['geometry'].attrs['cells']
except KeyError:
self.cells = f['geometry'].attrs['grid']
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self.size = f['geometry'].attrs['size']
self.origin = f['geometry'].attrs['origin']
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r=re.compile('inc[0-9]+' if self.version_minor < 12 else 'increment_[0-9]+')
increments_unsorted = {int(i[10:]):i for i in f.keys() if r.match(i)}
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self.increments = [increments_unsorted[i] for i in sorted(increments_unsorted)]
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self.times = [round(f[i].attrs['time/s'],12) for i in self.increments] if self.version_minor < 12 else \
[round(f[i].attrs['t/s'],12) for i in self.increments]
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grp = 'mapping' if self.version_minor < 12 else 'cell_to'
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self.N_materialpoints, self.N_constituents = np.shape(f[f'{grp}/phase'])
self.homogenizations = [m.decode() for m in np.unique(f[f'{grp}/homogenization']
['Name' if self.version_minor < 12 else 'label'])]
self.phases = [c.decode() for c in np.unique(f[f'{grp}/phase']
['Name' if self.version_minor < 12 else 'label'])]
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self.out_type_ph = []
for c in self.phases:
self.out_type_ph += f['/'.join([self.increments[0],'phase',c])].keys()
self.out_type_ph = list(set(self.out_type_ph)) # make unique
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self.out_type_ho = []
for m in self.homogenizations:
self.out_type_ho += f['/'.join([self.increments[0],'homogenization',m])].keys()
self.out_type_ho = list(set(self.out_type_ho)) # make unique
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self.visible = {'increments': self.increments,
'phases': self.phases,
'homogenizations': self.homogenizations,
'out_type_ph': self.out_type_ph,
'out_type_ho': self.out_type_ho
}
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self.fname = Path(fname).absolute()
self._allow_modification = False
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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self.view('increments',visible_increments[0:1])
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first = self.list_data()
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self.view('increments',visible_increments[-1:])
last = '' if len(visible_increments) < 2 else self.list_data()
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self.view('increments',visible_increments)
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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).
datasets : list of str or bool
Name of datasets as list; supports ? and * wildcards.
True is equivalent to [*], False is equivalent to [].
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"""
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def natural_sort(key):
convert = lambda text: int(text) if text.isdigit() else text
return [ convert(c) for c in re.split('([0-9]+)', key) ]
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# allow True/False and string arguments
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if datasets is True:
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datasets = ['*']
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elif datasets is False:
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datasets = []
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choice = datasets if hasattr(datasets,'__iter__') and not isinstance(datasets,str) else \
[datasets]
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inc = 'inc' if self.version_minor < 12 else 'increment_' # compatibility hack
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if what == 'increments':
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choice = [c if isinstance(c,str) and c.startswith(inc) else
f'{inc}{c}' for c in choice]
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elif what == 'times':
what = 'increments'
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])
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valid = [e for e_ in [glob.fnmatch.filter(getattr(self,what),s) for s in choice] for e in e_]
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existing = set(self.visible[what])
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if action == 'set':
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self.visible[what] = valid
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elif action == 'add':
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add = existing.union(valid)
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add_sorted = sorted(add, key=natural_sort)
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self.visible[what] = add_sorted
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elif action == 'del':
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diff = existing.difference(valid)
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diff_sorted = sorted(diff, key=natural_sort)
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self.visible[what] = diff_sorted
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def _get_attribute(self,path,attr):
"""
Get the attribute of a dataset.
Parameters
----------
Path : str
Path to the dataset.
attr : str
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Name of the attribute to get.
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Returns
-------
attr at path, str or None.
The requested attribute, None if not found.
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"""
with h5py.File(self.fname,'r') as f:
try:
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return f[path].attrs[attr] if h5py3 else f[path].attrs[attr].decode()
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except KeyError:
return None
def allow_modification(self):
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"""Allow to overwrite existing data."""
print(util.warn('Warning: Modification of existing datasets allowed!'))
self._allow_modification = True
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def disallow_modification(self):
"""Disallow to overwrite existing data (default case)."""
self._allow_modification = False
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def increments_in_range(self,start,end):
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"""
Select all increments within a given range.
Parameters
----------
start : int or str
Start increment.
end : int or str
End increment.
"""
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# compatibility hack
ln = 3 if self.version_minor < 12 else 10
selected = []
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for i,inc in enumerate([int(i[ln:]) for i in self.increments]):
s,e = map(lambda x: int(x[ln:] 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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"""
Select all increments within a given time range.
Parameters
----------
start : float
Time of start increment.
end : float
Time of end increment.
"""
selected = []
for i,time in enumerate(self.times):
if start <= time <= end:
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selected.append(self.times[i])
return selected
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def iterate(self,what):
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"""
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Iterate over visible items and view them independently.
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Parameters
----------
what : str
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Attribute to change (must be from self.visible).
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"""
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datasets = self.visible[what]
last_view = datasets.copy()
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for dataset in datasets:
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if last_view != self.visible[what]:
self._manage_view('set',what,datasets)
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raise Exception
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self._manage_view('set',what,dataset)
last_view = self.visible[what]
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yield dataset
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self._manage_view('set',what,datasets)
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def view(self,what,datasets):
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"""
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Set view.
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Parameters
----------
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what : str
Attribute to change (must be from self.visible).
datasets : list of str or bool
Name of datasets as list; supports ? and * wildcards.
True is equivalent to [*], False is equivalent to [].
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"""
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self._manage_view('set',what,datasets)
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def view_more(self,what,datasets):
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"""
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Add to view.
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Parameters
----------
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what : str
Attribute to change (must be from self.visible).
datasets : list of str or bool
Name of datasets as list; supports ? and * wildcards.
True is equivalent to [*], False is equivalent to [].
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"""
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self._manage_view('add',what,datasets)
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def view_less(self,what,datasets):
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"""
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Delete from view.
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Parameters
----------
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what : str
Attribute to change (must be from self.visible).
datasets : list of str or bool
Name of datasets as list; supports ? and * wildcards.
True is equivalent to [*], False is equivalent to [].
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"""
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self._manage_view('del',what,datasets)
def rename(self,name_old,name_new):
"""
Rename dataset.
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Parameters
----------
name_old : str
Name of the dataset to be renamed.
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name_new : str
New name of the dataset.
"""
if self._allow_modification:
with h5py.File(self.fname,'a') as f:
for path_old in self.get_dataset_location(name_old):
path_new = os.path.join(os.path.dirname(path_old),name_new)
f[path_new] = f[path_old]
f[path_new].attrs['Renamed'] = f'Original name: {name_old}' if h5py3 else \
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f'Original name: {name_old}'.encode()
del f[path_old]
else:
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raise PermissionError('Rename operation not permitted')
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def groups_with_datasets(self,datasets):
"""
Return groups that contain all requested datasets.
Only groups within
- inc*/phase/*/*
- inc*/homogenization/*/*
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- inc*/geometry/*
are considered as they contain user-relevant data.
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Single strings will be treated as list with one entry.
Wild card matching is allowed, but the number of arguments needs to fit.
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Parameters
----------
datasets : iterable or str or bool
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Examples
--------
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datasets = False matches no group
datasets = True matches all groups
datasets = ['F','P'] matches a group with ['F','P','sigma']
datasets = ['*','P'] matches a group with ['F','P']
datasets = ['*'] does not match a group with ['F','P','sigma']
datasets = ['*','*'] does not match a group with ['F','P','sigma']
datasets = ['*','*','*'] matches a group with ['F','P','sigma']
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"""
if datasets is False: return []
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sets = datasets if isinstance(datasets,bool) or (hasattr(datasets,'__iter__') and not isinstance(datasets,str)) else \
[datasets]
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groups = []
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with h5py.File(self.fname,'r') as f:
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for i in self.iterate('increments'):
for o,p in zip(['phases','homogenizations'],['out_type_ph','out_type_ho']):
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for oo in self.iterate(o):
for pp in self.iterate(p):
group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue
if sets is True:
groups.append(group)
else:
if group in f.keys():
match = [e for e_ in [glob.fnmatch.filter(f[group].keys(),s) for s in sets] for e in e_]
if len(set(match)) == len(sets): groups.append(group)
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return groups
def list_data(self):
"""Return information on all active datasets in the file."""
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# compatibility hack
de = 'Description' if self.version_minor < 12 else 'description'
un = 'Unit' if self.version_minor < 12 else 'unit'
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message = ''
with h5py.File(self.fname,'r') as f:
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for i in self.iterate('increments'):
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message += f'\n{i} ({self.times[self.increments.index(i)]}s)\n'
for o,p in zip(['phases','homogenizations'],['out_type_ph','out_type_ho']):
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message += f' {o[:-1]}\n'
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for oo in self.iterate(o):
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message += f' {oo}\n'
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for pp in self.iterate(p):
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message += f' {pp}\n'
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group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue
for d in f[group].keys():
try:
dataset = f['/'.join([group,d])]
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if un in dataset.attrs:
unit = f" / {dataset.attrs[un]}" if h5py3 else \
f" / {dataset.attrs[un].decode()}"
else:
unit = ''
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description = dataset.attrs[de] if h5py3 else \
dataset.attrs[de].decode()
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message += f' {d}{unit}: {description}\n'
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except KeyError:
pass
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return message
def get_dataset_location(self,label):
"""Return the location of all active datasets with given label."""
path = []
with h5py.File(self.fname,'r') as f:
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for i in self.iterate('increments'):
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k = '/'.join([i,'geometry',label])
try:
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f[k]
path.append(k)
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except KeyError:
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pass
for o,p in zip(['phases','homogenizations'],['out_type_ph','out_type_ho']):
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for oo in self.iterate(o):
for pp in self.iterate(p):
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k = '/'.join([i,o[:-1],oo,pp,label])
try:
f[k]
path.append(k)
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except KeyError:
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pass
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return path
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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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def read_dataset(self,path,c=0,plain=False):
"""
Dataset for all points/cells.
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If more than one path is given, the dataset is composed of the individual contributions.
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Parameters
----------
path : list of strings
The name of the datasets to consider.
c : int, optional
The constituent to consider. Defaults to 0.
plain: boolean, optional
Convert into plain numpy datatype.
Only relevant for compound datatype, e.g. the orientation.
Defaults to False.
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"""
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# compatibility hack
name = 'Name' if self.version_minor < 12 else 'label'
member = 'Position' if self.version_minor < 12 else 'entry'
grp = 'mapping' if self.version_minor < 12 else 'cell_to'
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with h5py.File(self.fname,'r') as f:
shape = (self.N_materialpoints,) + np.shape(f[path[0]])[1:]
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if len(shape) == 1: shape = shape +(1,)
dataset = np.full(shape,np.nan,dtype=np.dtype(f[path[0]]))
for pa in path:
label = pa.split('/')[2]
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if pa.split('/')[1] == 'geometry':
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dataset = np.array(f[pa])
continue
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p = np.where(f[f'{grp}/phase'][:,c][name] == str.encode(label))[0]
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if len(p)>0:
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u = (f[f'{grp}/phase'][member][p,c])
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a = np.array(f[pa])
if len(a.shape) == 1:
a=a.reshape([a.shape[0],1])
dataset[p,:] = a[u,:]
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p = np.where(f[f'{grp}/homogenization'][name] == str.encode(label))[0]
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if len(p)>0:
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u = (f[f'{grp}/homogenization'][member][p.tolist()])
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a = np.array(f[pa])
if len(a.shape) == 1:
a=a.reshape([a.shape[0],1])
dataset[p,:] = a[u,:]
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if plain and dataset.dtype.names is not None:
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return dataset.view(('float64',len(dataset.dtype.names)))
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else:
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return dataset
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@property
def coordinates0_point(self):
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"""Return initial coordinates of the cell centers."""
if self.structured:
return grid_filters.coordinates0_point(self.cells,self.size,self.origin).reshape(-1,3,order='F')
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else:
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with h5py.File(self.fname,'r') as f:
return f['geometry/x_c'][()]
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@property
def coordinates0_node(self):
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"""Return initial coordinates of the cell centers."""
if self.structured:
return grid_filters.coordinates0_node(self.cells,self.size,self.origin).reshape(-1,3,order='F')
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else:
with h5py.File(self.fname,'r') as f:
return f['geometry/x_n'][()]
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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
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Label 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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return {
'data': eval(formula),
'label': kwargs['label'],
'meta': {
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'unit': kwargs['unit'],
'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
'creator': 'add_calculation'
}
}
def add_calculation(self,label,formula,unit='n/a',description=None):
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"""
Add result of a general formula.
Parameters
----------
label : str
Label of resulting dataset.
formula : str
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Formula to calculate resulting dataset. Existing datasets are referenced by #TheirLabel#.
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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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"""
dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
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args = {'formula':formula,'label':label,'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
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Label of the dataset containing the first Piola-Kirchhoff stress. Defaults to P.
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F : str, optional
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Label 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
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Label of tensor dataset.
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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
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Label of tensor dataset.
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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
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Label of symmetric tensor dataset.
eigenvalue : str, optional
Eigenvalue. Select from max, mid, min. Defaults to max.
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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
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Label of symmetric tensor dataset.
eigenvalue : str, optional
Eigenvalue to which the eigenvector corresponds. Select from
max, mid, min. 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))
try:
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lattice = {'fcc':'cF','bcc':'cI','hex':'hP'}[q['meta']['lattice']]
except KeyError:
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lattice = q['meta']['lattice']
try:
o = Orientation(rotation = (rfn.structured_to_unstructured(q['data'])),lattice=lattice)
except ValueError:
o = Orientation(rotation = q['data'],lattice=lattice)
return {
'data': np.uint8(o.IPF_color(l)*255),
'label': 'IPFcolor_[{} {} {}]'.format(*m),
'meta' : {
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'unit': '8-bit RGB',
'lattice': q['meta']['lattice'],
'description': 'Inverse Pole Figure (IPF) colors along sample direction [{} {} {}]'.format(*m),
'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
Label of the dataset containing the crystallographic orientation as quaternions.
Defaults to 'O'.
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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
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Label 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']:
raise ValueError('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
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Label of symmetric tensorial stress or strain dataset.
kind : {'stress', 'strain', None}, optional
Kind of the von Mises equivalent. Defaults to None, in which case
it is selected based on the unit of the dataset ('1' -> strain, 'Pa' -> stress').
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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
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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
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Label 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
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Label of first Piola-Kirchhoff stress dataset. Defaults to P.
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F : str, optional
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Label of deformation gradient dataset. Defaults to F.
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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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# The add_pole functionality needs discussion.
# The new Crystal object can perform such a calculation but the outcome depends on the lattice parameters
# as well as on whether a direction or plane is concerned (see the DAMASK_examples/pole_figure notebook).
# Below code appears to be too simplistic.
# @staticmethod
# def _add_pole(q,p,polar):
# pole = np.array(p)
# unit_pole = pole/np.linalg.norm(pole)
# m = util.scale_to_coprime(pole)
# rot = Rotation(q['data'].view(np.double).reshape(-1,4))
#
# rotatedPole = rot @ np.broadcast_to(unit_pole,rot.shape+(3,)) # rotate pole according to crystal orientation
# xy = rotatedPole[:,0:2]/(1.+abs(unit_pole[2])) # stereographic projection
# coords = xy if not polar else \
# np.block([np.sqrt(xy[:,0:1]*xy[:,0:1]+xy[:,1:2]*xy[:,1:2]),np.arctan2(xy[:,1:2],xy[:,0:1])])
# return {
# 'data': coords,
# 'label': 'p^{}_[{} {} {})'.format(u'rφ' if polar else 'xy',*m),
# 'meta' : {
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# 'unit': '1',
# 'description': '{} coordinates of stereographic projection of pole (direction/plane) in crystal frame'\
# .format('Polar' if polar else 'Cartesian'),
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# 'creator': 'add_pole'
# }
# }
# def add_pole(self,q,p,polar=False):
# """
# Add coordinates of stereographic projection of given pole in crystal frame.
#
# Parameters
# ----------
# q : str
# Label of the dataset containing the crystallographic orientation as quaternions.
# p : numpy.array of shape (3)
# Crystallographic direction or plane.
# polar : bool, optional
# Give pole in polar coordinates. Defaults to False.
#
# """
# self._add_generic_pointwise(self._add_pole,{'q':q},{'p':p,'polar':polar})
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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, optional
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Label of deformation gradient dataset.
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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
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Label of tensor dataset.
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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 refer to damask.mechanics.strain
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Parameters
----------
F : str, optional
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Label of deformation gradient dataset. Defaults to F.
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t : {V, U}, optional
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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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"""
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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'],
'description': '{} stretch tensor of {} ({})'.format('left' if t.upper() == 'V' else 'right',
F['label'],F['meta']['description']),
'creator': 'add_stretch_tensor'
}
}
def add_stretch_tensor(self,F='F',t='V'):
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"""
Add stretch tensor of a deformation gradient.
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Parameters
----------
F : str, optional
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Label of deformation gradient dataset. Defaults to F.
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t : {V, U}, optional
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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})
def _job(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 if h5py3 else v.decode()) 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
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
Details of the datasets to be used: label (in HDF5 file) and
arg (argument to which the data is parsed in func).
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args : dictionary, optional
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Arguments parsed to func.
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"""
chunk_size = 1024**2//8
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pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',1)))
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lock = mp.Manager().Lock()
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groups = self.groups_with_datasets(datasets.values())
if len(groups) == 0:
print('No matching dataset found, no data was added.')
return
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default_arg = partial(self._job,func=func,datasets=datasets,args=args,lock=lock)
for result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):
if not result:
continue
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lock.acquire()
with h5py.File(self.fname, 'a') as f:
try:
if self._allow_modification and result[0]+'/'+result[1]['label'] in f:
dataset = f[result[0]+'/'+result[1]['label']]
dataset[...] = result[1]['data']
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dataset.attrs['overwritten'] = True
else:
if result[1]['data'].size >= chunk_size*2:
shape = result[1]['data'].shape
chunks = (chunk_size//np.prod(shape[1:]),)+shape[1:]
dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'],
maxshape=shape, chunks=chunks,
compression='gzip', compression_opts=6,
shuffle=True,fletcher32=True)
else:
dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'])
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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[1]['meta'].items():
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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()
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 save_XDMF(self):
"""
Write XDMF file to directly visualize data in DADF5 file.
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The view is not taken into account, i.e. the content of the
whole file will be included.
"""
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# compatibility hack
u = 'Unit' if self.version_minor < 12 else 'unit'
if self.N_constituents != 1 or len(self.phases) != 1 or not self.structured:
raise TypeError('XDMF output requires homogeneous grid')
attribute_type_map = defaultdict(lambda:'Matrix', ( ((),'Scalar'), ((3,),'Vector'), ((3,3),'Tensor')) )
def number_type_map(dtype):
if dtype in np.sctypes['int']: return 'Int'
if dtype in np.sctypes['uint']: return 'UInt'
if dtype in np.sctypes['float']: return 'Float'
xdmf=ET.Element('Xdmf')
xdmf.attrib={'Version': '2.0',
'xmlns:xi': 'http://www.w3.org/2001/XInclude'}
domain=ET.SubElement(xdmf, 'Domain')
collection = ET.SubElement(domain, 'Grid')
collection.attrib={'GridType': 'Collection',
'CollectionType': 'Temporal'}
time = ET.SubElement(collection, 'Time')
time.attrib={'TimeType': 'List'}
time_data = ET.SubElement(time, 'DataItem')
time_data.attrib={'Format': 'XML',
'NumberType': 'Float',
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'Dimensions': f'{len(self.times)}'}
time_data.text = ' '.join(map(str,self.times))
attributes = []
data_items = []
for inc in self.increments:
grid=ET.SubElement(collection,'Grid')
grid.attrib = {'GridType': 'Uniform',
'Name': inc}
topology=ET.SubElement(grid, 'Topology')
topology.attrib={'TopologyType': '3DCoRectMesh',
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'Dimensions': '{} {} {}'.format(*self.cells+1)}
geometry=ET.SubElement(grid, 'Geometry')
geometry.attrib={'GeometryType':'Origin_DxDyDz'}
origin=ET.SubElement(geometry, 'DataItem')
origin.attrib={'Format': 'XML',
'NumberType': 'Float',
'Dimensions': '3'}
origin.text="{} {} {}".format(*self.origin)
delta=ET.SubElement(geometry, 'DataItem')
delta.attrib={'Format': 'XML',
'NumberType': 'Float',
'Dimensions': '3'}
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delta.text="{} {} {}".format(*(self.size/self.cells))
with h5py.File(self.fname,'r') as f:
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'))
data_items[-1].attrib={'Format': 'HDF',
'Precision': '8',
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'Dimensions': '{} {} {} 3'.format(*(self.cells+1))}
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data_items[-1].text=f'{os.path.split(self.fname)[1]}:/{inc}/geometry/u_n'
for o,p in zip(['phases','homogenizations'],['out_type_ph','out_type_ho']):
for oo in getattr(self,o):
for pp in getattr(self,p):
g = '/'.join([inc,o[:-1],oo,pp])
for l in f[g]:
name = '/'.join([g,l])
shape = f[name].shape[1:]
dtype = f[name].dtype
if dtype not in np.sctypes['int']+np.sctypes['uint']+np.sctypes['float']: continue
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unit = f[name].attrs[u] if h5py3 else f[name].attrs[u].decode()
attributes.append(ET.SubElement(grid, 'Attribute'))
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attributes[-1].attrib={'Name': name.split('/',2)[2]+f' / {unit}',
'Center': 'Cell',
'AttributeType': attribute_type_map[shape]}
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
data_items[-1].attrib={'Format': 'HDF',
'NumberType': number_type_map(dtype),
'Precision': f'{dtype.itemsize}',
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'Dimensions': '{} {} {} {}'.format(*self.cells,1 if shape == () else
np.prod(shape))}
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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())
def save_VTK(self,labels=[],mode='cell'):
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"""
Export to vtk cell/point data.
Parameters
----------
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labels : str or list of, optional
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Labels of the datasets to be exported.
mode : str, either 'cell' or 'point'
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Export in cell format or point format.
Defaults to 'cell'.
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"""
if mode.lower()=='cell':
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if self.structured:
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v = VTK.from_rectilinear_grid(self.cells,self.size,self.origin)
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else:
with h5py.File(self.fname,'r') as f:
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v = VTK.from_unstructured_grid(f['/geometry/x_n'][()],
f['/geometry/T_c'][()]-1,
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f['/geometry/T_c'].attrs['VTK_TYPE'] if h5py3 else \
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f['/geometry/T_c'].attrs['VTK_TYPE'].decode())
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elif mode.lower()=='point':
v = VTK.from_poly_data(self.coordinates0_point)
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# compatibility hack
ln = 3 if self.version_minor < 12 else 10
N_digits = int(np.floor(np.log10(max(1,int(self.increments[-1][ln:])))))+1
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for inc in util.show_progress(self.iterate('increments'),len(self.visible['increments'])):
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viewed_backup_ho = self.visible['homogenizations'].copy()
self.view('homogenizations',False)
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for label in (labels if isinstance(labels,list) else [labels]):
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for o in self.iterate('out_type_ph'):
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for c in range(self.N_constituents):
prefix = '' if self.N_constituents == 1 else f'constituent{c}/'
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if o not in ['mechanics', 'mechanical']: # compatibility hack
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for _ in self.iterate('phases'):
path = self.get_dataset_location(label)
if len(path) == 0:
continue
array = self.read_dataset(path,c)
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v.add(array,prefix+path[0].split('/',1)[1]+f' / {self._get_attribute(path[0],"unit")}')
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else:
paths = self.get_dataset_location(label)
if len(paths) == 0:
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continue
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array = self.read_dataset(paths,c)
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if self.version_minor < 12:
ph_name = re.compile(r'(?<=(phase\/))(.*?)(?=(mechanics))') # identify phase name
else:
ph_name = re.compile(r'(?<=(phase\/))(.*?)(?=(mechanical))') # identify phase name
dset_name = prefix+re.sub(ph_name,r'',paths[0].split('/',1)[1]) # remove phase name
v.add(array,dset_name+f' / {self._get_attribute(paths[0],"unit")}')
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self.view('homogenizations',viewed_backup_ho)
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viewed_backup_ph = self.visible['phases'].copy()
self.view('phases',False)
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for label in (labels if isinstance(labels,list) else [labels]):
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for _ in self.iterate('out_type_ho'):
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paths = self.get_dataset_location(label)
if len(paths) == 0:
continue
array = self.read_dataset(paths)
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v.add(array,paths[0].split('/',1)[1]+f' / {self._get_attribute(paths[0],"unit")}')
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self.view('phases',viewed_backup_ph)
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u = self.read_dataset(self.get_dataset_location('u_n' if mode.lower() == 'cell' else 'u_p'))
v.add(u,'u')
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v.save(f'{self.fname.stem}_inc{inc[ln:].zfill(N_digits)}')
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def read(self,labels,compress=True,strip=True):
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"""
Export data from file per phase/homogenization.
The returned data structure reflects the group/folder structure
in the DADF5 file.
Parameters
----------
labels : str or list of, optional
Labels of the datasets to be read.
compress : bool
Squeeze out dictionaries that are not needed for a unique
structure. This might be beneficial in the case of single
constituent or single phase simulations or if only one
time increment is considered. Defaults to 'True'.
strip : bool
Remove branches that contain no dataset. Defaults to
'True'.
"""
r = {}
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labels_ = set([labels] if isinstance(labels,str) else labels)
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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 la in labels_.intersection(f['/'.join((inc,'geometry'))].keys()):
r[inc]['geometry'][la] = _read(f,'/'.join((inc,'geometry',la)))
for ty in ['phase','homogenization']:
for na in self.visible[ty+'s']:
r[inc][ty][na] = {}
for field in f['/'.join((inc,ty,na))].keys():
r[inc][ty][na][field] = {}
for la in labels_.intersection(f['/'.join((inc,ty,na,field))].keys()):
r[inc][ty][na][field][la] = _read(f,'/'.join((inc,ty,na,field,la)))
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if strip: r = util.dict_strip(r)
if compress: r = util.dict_compress(r)
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return r
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def place(self,labels,compress=True,strip=True,constituents=None,fill_float=0.0,fill_int=0):
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"""
Export data from file suitable sorted for spatial operations.
The returned data structure reflects the group/folder structure
in the DADF5 file. In the case of multi phase simulations, the
data is merged from the individual phases/homogenizations.
In the cases of a single constituent and single phase simulation
this function is equivalent to `read`.
Parameters
----------
labels : str or list of, optional
Labels of the datasets to be read.
compress : bool
Squeeze out dictionaries that are not needed for a unique
structure. This might be beneficial in the case of single
phase simulations or if only one time increment is
considered. Defaults to 'True'.
strip : bool
Remove branches that contain no dataset. Defaults to
'True'.
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constituents : int or list of, optional
Constituents to consider. Defaults to 'None', in which case
all constituents are considered.
fill_float : float
Fill value for non existent entries of floating point type.
Defaults to 0.0.
fill_int : int
Fill value for non existent entries of integer type.
Defaults to 0.
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"""
r = {}
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labels_ = set([labels] if isinstance(labels,str) else labels)
if constituents is None:
constituents_ = range(self.N_constituents)
else:
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constituents_ = constituents if isinstance(constituents,Iterable) else [constituents]
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suffixes = [''] if self.N_constituents == 1 or len(constituents_) == 1 else \
[f'#{c}' for c in constituents_]
grp = 'mapping' if self.version_minor < 12 else 'cell_to'
name = 'Name' if self.version_minor < 12 else 'label'
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member = 'member' if self.version_minor < 12 else 'entry'
with h5py.File(self.fname,'r') as f:
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at_cell_ph = []
in_data_ph = []
for c in constituents_:
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at_cell_ph.append({label: np.where(f[os.path.join(grp,'phase')][:,c][name] == label.encode())[0] \
for label in self.visible['phases']})
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in_data_ph.append({label: f[os.path.join(grp,'phase')][member][at_cell_ph[c][label]][...,0] \
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for label in self.visible['phases']})
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at_cell_ho = {label: np.where(f[os.path.join(grp,'homogenization')][:][name] == label.encode())[0] \
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for label in self.visible['homogenizations']}
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in_data_ho = {label: f[os.path.join(grp,'homogenization')][member][at_cell_ho[label]] \
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for label in self.visible['homogenizations']}
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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 la in labels_.intersection(f[os.path.join(inc,'geometry')].keys()):
r[inc]['geometry'][la] = _read(f,os.path.join(inc,'geometry',la))
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for ph in self.visible['phases']:
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for field in f[os.path.join(inc,'phase',ph)].keys():
if field not in r[inc]['phase'].keys():
r[inc]['phase'][field] = {}
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for la in labels_.intersection(f[os.path.join(inc,'phase',ph,field)].keys()):
data = ma.array(_read(f,os.path.join(inc,'phase',ph,field,la)))
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if la+suffixes[0] not in r[inc]['phase'][field].keys():
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container = np.empty((self.N_materialpoints,)+data.shape[1:],dtype=data.dtype)
fill_value = fill_float if data.dtype in np.sctypes['float'] else \
fill_int
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for c,suffix in zip(constituents_, suffixes):
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r[inc]['phase'][field][la+suffix] = \
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ma.array(container,fill_value=fill_value,mask=True)
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for c,suffix in zip(constituents_, suffixes):
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r[inc]['phase'][field][la+suffix][at_cell_ph[c][ph]] = data[in_data_ph[c][ph]]
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for ho in self.visible['homogenizations']:
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for field in f[os.path.join(inc,'homogenization',ho)].keys():
if field not in r[inc]['homogenization'].keys():
r[inc]['homogenization'][field] = {}
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for la in labels_.intersection(f[os.path.join(inc,'homogenization',ho,field)].keys()):
data = ma.array(_read(f,os.path.join(inc,'homogenization',ho,field,la)))
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if la not in r[inc]['homogenization'][field].keys():
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container = np.empty((self.N_materialpoints,)+data.shape[1:],dtype=data.dtype)
fill_value = fill_float if data.dtype in np.sctypes['float'] else \
fill_int
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r[inc]['homogenization'][field][la] = \
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ma.array(container,fill_value=fill_value,mask=True)
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r[inc]['homogenization'][field][la][at_cell_ho[ho]] = data[in_data_ho[ho]]
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if strip: r = util.dict_strip(r)
if compress: r = util.dict_compress(r)
return r