1356 lines
53 KiB
Python
1356 lines
53 KiB
Python
import multiprocessing as mp
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import re
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import fnmatch
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import os
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import copy
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import datetime
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import xml.etree.ElementTree as ET
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import xml.dom.minidom
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from pathlib import Path
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from functools import partial
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from collections import defaultdict
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from collections.abc import Iterable
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import h5py
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import numpy as np
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import numpy.ma as ma
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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
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from . import tensor
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from . import util
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h5py3 = h5py.__version__[0] == '3'
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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()}
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dtype = np.dtype(dataset.dtype,metadata=metadata)
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return np.array(dataset,dtype=dtype)
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def _match(requested,existing):
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"""Find matches among two sets of labels."""
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def flatten_list(list_of_lists):
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return [e for e_ in list_of_lists for e in e_]
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if requested is True:
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requested = '*'
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elif requested is False or requested is None:
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requested = []
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requested_ = requested if hasattr(requested,'__iter__') and not isinstance(requested,str) else \
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[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),
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fill_value = fill_float if dataset.dtype in np.sctypes['float'] else fill_int,
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mask = True)
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class Result:
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"""
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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
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in material.yaml.
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"""
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def __init__(self,fname):
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"""
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New result view bound to a HDF5 file.
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Parameters
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----------
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fname : str or pathlib.Path
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Name of the DADF5 file to be opened.
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"""
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with h5py.File(fname,'r') as f:
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self.version_major = f.attrs['DADF5_version_major']
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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 \
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'cells' in f['geometry'].attrs.keys()
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if self.structured:
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try:
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self.cells = f['geometry'].attrs['cells']
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except KeyError:
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self.cells = f['geometry'].attrs['grid']
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self.size = f['geometry'].attrs['size']
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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]+')
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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['time/s' if self.version_minor < 12 else
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'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'])
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self.homogenizations = [m.decode() for m in np.unique(f[f'{grp}/homogenization']
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['Name' if self.version_minor < 12 else 'label'])]
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self.homogenizations = sorted(self.homogenizations,key=util.natural_sort)
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self.phases = [c.decode() for c in np.unique(f[f'{grp}/phase']
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['Name' if self.version_minor < 12 else 'label'])]
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self.phases = sorted(self.phases,key=util.natural_sort)
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self.fields = []
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for c in self.phases:
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self.fields += f['/'.join([self.increments[0],'phase',c])].keys()
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for m in self.homogenizations:
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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,
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'phases': self.phases,
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'homogenizations': self.homogenizations,
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'fields': self.fields,
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}
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self.fname = Path(fname).absolute()
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self._allow_modification = False
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def __copy__(self):
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"""Create deep copy."""
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return copy.deepcopy(self)
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copy = __copy__
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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 \
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self.view('increments',visible_increments[-1:]).list_data()
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in_between = '' if len(visible_increments) < 3 else \
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''.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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"""
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Manages the visibility of the groups.
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Parameters
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----------
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action : str
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Select from 'set', 'add', and 'del'.
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what : str
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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
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Name of datasets; supports '?' and '*' wildcards.
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True is equivalent to '*', False is equivalent to [].
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"""
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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 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]
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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
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f'{inc}{c}' for c in choice]
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elif what == 'times':
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what = 'increments'
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if choice == ['*']:
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choice = self.increments
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else:
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iterator = map(float,choice)
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choice = []
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for c in iterator:
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idx = np.searchsorted(self.times,c)
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if idx >= len(self.times): continue
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if np.isclose(c,self.times[idx]):
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choice.append(self.increments[idx])
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elif np.isclose(c,self.times[idx+1]):
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choice.append(self.increments[idx+1])
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valid = _match(choice,getattr(self,what))
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existing = set(self.visible[what])
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dup = self.copy()
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if action == 'set':
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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)
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dup.visible[what] = sorted(add, key=util.natural_sort)
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elif action == 'del':
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diff = existing.difference(valid)
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dup.visible[what] = sorted(diff, key=util.natural_sort)
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return dup
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def allow_modification(self):
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"""Allow to overwrite existing data."""
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print(util.warn('Warning: Modification of existing datasets allowed!'))
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dup = self.copy()
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dup._allow_modification = True
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return dup
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def disallow_modification(self):
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"""Disallow to overwrite existing data (default case)."""
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dup = self.copy()
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dup._allow_modification = False
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return dup
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def increments_in_range(self,start,end):
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"""
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Select all increments within a given range.
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Parameters
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----------
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start : int or str
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Start increment.
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end : int or str
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End increment.
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"""
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# compatibility hack
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ln = 3 if self.version_minor < 12 else 10
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selected = []
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for i,inc in enumerate([int(i[ln:]) for i in self.increments]):
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s,e = map(lambda x: int(x[ln:] if isinstance(x,str) and x.startswith('inc') else x), (start,end))
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if s <= inc <= e:
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selected.append(self.increments[i])
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return selected
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def times_in_range(self,start,end):
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"""
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Select all increments within a given time range.
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Parameters
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----------
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start : float
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Time of start increment.
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end : float
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Time of end increment.
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"""
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selected = []
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for i,time in enumerate(self.times):
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if start <= time <= end:
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selected.append(self.times[i])
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return selected
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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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----------
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what : {'increments', 'times', 'phases', 'homogenizations', 'fields'}
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Attribute to change.
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datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool
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Name of datasets; supports '?' and '*' wildcards.
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True is equivalent to '*', False is equivalent to [].
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"""
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return 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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----------
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what : {'increments', 'times', 'phases', 'homogenizations', 'fields'}
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Attribute to change.
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datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool
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Name of datasets; supports '?' and '*' wildcards.
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True is equivalent to '*', False is equivalent to [].
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"""
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return 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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----------
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what : {'increments', 'times', 'phases', 'homogenizations', 'fields'}
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Attribute to change.
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datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool
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Name of datasets; supports '?' and '*' wildcards.
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True is equivalent to '*', False is equivalent to [].
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"""
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return self._manage_view('del',what,datasets)
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def rename(self,name_old,name_new):
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"""
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Rename dataset.
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Parameters
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----------
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name_old : str
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Name of the dataset to be renamed.
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name_new : str
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New name of the dataset.
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"""
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if not self._allow_modification:
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raise PermissionError('Rename operation not permitted')
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with h5py.File(self.fname,'a') as f:
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for inc in self.visible['increments']:
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for ty in ['phase','homogenization']:
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for label in self.visible[ty+'s']:
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for field in self.visible['fields']:
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path_old = '/'.join([inc,ty,label,field,name_old])
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path_new = '/'.join([inc,ty,label,field,name_new])
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if path_old in f.keys():
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f[path_new] = f[path_old]
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f[path_new].attrs['renamed'] = f'original name: {name_old}' if h5py3 else \
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f'original name: {name_old}'.encode()
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del f[path_old]
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def list_data(self):
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"""Return information on all active datasets in the file."""
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# compatibility hack
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de = 'Description' if self.version_minor < 12 else 'description'
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un = 'Unit' if self.version_minor < 12 else 'unit'
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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'])
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for ty in ['phase','homogenization']:
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msg = ' '.join([msg,f'{ty}\n'])
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for label in self.visible[ty+'s']:
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msg = ' '.join([msg,f'{label}\n'])
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for field in self.visible['fields']:
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msg = ' '.join([msg,f'{field}\n'])
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for d in f['/'.join([inc,ty,label,field])].keys():
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dataset = f['/'.join([inc,ty,label,field,d])]
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unit = f' / {dataset.attrs[un]}' if h5py3 else \
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f' / {dataset.attrs[un].decode()}'
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description = dataset.attrs[de] if h5py3 else \
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dataset.attrs[de].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):
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globals()[func.__name__]=func
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print(f'Function {func.__name__} enabled in add_calculation.')
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|
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@property
|
||
def coordinates0_point(self):
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||
"""Return initial coordinates of the cell centers."""
|
||
if self.structured:
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return grid_filters.coordinates0_point(self.cells,self.size,self.origin).reshape(-1,3,order='F')
|
||
else:
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with h5py.File(self.fname,'r') as f:
|
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return f['geometry/x_c'][()]
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|
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@property
|
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def coordinates0_node(self):
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"""Return initial coordinates of the cell centers."""
|
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if self.structured:
|
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return grid_filters.coordinates0_node(self.cells,self.size,self.origin).reshape(-1,3,order='F')
|
||
else:
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with h5py.File(self.fname,'r') as f:
|
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return f['geometry/x_n'][()]
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||
|
||
@property
|
||
def geometry0(self):
|
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if self.structured:
|
||
return VTK.from_rectilinear_grid(self.cells,self.size,self.origin)
|
||
else:
|
||
with h5py.File(self.fname,'r') as f:
|
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return VTK.from_unstructured_grid(f['/geometry/x_n'][()],
|
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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())
|
||
|
||
|
||
@staticmethod
|
||
def _add_absolute(x):
|
||
return {
|
||
'data': np.abs(x['data']),
|
||
'label': f'|{x["label"]}|',
|
||
'meta': {
|
||
'unit': x['meta']['unit'],
|
||
'description': f"absolute value of {x['label']} ({x['meta']['description']})",
|
||
'creator': 'add_absolute'
|
||
}
|
||
}
|
||
def add_absolute(self,x):
|
||
"""
|
||
Add absolute value.
|
||
|
||
Parameters
|
||
----------
|
||
x : str
|
||
Label of scalar, vector, or tensor dataset to take absolute value of.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_absolute,{'x':x})
|
||
|
||
|
||
@staticmethod
|
||
def _add_calculation(**kwargs):
|
||
formula = kwargs['formula']
|
||
for d in re.findall(r'#(.*?)#',formula):
|
||
formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
|
||
|
||
return {
|
||
'data': eval(formula),
|
||
'label': kwargs['label'],
|
||
'meta': {
|
||
'unit': kwargs['unit'],
|
||
'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
|
||
'creator': 'add_calculation'
|
||
}
|
||
}
|
||
def add_calculation(self,label,formula,unit='n/a',description=None):
|
||
"""
|
||
Add result of a general formula.
|
||
|
||
Parameters
|
||
----------
|
||
label : str
|
||
Label of resulting dataset.
|
||
formula : str
|
||
Formula to calculate resulting dataset. Existing datasets are referenced by '#TheirLabel#'.
|
||
unit : str, optional
|
||
Physical unit of the result.
|
||
description : str, optional
|
||
Human-readable description of the result.
|
||
|
||
"""
|
||
dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
|
||
args = {'formula':formula,'label':label,'unit':unit,'description':description}
|
||
self._add_generic_pointwise(self._add_calculation,dataset_mapping,args)
|
||
|
||
|
||
@staticmethod
|
||
def _add_stress_Cauchy(P,F):
|
||
return {
|
||
'data': mechanics.stress_Cauchy(P['data'],F['data']),
|
||
'label': 'sigma',
|
||
'meta': {
|
||
'unit': P['meta']['unit'],
|
||
'description': "Cauchy stress calculated "
|
||
f"from {P['label']} ({P['meta']['description']})"
|
||
f" and {F['label']} ({F['meta']['description']})",
|
||
'creator': 'add_stress_Cauchy'
|
||
}
|
||
}
|
||
def add_stress_Cauchy(self,P='P',F='F'):
|
||
"""
|
||
Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
P : str, optional
|
||
Label of the dataset containing the first Piola-Kirchhoff stress. Defaults to 'P'.
|
||
F : str, optional
|
||
Label of the dataset containing the deformation gradient. Defaults to 'F'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_stress_Cauchy,{'P':P,'F':F})
|
||
|
||
|
||
@staticmethod
|
||
def _add_determinant(T):
|
||
return {
|
||
'data': np.linalg.det(T['data']),
|
||
'label': f"det({T['label']})",
|
||
'meta': {
|
||
'unit': T['meta']['unit'],
|
||
'description': f"determinant of tensor {T['label']} ({T['meta']['description']})",
|
||
'creator': 'add_determinant'
|
||
}
|
||
}
|
||
def add_determinant(self,T):
|
||
"""
|
||
Add the determinant of a tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T : str
|
||
Label of tensor dataset.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_determinant,{'T':T})
|
||
|
||
|
||
@staticmethod
|
||
def _add_deviator(T):
|
||
return {
|
||
'data': tensor.deviatoric(T['data']),
|
||
'label': f"s_{T['label']}",
|
||
'meta': {
|
||
'unit': T['meta']['unit'],
|
||
'description': f"deviator of tensor {T['label']} ({T['meta']['description']})",
|
||
'creator': 'add_deviator'
|
||
}
|
||
}
|
||
def add_deviator(self,T):
|
||
"""
|
||
Add the deviatoric part of a tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T : str
|
||
Label of tensor dataset.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_deviator,{'T':T})
|
||
|
||
|
||
@staticmethod
|
||
def _add_eigenvalue(T_sym,eigenvalue):
|
||
if eigenvalue == 'max':
|
||
label,p = 'maximum',2
|
||
elif eigenvalue == 'mid':
|
||
label,p = 'intermediate',1
|
||
elif eigenvalue == 'min':
|
||
label,p = 'minimum',0
|
||
|
||
return {
|
||
'data': tensor.eigenvalues(T_sym['data'])[:,p],
|
||
'label': f"lambda_{eigenvalue}({T_sym['label']})",
|
||
'meta' : {
|
||
'unit': T_sym['meta']['unit'],
|
||
'description': f"{label} eigenvalue of {T_sym['label']} ({T_sym['meta']['description']})",
|
||
'creator': 'add_eigenvalue'
|
||
}
|
||
}
|
||
def add_eigenvalue(self,T_sym,eigenvalue='max'):
|
||
"""
|
||
Add eigenvalues of symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Label of symmetric tensor dataset.
|
||
eigenvalue : str, optional
|
||
Eigenvalue. Select from 'max', 'mid', 'min'. Defaults to 'max'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
||
|
||
|
||
@staticmethod
|
||
def _add_eigenvector(T_sym,eigenvalue):
|
||
if eigenvalue == 'max':
|
||
label,p = 'maximum',2
|
||
elif eigenvalue == 'mid':
|
||
label,p = 'intermediate',1
|
||
elif eigenvalue == 'min':
|
||
label,p = 'minimum',0
|
||
return {
|
||
'data': tensor.eigenvectors(T_sym['data'])[:,p],
|
||
'label': f"v_{eigenvalue}({T_sym['label']})",
|
||
'meta' : {
|
||
'unit': '1',
|
||
'description': f"eigenvector corresponding to {label} eigenvalue"
|
||
f" of {T_sym['label']} ({T_sym['meta']['description']})",
|
||
'creator': 'add_eigenvector'
|
||
}
|
||
}
|
||
def add_eigenvector(self,T_sym,eigenvalue='max'):
|
||
"""
|
||
Add eigenvector of symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Label of symmetric tensor dataset.
|
||
eigenvalue : str, optional
|
||
Eigenvalue to which the eigenvector corresponds.
|
||
Select from 'max', 'mid', 'min'. Defaults to 'max'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
||
|
||
|
||
@staticmethod
|
||
def _add_IPF_color(l,q):
|
||
m = util.scale_to_coprime(np.array(l))
|
||
try:
|
||
lattice = {'fcc':'cF','bcc':'cI','hex':'hP'}[q['meta']['lattice']]
|
||
except KeyError:
|
||
lattice = q['meta']['lattice']
|
||
try:
|
||
o = Orientation(rotation = (rfn.structured_to_unstructured(q['data'])),lattice=lattice)
|
||
except ValueError:
|
||
o = Orientation(rotation = q['data'],lattice=lattice)
|
||
|
||
return {
|
||
'data': np.uint8(o.IPF_color(l)*255),
|
||
'label': 'IPFcolor_({} {} {})'.format(*m),
|
||
'meta' : {
|
||
'unit': '8-bit RGB',
|
||
'lattice': q['meta']['lattice'],
|
||
'description': 'Inverse Pole Figure (IPF) colors along sample direction ({} {} {})'.format(*m),
|
||
'creator': 'add_IPF_color'
|
||
}
|
||
}
|
||
def add_IPF_color(self,l,q='O'):
|
||
"""
|
||
Add RGB color tuple of inverse pole figure (IPF) color.
|
||
|
||
Parameters
|
||
----------
|
||
l : numpy.array of shape (3)
|
||
Lab frame direction for inverse pole figure.
|
||
q : str
|
||
Label of the dataset containing the crystallographic orientation as quaternions.
|
||
Defaults to 'O'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_IPF_color,{'q':q},{'l':l})
|
||
|
||
|
||
@staticmethod
|
||
def _add_maximum_shear(T_sym):
|
||
return {
|
||
'data': mechanics.maximum_shear(T_sym['data']),
|
||
'label': f"max_shear({T_sym['label']})",
|
||
'meta': {
|
||
'unit': T_sym['meta']['unit'],
|
||
'description': f"maximum shear component of {T_sym['label']} ({T_sym['meta']['description']})",
|
||
'creator': 'add_maximum_shear'
|
||
}
|
||
}
|
||
def add_maximum_shear(self,T_sym):
|
||
"""
|
||
Add maximum shear components of symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Label of symmetric tensor dataset.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym})
|
||
|
||
|
||
@staticmethod
|
||
def _add_equivalent_Mises(T_sym,kind):
|
||
k = kind
|
||
if k is None:
|
||
if T_sym['meta']['unit'] == '1':
|
||
k = 'strain'
|
||
elif T_sym['meta']['unit'] == 'Pa':
|
||
k = 'stress'
|
||
if k not in ['stress', 'strain']:
|
||
raise ValueError(f'invalid von Mises kind {kind}')
|
||
|
||
return {
|
||
'data': (mechanics.equivalent_strain_Mises if k=='strain' else \
|
||
mechanics.equivalent_stress_Mises)(T_sym['data']),
|
||
'label': f"{T_sym['label']}_vM",
|
||
'meta': {
|
||
'unit': T_sym['meta']['unit'],
|
||
'description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['description']})",
|
||
'creator': 'add_Mises'
|
||
}
|
||
}
|
||
def add_equivalent_Mises(self,T_sym,kind=None):
|
||
"""
|
||
Add the equivalent Mises stress or strain of a symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Label of symmetric tensorial stress or strain dataset.
|
||
kind : {'stress', 'strain', None}, optional
|
||
Kind of the von Mises equivalent. Defaults to None, in which case
|
||
it is selected based on the unit of the dataset ('1' -> strain, 'Pa' -> stress).
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
|
||
|
||
|
||
@staticmethod
|
||
def _add_norm(x,ord):
|
||
o = ord
|
||
if len(x['data'].shape) == 2:
|
||
axis = 1
|
||
t = 'vector'
|
||
if o is None: o = 2
|
||
elif len(x['data'].shape) == 3:
|
||
axis = (1,2)
|
||
t = 'tensor'
|
||
if o is None: o = 'fro'
|
||
else:
|
||
raise ValueError
|
||
|
||
return {
|
||
'data': np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True),
|
||
'label': f"|{x['label']}|_{o}",
|
||
'meta': {
|
||
'unit': x['meta']['unit'],
|
||
'description': f"{o}-norm of {t} {x['label']} ({x['meta']['description']})",
|
||
'creator': 'add_norm'
|
||
}
|
||
}
|
||
def add_norm(self,x,ord=None):
|
||
"""
|
||
Add the norm of vector or tensor.
|
||
|
||
Parameters
|
||
----------
|
||
x : str
|
||
Label of vector or tensor dataset.
|
||
ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
|
||
Order of the norm. inf means NumPy’s inf object. For details refer to numpy.linalg.norm.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_norm,{'x':x},{'ord':ord})
|
||
|
||
|
||
@staticmethod
|
||
def _add_stress_second_Piola_Kirchhoff(P,F):
|
||
return {
|
||
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
|
||
'label': 'S',
|
||
'meta': {
|
||
'unit': P['meta']['unit'],
|
||
'description': "second Piola-Kirchhoff stress calculated "
|
||
f"from {P['label']} ({P['meta']['description']})"
|
||
f" and {F['label']} ({F['meta']['description']})",
|
||
'creator': 'add_stress_second_Piola_Kirchhoff'
|
||
}
|
||
}
|
||
def add_stress_second_Piola_Kirchhoff(self,P='P',F='F'):
|
||
"""
|
||
Add second Piola-Kirchhoff stress calculated from first Piola-Kirchhoff stress and deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
P : str, optional
|
||
Label of first Piola-Kirchhoff stress dataset. Defaults to 'P'.
|
||
F : str, optional
|
||
Label of deformation gradient dataset. Defaults to 'F'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_stress_second_Piola_Kirchhoff,{'P':P,'F':F})
|
||
|
||
|
||
# The add_pole functionality needs discussion.
|
||
# The new Crystal object can perform such a calculation but the outcome depends on the lattice parameters
|
||
# as well as on whether a direction or plane is concerned (see the DAMASK_examples/pole_figure notebook).
|
||
# Below code appears to be too simplistic.
|
||
|
||
# @staticmethod
|
||
# def _add_pole(q,p,polar):
|
||
# pole = np.array(p)
|
||
# unit_pole = pole/np.linalg.norm(pole)
|
||
# m = util.scale_to_coprime(pole)
|
||
# rot = Rotation(q['data'].view(np.double).reshape(-1,4))
|
||
#
|
||
# rotatedPole = rot @ np.broadcast_to(unit_pole,rot.shape+(3,)) # rotate pole according to crystal orientation
|
||
# xy = rotatedPole[:,0:2]/(1.+abs(unit_pole[2])) # stereographic projection
|
||
# coords = xy if not polar else \
|
||
# np.block([np.sqrt(xy[:,0:1]*xy[:,0:1]+xy[:,1:2]*xy[:,1:2]),np.arctan2(xy[:,1:2],xy[:,0:1])])
|
||
# return {
|
||
# 'data': coords,
|
||
# 'label': 'p^{}_[{} {} {})'.format(u'rφ' if polar else 'xy',*m),
|
||
# 'meta' : {
|
||
# 'unit': '1',
|
||
# 'description': '{} coordinates of stereographic projection of pole (direction/plane) in crystal frame'\
|
||
# .format('Polar' if polar else 'Cartesian'),
|
||
# 'creator': 'add_pole'
|
||
# }
|
||
# }
|
||
# def add_pole(self,q,p,polar=False):
|
||
# """
|
||
# Add coordinates of stereographic projection of given pole in crystal frame.
|
||
#
|
||
# Parameters
|
||
# ----------
|
||
# q : str
|
||
# Label of the dataset containing the crystallographic orientation as quaternions.
|
||
# p : numpy.array of shape (3)
|
||
# Crystallographic direction or plane.
|
||
# polar : bool, optional
|
||
# Give pole in polar coordinates. Defaults to False.
|
||
#
|
||
# """
|
||
# self._add_generic_pointwise(self._add_pole,{'q':q},{'p':p,'polar':polar})
|
||
|
||
|
||
@staticmethod
|
||
def _add_rotation(F):
|
||
return {
|
||
'data': mechanics.rotation(F['data']).as_matrix(),
|
||
'label': f"R({F['label']})",
|
||
'meta': {
|
||
'unit': F['meta']['unit'],
|
||
'description': f"rotational part of {F['label']} ({F['meta']['description']})",
|
||
'creator': 'add_rotation'
|
||
}
|
||
}
|
||
def add_rotation(self,F):
|
||
"""
|
||
Add rotational part of a deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
F : str, optional
|
||
Label of deformation gradient dataset.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_rotation,{'F':F})
|
||
|
||
|
||
@staticmethod
|
||
def _add_spherical(T):
|
||
return {
|
||
'data': tensor.spherical(T['data'],False),
|
||
'label': f"p_{T['label']}",
|
||
'meta': {
|
||
'unit': T['meta']['unit'],
|
||
'description': f"spherical component of tensor {T['label']} ({T['meta']['description']})",
|
||
'creator': 'add_spherical'
|
||
}
|
||
}
|
||
def add_spherical(self,T):
|
||
"""
|
||
Add the spherical (hydrostatic) part of a tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T : str
|
||
Label of tensor dataset.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_spherical,{'T':T})
|
||
|
||
|
||
@staticmethod
|
||
def _add_strain(F,t,m):
|
||
return {
|
||
'data': mechanics.strain(F['data'],t,m),
|
||
'label': f"epsilon_{t}^{m}({F['label']})",
|
||
'meta': {
|
||
'unit': F['meta']['unit'],
|
||
'description': f"strain tensor of {F['label']} ({F['meta']['description']})",
|
||
'creator': 'add_strain'
|
||
}
|
||
}
|
||
def add_strain(self,F='F',t='V',m=0.0):
|
||
"""
|
||
Add strain tensor of a deformation gradient.
|
||
|
||
For details, see damask.mechanics.strain.
|
||
|
||
Parameters
|
||
----------
|
||
F : str, optional
|
||
Label of deformation gradient dataset. Defaults to 'F'.
|
||
t : {'V', 'U'}, optional
|
||
Type of the polar decomposition, 'V' for left stretch tensor and 'U' for right stretch tensor.
|
||
Defaults to 'V'.
|
||
m : float, optional
|
||
Order of the strain calculation. Defaults to 0.0.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_strain,{'F':F},{'t':t,'m':m})
|
||
|
||
|
||
@staticmethod
|
||
def _add_stretch_tensor(F,t):
|
||
return {
|
||
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
|
||
'label': f"{t}({F['label']})",
|
||
'meta': {
|
||
'unit': F['meta']['unit'],
|
||
'description': '{} stretch tensor of {} ({})'.format('left' if t.upper() == 'V' else 'right',
|
||
F['label'],F['meta']['description']),
|
||
'creator': 'add_stretch_tensor'
|
||
}
|
||
}
|
||
def add_stretch_tensor(self,F='F',t='V'):
|
||
"""
|
||
Add stretch tensor of a deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
F : str, optional
|
||
Label of deformation gradient dataset. Defaults to 'F'.
|
||
t : {'V', 'U'}, optional
|
||
Type of the polar decomposition, 'V' for left stretch tensor and 'U' for right stretch tensor.
|
||
Defaults to 'V'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
|
||
|
||
|
||
def _job(self,group,func,datasets,args,lock):
|
||
"""Execute job for _add_generic_pointwise."""
|
||
try:
|
||
datasets_in = {}
|
||
lock.acquire()
|
||
with h5py.File(self.fname,'r') as f:
|
||
for arg,label in datasets.items():
|
||
loc = f[group+'/'+label]
|
||
datasets_in[arg]={'data' :loc[()],
|
||
'label':label,
|
||
'meta': {k:(v if h5py3 else v.decode()) for k,v in loc.attrs.items()}}
|
||
lock.release()
|
||
r = func(**datasets_in,**args)
|
||
return [group,r]
|
||
except Exception as err:
|
||
print(f'Error during calculation: {err}.')
|
||
return None
|
||
|
||
|
||
def _add_generic_pointwise(self,func,datasets,args={}):
|
||
"""
|
||
General function to add pointwise data.
|
||
|
||
Parameters
|
||
----------
|
||
func : function
|
||
Callback function that calculates a new dataset from one or
|
||
more datasets per HDF5 group.
|
||
datasets : dictionary
|
||
Details of the datasets to be used:
|
||
{arg (name to which the data is passed in func): label (in HDF5 file)}.
|
||
args : dictionary, optional
|
||
Arguments parsed to func.
|
||
|
||
"""
|
||
chunk_size = 1024**2//8
|
||
pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',1)))
|
||
lock = mp.Manager().Lock()
|
||
|
||
groups = []
|
||
with h5py.File(self.fname,'r') as f:
|
||
for inc in self.visible['increments']:
|
||
for ty in ['phase','homogenization']:
|
||
for label in self.visible[ty+'s']:
|
||
for field in self.visible['fields']:
|
||
group = '/'.join([inc,ty,label,field])
|
||
if set(datasets.values()).issubset(f[group].keys()): groups.append(group)
|
||
|
||
if len(groups) == 0:
|
||
print('No matching dataset found, no data was added.')
|
||
return
|
||
|
||
default_arg = partial(self._job,func=func,datasets=datasets,args=args,lock=lock)
|
||
|
||
for result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):
|
||
if not result:
|
||
continue
|
||
lock.acquire()
|
||
with h5py.File(self.fname, 'a') as f:
|
||
try:
|
||
if self._allow_modification and result[0]+'/'+result[1]['label'] in f:
|
||
dataset = f[result[0]+'/'+result[1]['label']]
|
||
dataset[...] = result[1]['data']
|
||
dataset.attrs['overwritten'] = 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'])
|
||
|
||
now = datetime.datetime.now().astimezone()
|
||
dataset.attrs['created'] = now.strftime('%Y-%m-%d %H:%M:%S%z') if h5py3 else \
|
||
now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
|
||
|
||
for l,v in result[1]['meta'].items():
|
||
dataset.attrs[l.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:
|
||
print(f'Could not add dataset: {err}.')
|
||
lock.release()
|
||
|
||
pool.close()
|
||
pool.join()
|
||
|
||
|
||
def save_XDMF(self,output='*'):
|
||
"""
|
||
Write XDMF file to directly visualize data in DADF5 file.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str
|
||
Labels of the datasets to read.
|
||
Defaults to '*', in which case all datasets are considered.
|
||
|
||
"""
|
||
u = 'Unit' if self.version_minor < 12 else 'unit' # compatibility hack
|
||
if self.N_constituents != 1 or len(self.phases) != 1 or not self.structured:
|
||
raise TypeError('XDMF output requires structured grid with single phase and single constituent.')
|
||
|
||
|
||
attribute_type_map = defaultdict(lambda:'Matrix', ( ((),'Scalar'), ((3,),'Vector'), ((3,3),'Tensor')) )
|
||
|
||
def number_type_map(dtype):
|
||
if dtype in np.sctypes['int']: return 'Int'
|
||
if dtype in np.sctypes['uint']: return 'UInt'
|
||
if dtype in np.sctypes['float']: return 'Float'
|
||
|
||
|
||
xdmf = ET.Element('Xdmf')
|
||
xdmf.attrib={'Version': '2.0',
|
||
'xmlns:xi': 'http://www.w3.org/2001/XInclude'}
|
||
|
||
domain = ET.SubElement(xdmf, 'Domain')
|
||
|
||
collection = ET.SubElement(domain, 'Grid')
|
||
collection.attrib={'GridType': 'Collection',
|
||
'CollectionType': 'Temporal'}
|
||
|
||
time = ET.SubElement(collection, 'Time')
|
||
time.attrib={'TimeType': 'List'}
|
||
|
||
time_data = ET.SubElement(time, 'DataItem')
|
||
times = [self.times[self.increments.index(i)] for i in self.visible['increments']]
|
||
time_data.attrib={'Format': 'XML',
|
||
'NumberType': 'Float',
|
||
'Dimensions': f'{len(times)}'}
|
||
time_data.text = ' '.join(map(str,times))
|
||
|
||
attributes = []
|
||
data_items = []
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
for inc in self.visible['increments']:
|
||
|
||
grid = ET.SubElement(collection,'Grid')
|
||
grid.attrib = {'GridType': 'Uniform',
|
||
'Name': inc}
|
||
|
||
topology = ET.SubElement(grid, 'Topology')
|
||
topology.attrib = {'TopologyType': '3DCoRectMesh',
|
||
'Dimensions': '{} {} {}'.format(*(self.cells+1))}
|
||
|
||
geometry = ET.SubElement(grid, 'Geometry')
|
||
geometry.attrib = {'GeometryType':'Origin_DxDyDz'}
|
||
|
||
origin = ET.SubElement(geometry, 'DataItem')
|
||
origin.attrib = {'Format': 'XML',
|
||
'NumberType': 'Float',
|
||
'Dimensions': '3'}
|
||
origin.text = "{} {} {}".format(*self.origin)
|
||
|
||
delta = ET.SubElement(geometry, 'DataItem')
|
||
delta.attrib = {'Format': 'XML',
|
||
'NumberType': 'Float',
|
||
'Dimensions': '3'}
|
||
delta.text="{} {} {}".format(*(self.size/self.cells))
|
||
|
||
attributes.append(ET.SubElement(grid, 'Attribute'))
|
||
attributes[-1].attrib = {'Name': 'u / m',
|
||
'Center': 'Node',
|
||
'AttributeType': 'Vector'}
|
||
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
|
||
data_items[-1].attrib = {'Format': 'HDF',
|
||
'Precision': '8',
|
||
'Dimensions': '{} {} {} 3'.format(*(self.cells+1))}
|
||
data_items[-1].text = f'{os.path.split(self.fname)[1]}:/{inc}/geometry/u_n'
|
||
|
||
for ty in ['phase','homogenization']:
|
||
for label in self.visible[ty+'s']:
|
||
for field in self.visible['fields']:
|
||
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
|
||
name = '/'.join([inc,ty,label,field,out])
|
||
shape = f[name].shape[1:]
|
||
dtype = f[name].dtype
|
||
|
||
unit = f[name].attrs[u] if h5py3 else f[name].attrs[u].decode()
|
||
|
||
attributes.append(ET.SubElement(grid, 'Attribute'))
|
||
attributes[-1].attrib = {'Name': name.split('/',2)[2]+f' / {unit}',
|
||
'Center': 'Cell',
|
||
'AttributeType': attribute_type_map[shape]}
|
||
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
|
||
data_items[-1].attrib = {'Format': 'HDF',
|
||
'NumberType': number_type_map(dtype),
|
||
'Precision': f'{dtype.itemsize}',
|
||
'Dimensions': '{} {} {} {}'.format(*self.cells,1 if shape == () else
|
||
np.prod(shape))}
|
||
data_items[-1].text = f'{os.path.split(self.fname)[1]}:{name}'
|
||
|
||
with open(self.fname.with_suffix('.xdmf').name,'w',newline='\n') as f:
|
||
f.write(xml.dom.minidom.parseString(ET.tostring(xdmf).decode()).toprettyxml())
|
||
|
||
|
||
def _mappings(self):
|
||
grp = 'mapping' if self.version_minor < 12 else 'cell_to' # compatibility hack
|
||
name = 'Name' if self.version_minor < 12 else 'label' # compatibility hack
|
||
member = 'member' if self.version_minor < 12 else 'entry' # compatibility hack
|
||
|
||
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(f['/'.join([grp,'phase'])][:,c][name] == label.encode())[0] \
|
||
for label in self.visible['phases']})
|
||
in_data_ph.append({label: f['/'.join([grp,'phase'])][member][at_cell_ph[c][label]][:,c] \
|
||
for label in self.visible['phases']})
|
||
|
||
at_cell_ho = {label: np.where(f['/'.join([grp,'homogenization'])][:][name] == label.encode())[0] \
|
||
for label in self.visible['homogenizations']}
|
||
in_data_ho = {label: f['/'.join([grp,'homogenization'])][member][at_cell_ho[label]] \
|
||
for label in self.visible['homogenizations']}
|
||
|
||
return at_cell_ph,in_data_ph,at_cell_ho,in_data_ho
|
||
|
||
|
||
def save_VTK(self,output='*',mode='cell',constituents=None,fill_float=np.nan,fill_int=0,parallel=True):
|
||
"""
|
||
Export to VTK cell/point data.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str, optional
|
||
Labels of the datasets to place.
|
||
Defaults to '*', in which case all datasets are exported.
|
||
mode : {'cell', 'point'}
|
||
Export in cell format or point format.
|
||
Defaults to 'cell'.
|
||
constituents : (list of) int, optional
|
||
Constituents to consider.
|
||
Defaults to None, in which case all constituents are considered.
|
||
fill_float : float
|
||
Fill value for non-existent entries of floating point type.
|
||
Defaults to NaN.
|
||
fill_int : int
|
||
Fill value for non-existent entries of integer type.
|
||
Defaults to 0.
|
||
parallel : bool
|
||
Write out VTK files in parallel in a separate background process.
|
||
Defaults to True.
|
||
|
||
"""
|
||
if mode.lower()=='cell':
|
||
v = self.geometry0
|
||
elif mode.lower()=='point':
|
||
v = VTK.from_poly_data(self.coordinates0_point)
|
||
|
||
ln = 3 if self.version_minor < 12 else 10 # compatibility hack
|
||
N_digits = int(np.floor(np.log10(max(1,int(self.increments[-1][ln:])))))+1
|
||
|
||
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_]
|
||
|
||
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
|
||
for inc in util.show_progress(self.visible['increments']):
|
||
|
||
u = _read(f['/'.join([inc,'geometry','u_n' if mode.lower() == 'cell' else 'u_p'])])
|
||
v.add(u,'u')
|
||
|
||
for ty in ['phase','homogenization']:
|
||
for field in self.visible['fields']:
|
||
outs = {}
|
||
for label in self.visible[ty+'s']:
|
||
if field not in f['/'.join([inc,ty,label])].keys(): continue
|
||
|
||
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
|
||
data = ma.array(_read(f['/'.join([inc,ty,label,field,out])]))
|
||
|
||
if ty == 'phase':
|
||
if out+suffixes[0] not in outs.keys():
|
||
for c,suffix in zip(constituents_,suffixes):
|
||
outs[out+suffix] = \
|
||
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
|
||
|
||
for c,suffix in zip(constituents_,suffixes):
|
||
outs[out+suffix][at_cell_ph[c][label]] = data[in_data_ph[c][label]]
|
||
|
||
if ty == 'homogenization':
|
||
if out not in outs.keys():
|
||
outs[out] = _empty_like(data,self.N_materialpoints,fill_float,fill_int)
|
||
|
||
outs[out][at_cell_ho[label]] = data[in_data_ho[label]]
|
||
|
||
for label,dataset in outs.items():
|
||
v.add(dataset,' / '.join(['/'.join([ty,field,label]),dataset.dtype.metadata['unit']]))
|
||
|
||
v.save(f'{self.fname.stem}_inc{inc[ln:].zfill(N_digits)}',parallel=parallel)
|
||
|
||
|
||
def get(self,output='*',flatten=True,prune=True):
|
||
"""
|
||
Collect data per phase/homogenization reflecting the group/folder structure in the DADF5 file.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str
|
||
Labels of the datasets to read.
|
||
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.
|
||
|
||
Returns
|
||
-------
|
||
data : dict of numpy.ndarray
|
||
Datasets structured by phase/homogenization and according to selected view.
|
||
|
||
"""
|
||
r = {}
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
for inc in util.show_progress(self.visible['increments']):
|
||
r[inc] = {'phase':{},'homogenization':{},'geometry':{}}
|
||
|
||
for out in _match(output,f['/'.join([inc,'geometry'])].keys()):
|
||
r[inc]['geometry'][out] = _read(f['/'.join([inc,'geometry',out])])
|
||
|
||
for ty in ['phase','homogenization']:
|
||
for label in self.visible[ty+'s']:
|
||
r[inc][ty][label] = {}
|
||
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
|
||
r[inc][ty][label][field] = {}
|
||
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
|
||
r[inc][ty][label][field][out] = _read(f['/'.join([inc,ty,label,field,out])])
|
||
|
||
if prune: r = util.dict_prune(r)
|
||
if flatten: r = util.dict_flatten(r)
|
||
|
||
return r
|
||
|
||
|
||
def place(self,output='*',flatten=True,prune=True,constituents=None,fill_float=np.nan,fill_int=0):
|
||
"""
|
||
Merge data into spatial order that is compatible with the damask.VTK geometry representation.
|
||
|
||
The returned data structure reflects the group/folder structure
|
||
in the DADF5 file.
|
||
|
||
Multi-phase data is fused into a single output.
|
||
`place` is equivalent to `read` if only one phase/homogenization
|
||
and one constituent is present.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str, optional
|
||
Labels of the datasets to place.
|
||
Defaults to '*', in which case all datasets are placed.
|
||
flatten : bool
|
||
Remove singular levels of the folder hierarchy.
|
||
This might be beneficial in case of single increment or field.
|
||
Defaults to True.
|
||
prune : bool
|
||
Remove branches with no data. Defaults to True.
|
||
constituents : (list of) int, optional
|
||
Constituents to consider.
|
||
Defaults to 'None', in which case all constituents are considered.
|
||
fill_float : float
|
||
Fill value for non-existent entries of floating point type.
|
||
Defaults to NaN.
|
||
fill_int : int
|
||
Fill value for non-existent entries of integer type.
|
||
Defaults to 0.
|
||
|
||
Returns
|
||
-------
|
||
data : dict of numpy.ma.MaskedArray
|
||
Datasets structured by spatial position and according to selected view.
|
||
|
||
"""
|
||
r = {}
|
||
|
||
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_]
|
||
|
||
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
|
||
for inc in util.show_progress(self.visible['increments']):
|
||
r[inc] = {'phase':{},'homogenization':{},'geometry':{}}
|
||
|
||
for out in _match(output,f['/'.join([inc,'geometry'])].keys()):
|
||
r[inc]['geometry'][out] = ma.array(_read(f['/'.join([inc,'geometry',out])]),fill_value = fill_float)
|
||
|
||
for ty in ['phase','homogenization']:
|
||
for label in self.visible[ty+'s']:
|
||
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
|
||
if field not in r[inc][ty].keys():
|
||
r[inc][ty][field] = {}
|
||
|
||
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
|
||
data = ma.array(_read(f['/'.join([inc,ty,label,field,out])]))
|
||
|
||
if ty == 'phase':
|
||
if out+suffixes[0] not in r[inc][ty][field].keys():
|
||
for c,suffix in zip(constituents_,suffixes):
|
||
r[inc][ty][field][out+suffix] = \
|
||
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
|
||
|
||
for c,suffix in zip(constituents_,suffixes):
|
||
r[inc][ty][field][out+suffix][at_cell_ph[c][label]] = data[in_data_ph[c][label]]
|
||
|
||
if ty == 'homogenization':
|
||
if out not in r[inc][ty][field].keys():
|
||
r[inc][ty][field][out] = \
|
||
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
|
||
|
||
r[inc][ty][field][out][at_cell_ho[label]] = data[in_data_ho[label]]
|
||
|
||
if prune: r = util.dict_prune(r)
|
||
if flatten: r = util.dict_flatten(r)
|
||
|
||
return r
|