1263 lines
49 KiB
Python
1263 lines
49 KiB
Python
import multiprocessing
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import re
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import inspect
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import glob
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import os
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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 functools import partial
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import h5py
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import numpy as np
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from . import VTK
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from . import Table
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from . import Rotation
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from . import Orientation
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from . import Environment
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from . import grid_filters
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from . import mechanics
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from . import util
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from . import version
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class Result:
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"""
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Read and write to DADF5 files.
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DADF5 (DAMASK HDF5) files contain DAMASK results.
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"""
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def __init__(self,fname):
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"""
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Open an existing DADF5 file.
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Parameters
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----------
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fname : str
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name of the DADF5 file to be openend.
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"""
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with h5py.File(fname,'r') as f:
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try:
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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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except KeyError:
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self.version_major = f.attrs['DADF5-major']
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self.version_minor = f.attrs['DADF5-minor']
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if self.version_major != 0 or not 2 <= self.version_minor <= 6:
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raise TypeError('Unsupported DADF5 version {}.{} '.format(self.version_major,
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self.version_minor))
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self.structured = 'grid' in f['geometry'].attrs.keys()
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if self.structured:
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self.grid = f['geometry'].attrs['grid']
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self.size = f['geometry'].attrs['size']
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self.origin = f['geometry'].attrs['origin'] if self.version_major == 0 and self.version_minor >= 5 else \
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np.zeros(3)
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r=re.compile('inc[0-9]+')
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increments_unsorted = {int(i[3:]):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]
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self.Nmaterialpoints, self.Nconstituents = np.shape(f['mapping/cellResults/constituent'])
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self.materialpoints = [m.decode() for m in np.unique(f['mapping/cellResults/materialpoint']['Name'])]
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self.constituents = [c.decode() for c in np.unique(f['mapping/cellResults/constituent'] ['Name'])]
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# faster, but does not work with (deprecated) DADF5_postResults
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#self.materialpoints = [m for m in f['inc0/materialpoint']]
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#self.constituents = [c for c in f['inc0/constituent']]
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self.con_physics = []
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for c in self.constituents:
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self.con_physics += f['/'.join([self.increments[0],'constituent',c])].keys()
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self.con_physics = list(set(self.con_physics)) # make unique
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self.mat_physics = []
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for m in self.materialpoints:
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self.mat_physics += f['/'.join([self.increments[0],'materialpoint',m])].keys()
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self.mat_physics = list(set(self.mat_physics)) # make unique
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self.selection = {'increments': self.increments,
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'constituents': self.constituents,'materialpoints': self.materialpoints,
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'con_physics': self.con_physics, 'mat_physics': self.mat_physics
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}
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self.fname = os.path.abspath(fname)
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self._allow_overwrite = False
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def __repr__(self):
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"""Show selected data."""
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all_selected_increments = self.selection['increments']
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self.pick('increments',all_selected_increments[0:1])
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first = self.list_data()
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self.pick('increments',all_selected_increments[-1:])
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last = '' if len(all_selected_increments) < 2 else self.list_data()
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self.pick('increments',all_selected_increments)
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in_between = '' if len(all_selected_increments) < 3 else \
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''.join(['\n{}\n ...\n'.format(inc) for inc in all_selected_increments[1:-2]])
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return util.srepr(first + in_between + last)
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def _manage_selection(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.selection)
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datasets : list of str or bool
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name of datasets as list, 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:
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datasets = []
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choice = datasets if hasattr(datasets,'__iter__') and not isinstance(datasets,str) else \
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[datasets]
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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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'inc{}'.format(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 = [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.selection[what])
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if action == 'set':
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self.selection[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=lambda x: int("".join([i for i in x if i.isdigit()])))
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self.selection[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=lambda x: int("".join([i for i in x if i.isdigit()])))
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self.selection[what] = diff_sorted
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def enable_overwrite(self):
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print(util.bcolors().WARNING,util.bcolors().BOLD,
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'Warning: Enabled overwrite of existing datasets!',
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util.bcolors().ENDC)
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self._allow_overwrite = True
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def disable_overwrite(self):
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self._allow_overwrite = False
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def incs_in_range(self,start,end):
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selected = []
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for i,inc in enumerate([int(i[3:]) for i in self.increments]):
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s,e = map(lambda x: int(x[3:] 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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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 iterate(self,what):
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"""
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Iterate over selection items by setting each one selected.
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Parameters
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----------
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what : str
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attribute to change (must be from self.selection)
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"""
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datasets = self.selection[what]
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last_selection = datasets.copy()
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for dataset in datasets:
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if last_selection != self.selection[what]:
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self._manage_selection('set',what,datasets)
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raise Exception
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self._manage_selection('set',what,dataset)
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last_selection = self.selection[what]
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yield dataset
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self._manage_selection('set',what,datasets)
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def pick(self,what,datasets):
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"""
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Set selection.
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Parameters
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----------
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what : str
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attribute to change (must be from self.selection)
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datasets : list of str or bool
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name of datasets as list, supports ? and * wildcards.
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True is equivalent to [*], False is equivalent to []
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"""
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self._manage_selection('set',what,datasets)
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def pick_more(self,what,datasets):
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"""
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Add to selection.
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Parameters
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----------
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what : str
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attribute to change (must be from self.selection)
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datasets : list of str or bool
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name of datasets as list, supports ? and * wildcards.
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True is equivalent to [*], False is equivalent to []
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"""
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self._manage_selection('add',what,datasets)
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def pick_less(self,what,datasets):
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"""
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Delete from selection.
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Parameters
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----------
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what : str
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attribute to change (must be from self.selection)
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datasets : list of str or bool
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name of datasets as list, supports ? and * wildcards.
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True is equivalent to [*], False is equivalent to []
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"""
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self._manage_selection('del',what,datasets)
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# def datamerger(regular expression to filter groups into one copy)
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def place(self,datasets,component=0,tagged=False,split=True):
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"""
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||
Distribute datasets onto geometry and return Table or (split) dictionary of Tables.
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Must not mix nodal end cell data.
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Only data within
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- inc?????/constituent/*_*/*
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- inc?????/materialpoint/*_*/*
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- inc?????/geometry/*
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||
are considered.
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||
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Parameters
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----------
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datasets : iterable or str
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component : int
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homogenization component to consider for constituent data
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||
tagged : bool
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||
tag Table.column name with '#component'
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defaults to False
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split : bool
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split Table by increment and return dictionary of Tables
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defaults to True
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"""
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sets = datasets if hasattr(datasets,'__iter__') and not isinstance(datasets,str) \
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else [datasets]
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tag = f'#{component}' if tagged else ''
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tbl = {} if split else None
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inGeom = {}
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inData = {}
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with h5py.File(self.fname,'r') as f:
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for dataset in sets:
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for group in self.groups_with_datasets(dataset):
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path = os.path.join(group,dataset)
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inc,prop,name,cat,item = (path.split('/') + ['']*5)[:5]
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key = '/'.join([prop,name+tag])
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if key not in inGeom:
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if prop == 'geometry':
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inGeom[key] = inData[key] = np.arange(self.Nmaterialpoints)
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elif prop == 'constituent':
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inGeom[key] = np.where(f['mapping/cellResults/constituent'][:,component]['Name'] == str.encode(name))[0]
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inData[key] = f['mapping/cellResults/constituent'][inGeom[key],component]['Position']
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else:
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inGeom[key] = np.where(f['mapping/cellResults/materialpoint']['Name'] == str.encode(name))[0]
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inData[key] = f['mapping/cellResults/materialpoint'][inGeom[key].tolist()]['Position']
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shape = np.shape(f[path])
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data = np.full((self.Nmaterialpoints,) + (shape[1:] if len(shape)>1 else (1,)),
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np.nan,
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dtype=np.dtype(f[path]))
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data[inGeom[key]] = (f[path] if len(shape)>1 else np.expand_dims(f[path],1))[inData[key]]
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path = (os.path.join(*([prop,name]+([cat] if cat else [])+([item] if item else []))) if split else path)+tag
|
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if split:
|
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try:
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tbl[inc].add(path,data)
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except KeyError:
|
||
tbl[inc] = Table(data.reshape(self.Nmaterialpoints,-1),{path:data.shape[1:]})
|
||
else:
|
||
try:
|
||
tbl.add(path,data)
|
||
except AttributeError:
|
||
tbl = Table(data.reshape(self.Nmaterialpoints,-1),{path:data.shape[1:]})
|
||
|
||
return tbl
|
||
|
||
|
||
def groups_with_datasets(self,datasets):
|
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"""
|
||
Return groups that contain all requested datasets.
|
||
|
||
Only groups within
|
||
- inc*/constituent/*/*
|
||
- inc*/materialpoint/*/*
|
||
- inc*/geometry/*
|
||
|
||
are considered as they contain user-relevant data.
|
||
Single strings will be treated as list with one entry.
|
||
|
||
Wild card matching is allowed, but the number of arguments need to fit.
|
||
|
||
Parameters
|
||
----------
|
||
datasets : iterable or str or bool
|
||
|
||
Examples
|
||
--------
|
||
datasets = False matches no group
|
||
datasets = True matches all groups
|
||
datasets = ['F','P'] matches a group with ['F','P','sigma']
|
||
datasets = ['*','P'] matches a group with ['F','P']
|
||
datasets = ['*'] does not match a group with ['F','P','sigma']
|
||
datasets = ['*','*'] does not match a group with ['F','P','sigma']
|
||
datasets = ['*','*','*'] matches a group with ['F','P','sigma']
|
||
|
||
"""
|
||
if datasets is False: return []
|
||
|
||
sets = datasets if isinstance(datasets,bool) or (hasattr(datasets,'__iter__') and not isinstance(datasets,str)) else \
|
||
[datasets]
|
||
|
||
groups = []
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
for i in self.iterate('increments'):
|
||
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
|
||
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:
|
||
match = [e for e_ in [glob.fnmatch.filter(f[group].keys(),s) for s in sets] for e in e_]
|
||
if len(set(match)) == len(sets): groups.append(group)
|
||
return groups
|
||
|
||
|
||
def list_data(self):
|
||
"""Return information on all active datasets in the file."""
|
||
message = ''
|
||
with h5py.File(self.fname,'r') as f:
|
||
for i in self.iterate('increments'):
|
||
message += '\n{} ({}s)\n'.format(i,self.times[self.increments.index(i)])
|
||
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
|
||
for oo in self.iterate(o):
|
||
message += ' {}\n'.format(oo)
|
||
for pp in self.iterate(p):
|
||
message += ' {}\n'.format(pp)
|
||
group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue
|
||
for d in f[group].keys():
|
||
try:
|
||
dataset = f['/'.join([group,d])]
|
||
message += ' {} / ({}): {}\n'.\
|
||
format(d,dataset.attrs['Unit'].decode(),dataset.attrs['Description'].decode())
|
||
except KeyError:
|
||
pass
|
||
return message
|
||
|
||
|
||
def get_dataset_location(self,label):
|
||
"""Return the location of all active datasets with given label."""
|
||
path = []
|
||
with h5py.File(self.fname,'r') as f:
|
||
for i in self.iterate('increments'):
|
||
k = '/'.join([i,'geometry',label])
|
||
try:
|
||
f[k]
|
||
path.append(k)
|
||
except KeyError:
|
||
pass
|
||
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
|
||
for oo in self.iterate(o):
|
||
for pp in self.iterate(p):
|
||
k = '/'.join([i,o[:-1],oo,pp,label])
|
||
try:
|
||
f[k]
|
||
path.append(k)
|
||
except KeyError:
|
||
pass
|
||
return path
|
||
|
||
|
||
def get_constituent_ID(self,c=0):
|
||
"""Pointwise constituent ID."""
|
||
with h5py.File(self.fname,'r') as f:
|
||
names = f['/mapping/cellResults/constituent']['Name'][:,c].astype('str')
|
||
return np.array([int(n.split('_')[0]) for n in names.tolist()],dtype=np.int32)
|
||
|
||
|
||
def get_crystal_structure(self): # ToDo: extension to multi constituents/phase
|
||
"""Info about the crystal structure."""
|
||
with h5py.File(self.fname,'r') as f:
|
||
return f[self.get_dataset_location('orientation')[0]].attrs['Lattice'].astype('str') # np.bytes_ to string
|
||
|
||
|
||
def read_dataset(self,path,c=0,plain=False):
|
||
"""
|
||
Dataset for all points/cells.
|
||
|
||
If more than one path is given, the dataset is composed of the individual contributions.
|
||
"""
|
||
with h5py.File(self.fname,'r') as f:
|
||
shape = (self.Nmaterialpoints,) + np.shape(f[path[0]])[1:]
|
||
if len(shape) == 1: shape = shape +(1,)
|
||
dataset = np.full(shape,np.nan,dtype=np.dtype(f[path[0]]))
|
||
for pa in path:
|
||
label = pa.split('/')[2]
|
||
|
||
if pa.split('/')[1] == 'geometry':
|
||
dataset = np.array(f[pa])
|
||
continue
|
||
|
||
p = np.where(f['mapping/cellResults/constituent'][:,c]['Name'] == str.encode(label))[0]
|
||
if len(p)>0:
|
||
u = (f['mapping/cellResults/constituent']['Position'][p,c])
|
||
a = np.array(f[pa])
|
||
if len(a.shape) == 1:
|
||
a=a.reshape([a.shape[0],1])
|
||
dataset[p,:] = a[u,:]
|
||
|
||
p = np.where(f['mapping/cellResults/materialpoint']['Name'] == str.encode(label))[0]
|
||
if len(p)>0:
|
||
u = (f['mapping/cellResults/materialpoint']['Position'][p.tolist()])
|
||
a = np.array(f[pa])
|
||
if len(a.shape) == 1:
|
||
a=a.reshape([a.shape[0],1])
|
||
dataset[p,:] = a[u,:]
|
||
|
||
if plain and dataset.dtype.names is not None:
|
||
return dataset.view(('float64',len(dataset.dtype.names)))
|
||
else:
|
||
return dataset
|
||
|
||
|
||
def cell_coordinates(self):
|
||
"""Return initial coordinates of the cell centers."""
|
||
if self.structured:
|
||
return grid_filters.cell_coord0(self.grid,self.size,self.origin).reshape(-1,3,order='F')
|
||
else:
|
||
with h5py.File(self.fname,'r') as f:
|
||
return f['geometry/x_c'][()]
|
||
|
||
def node_coordinates(self):
|
||
"""Return initial coordinates of the cell centers."""
|
||
if self.structured:
|
||
return grid_filters.node_coord0(self.grid,self.size,self.origin).reshape(-1,3,order='F')
|
||
else:
|
||
with h5py.File(self.fname,'r') as f:
|
||
return f['geometry/x_n'][()]
|
||
|
||
|
||
@staticmethod
|
||
def _add_absolute(x):
|
||
return {
|
||
'data': np.abs(x['data']),
|
||
'label': '|{}|'.format(x['label']),
|
||
'meta': {
|
||
'Unit': x['meta']['Unit'],
|
||
'Description': 'Absolute value of {} ({})'.format(x['label'],x['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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('#{}#'.format(d),"kwargs['{}']['data']".format(d))
|
||
|
||
return {
|
||
'data': eval(formula),
|
||
'label': kwargs['label'],
|
||
'meta': {
|
||
'Unit': kwargs['unit'],
|
||
'Description': '{} (formula: {})'.format(kwargs['description'],kwargs['formula']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
def add_calculation(self,label,formula,unit='n/a',description=None,vectorized=True):
|
||
"""
|
||
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.
|
||
vectorized : bool, optional
|
||
Indicate whether the formula can be used in vectorized form. Defaults to ‘True’.
|
||
|
||
"""
|
||
if not vectorized:
|
||
raise NotImplementedError
|
||
|
||
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_Cauchy(P,F):
|
||
return {
|
||
'data': mechanics.Cauchy(P['data'],F['data']),
|
||
'label': 'sigma',
|
||
'meta': {
|
||
'Unit': P['meta']['Unit'],
|
||
'Description': 'Cauchy stress calculated from {} ({}) and {} ({})'\
|
||
.format(P['label'],P['meta']['Description'],F['label'],F['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
def add_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_Cauchy,{'P':P,'F':F})
|
||
|
||
|
||
@staticmethod
|
||
def _add_determinant(T):
|
||
return {
|
||
'data': np.linalg.det(T['data']),
|
||
'label': 'det({})'.format(T['label']),
|
||
'meta': {
|
||
'Unit': T['meta']['Unit'],
|
||
'Description': 'Determinant of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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': mechanics.deviatoric_part(T['data']),
|
||
'label': 's_{}'.format(T['label']),
|
||
'meta': {
|
||
'Unit': T['meta']['Unit'],
|
||
'Description': 'Deviator of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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': mechanics.eigenvalues(T_sym['data'])[:,p],
|
||
'label': 'lambda_{}({})'.format(eigenvalue,T_sym['label']),
|
||
'meta' : {
|
||
'Unit': T_sym['meta']['Unit'],
|
||
'Description': '{} eigenvalue of {} ({})'.format(label,T_sym['label'],T_sym['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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
|
||
print('p',eigenvalue)
|
||
return {
|
||
'data': mechanics.eigenvectors(T_sym['data'])[:,p],
|
||
'label': 'v_{}({})'.format(eigenvalue,T_sym['label']),
|
||
'meta' : {
|
||
'Unit': '1',
|
||
'Description': 'Eigenvector corresponding to {} eigenvalue of {} ({})'\
|
||
.format(label,T_sym['label'],T_sym['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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_IPFcolor(q,l):
|
||
d = np.array(l)
|
||
d_unit = d/np.linalg.norm(d)
|
||
m = util.scale_to_coprime(d)
|
||
colors = np.empty((len(q['data']),3),np.uint8)
|
||
|
||
lattice = q['meta']['Lattice']
|
||
|
||
for i,qu in enumerate(q['data']):
|
||
o = Orientation(np.array([qu['w'],qu['x'],qu['y'],qu['z']]),lattice).reduced()
|
||
colors[i] = np.uint8(o.IPFcolor(d_unit)*255)
|
||
|
||
return {
|
||
'data': colors,
|
||
'label': 'IPFcolor_[{} {} {}]'.format(*m),
|
||
'meta' : {
|
||
'Unit': 'RGB (8bit)',
|
||
'Lattice': lattice,
|
||
'Description': 'Inverse Pole Figure (IPF) colors along sample direction [{} {} {}]'.format(*m),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
def add_IPFcolor(self,q,l):
|
||
"""
|
||
Add RGB color tuple of inverse pole figure (IPF) color.
|
||
|
||
Parameters
|
||
----------
|
||
q : str
|
||
Label of the dataset containing the crystallographic orientation as quaternions.
|
||
l : numpy.array of shape (3)
|
||
Lab frame direction for inverse pole figure.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_IPFcolor,{'q':q},{'l':l})
|
||
|
||
|
||
@staticmethod
|
||
def _add_maximum_shear(T_sym):
|
||
return {
|
||
'data': mechanics.maximum_shear(T_sym['data']),
|
||
'label': 'max_shear({})'.format(T_sym['label']),
|
||
'meta': {
|
||
'Unit': T_sym['meta']['Unit'],
|
||
'Description': 'Maximum shear component of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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_Mises(T_sym):
|
||
t = 'strain' if T_sym['meta']['Unit'] == '1' else \
|
||
'stress'
|
||
|
||
return {
|
||
'data': mechanics.Mises_strain(T_sym['data']) if t=='strain' else mechanics.Mises_stress(T_sym['data']),
|
||
'label': '{}_vM'.format(T_sym['label']),
|
||
'meta': {
|
||
'Unit': T_sym['meta']['Unit'],
|
||
'Description': 'Mises equivalent {} of {} ({})'.format(t,T_sym['label'],T_sym['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
def add_Mises(self,T_sym):
|
||
"""
|
||
Add the equivalent Mises stress or strain of a symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Label of symmetric tensorial stress or strain dataset.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_Mises,{'T_sym':T_sym})
|
||
|
||
|
||
@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': '|{}|_{}'.format(x['label'],o),
|
||
'meta': {
|
||
'Unit': x['meta']['Unit'],
|
||
'Description': '{}-norm of {} {} ({})'.format(o,t,x['label'],x['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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_PK2(P,F):
|
||
return {
|
||
'data': mechanics.PK2(P['data'],F['data']),
|
||
'label': 'S',
|
||
'meta': {
|
||
'Unit': P['meta']['Unit'],
|
||
'Description': '2. Piola-Kirchhoff stress calculated from {} ({}) and {} ({})'\
|
||
.format(P['label'],P['meta']['Description'],F['label'],F['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
def add_PK2(self,P='P',F='F'):
|
||
"""
|
||
Add second Piola-Kirchhoff calculated from first Piola-Kirchhoff stress and deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
P : str, optional
|
||
Label 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_PK2,{'P':P,'F':F})
|
||
|
||
|
||
@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': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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_rotational_part(F):
|
||
return {
|
||
'data': mechanics.rotational_part(F['data']),
|
||
'label': 'R({})'.format(F['label']),
|
||
'meta': {
|
||
'Unit': F['meta']['Unit'],
|
||
'Description': 'Rotational part of {} ({})'.format(F['label'],F['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
def add_rotational_part(self,F):
|
||
"""
|
||
Add rotational part of a deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
F : str, optional
|
||
Label of deformation gradient dataset.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_rotational_part,{'F':F})
|
||
|
||
|
||
@staticmethod
|
||
def _add_spherical(T):
|
||
return {
|
||
'data': mechanics.spherical_part(T['data']),
|
||
'label': 'p_{}'.format(T['label']),
|
||
'meta': {
|
||
'Unit': T['meta']['Unit'],
|
||
'Description': 'Spherical component of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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_tensor(F,t,m):
|
||
return {
|
||
'data': mechanics.strain_tensor(F['data'],t,m),
|
||
'label': 'epsilon_{}^{}({})'.format(t,m,F['label']),
|
||
'meta': {
|
||
'Unit': F['meta']['Unit'],
|
||
'Description': 'Strain tensor of {} ({})'.format(F['label'],F['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
def add_strain_tensor(self,F='F',t='V',m=0.0):
|
||
"""
|
||
Add strain tensor of a deformation gradient.
|
||
|
||
For details refer to damask.mechanics.strain_tensor
|
||
|
||
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_tensor,{'F':F},{'t':t,'m':m})
|
||
|
||
|
||
@staticmethod
|
||
def _add_stretch_tensor(F,t):
|
||
return {
|
||
'data': mechanics.left_stretch(F['data']) if t == 'V' else mechanics.right_stretch(F['data']),
|
||
'label': '{}({})'.format(t,F['label']),
|
||
'meta': {
|
||
'Unit': F['meta']['Unit'],
|
||
'Description': '{} stretch tensor of {} ({})'.format('Left' if t == 'V' else 'Right',
|
||
F['label'],F['meta']['Description']),
|
||
'Creator': inspect.stack()[0][3][1:]
|
||
}
|
||
}
|
||
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.decode() for k,v in loc.attrs.items()}}
|
||
lock.release()
|
||
r = func(**datasets_in,**args)
|
||
return [group,r]
|
||
except Exception as err:
|
||
print('Error during calculation: {}.'.format(err))
|
||
return None
|
||
|
||
|
||
def _add_generic_pointwise(self,func,datasets,args={}):
|
||
"""
|
||
General function to add pointwise data.
|
||
|
||
Parameters
|
||
----------
|
||
func : function
|
||
Callback function that calculates a new dataset from one or more datasets per HDF5 group.
|
||
datasets : dictionary
|
||
Details of the datasets to be used: label (in HDF5 file) and arg (argument to which the data is parsed in func).
|
||
args : dictionary, optional
|
||
Arguments parsed to func.
|
||
|
||
"""
|
||
pool = multiprocessing.Pool(int(Environment().options['DAMASK_NUM_THREADS']))
|
||
lock = multiprocessing.Manager().Lock()
|
||
|
||
groups = self.groups_with_datasets(datasets.values())
|
||
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_overwrite and result[0]+'/'+result[1]['label'] in f:
|
||
dataset = f[result[0]+'/'+result[1]['label']]
|
||
dataset[...] = result[1]['data']
|
||
dataset.attrs['Overwritten'] = 'Yes'.encode()
|
||
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').encode()
|
||
|
||
for l,v in result[1]['meta'].items():
|
||
dataset.attrs[l]=v.encode()
|
||
creator = 'damask.Result.{} v{}'.format(dataset.attrs['Creator'].decode(),version)
|
||
dataset.attrs['Creator'] = creator.encode()
|
||
|
||
except (OSError,RuntimeError) as err:
|
||
print('Could not add dataset: {}.'.format(err))
|
||
lock.release()
|
||
|
||
pool.close()
|
||
pool.join()
|
||
|
||
|
||
def write_XDMF(self):
|
||
"""
|
||
Write XDMF file to directly visualize data in DADF5 file.
|
||
|
||
This works only for scalar, 3-vector and 3x3-tensor data.
|
||
Selection is not taken into account.
|
||
"""
|
||
if len(self.constituents) != 1 or not self.structured:
|
||
raise NotImplementedError('XDMF only available for grid results with 1 constituent.')
|
||
|
||
xdmf=ET.Element('Xdmf')
|
||
xdmf.attrib={'Version': '2.0',
|
||
'xmlns:xi': 'http://www.w3.org/2001/XInclude'}
|
||
|
||
domain=ET.SubElement(xdmf, 'Domain')
|
||
|
||
collection = ET.SubElement(domain, 'Grid')
|
||
collection.attrib={'GridType': 'Collection',
|
||
'CollectionType': 'Temporal'}
|
||
|
||
time = ET.SubElement(collection, 'Time')
|
||
time.attrib={'TimeType': 'List'}
|
||
|
||
time_data = ET.SubElement(time, 'DataItem')
|
||
time_data.attrib={'Format': 'XML',
|
||
'NumberType': 'Float',
|
||
'Dimensions': '{}'.format(len(self.times))}
|
||
time_data.text = ' '.join(map(str,self.times))
|
||
|
||
attributes = []
|
||
data_items = []
|
||
|
||
for inc in self.increments:
|
||
|
||
grid=ET.SubElement(collection,'Grid')
|
||
grid.attrib = {'GridType': 'Uniform',
|
||
'Name': inc}
|
||
|
||
topology=ET.SubElement(grid, 'Topology')
|
||
topology.attrib={'TopologyType': '3DCoRectMesh',
|
||
'Dimensions': '{} {} {}'.format(*self.grid+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.grid))
|
||
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
attributes.append(ET.SubElement(grid, 'Attribute'))
|
||
attributes[-1].attrib={'Name': 'u',
|
||
'Center': 'Node',
|
||
'AttributeType': 'Vector'}
|
||
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
|
||
data_items[-1].attrib={'Format': 'HDF',
|
||
'Precision': '8',
|
||
'Dimensions': '{} {} {} 3'.format(*(self.grid+1))}
|
||
data_items[-1].text='{}:/{}/geometry/u_n'.format(os.path.split(self.fname)[1],inc)
|
||
|
||
for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
|
||
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
|
||
prec = f[name].dtype.itemsize
|
||
|
||
if (shape not in [(1,), (3,), (3,3)]) or dtype != np.float64: continue
|
||
|
||
attributes.append(ET.SubElement(grid, 'Attribute'))
|
||
attributes[-1].attrib={'Name': '{}'.format(name.split('/',2)[2]),
|
||
'Center': 'Cell',
|
||
'AttributeType': 'Tensor'}
|
||
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
|
||
data_items[-1].attrib={'Format': 'HDF',
|
||
'NumberType': 'Float',
|
||
'Precision': '{}'.format(prec),
|
||
'Dimensions': '{} {} {} {}'.format(*self.grid,np.prod(shape))}
|
||
data_items[-1].text='{}:{}'.format(os.path.split(self.fname)[1],name)
|
||
|
||
with open(os.path.splitext(self.fname)[0]+'.xdmf','w') as f:
|
||
f.write(xml.dom.minidom.parseString(ET.tostring(xdmf).decode()).toprettyxml())
|
||
|
||
|
||
def to_vtk(self,labels=[],mode='cell'):
|
||
"""
|
||
Export to vtk cell/point data.
|
||
|
||
Parameters
|
||
----------
|
||
labels : str or list of, optional
|
||
Labels of the datasets to be exported.
|
||
mode : str, either 'cell' or 'point'
|
||
Export in cell format or point format.
|
||
Defaults to 'cell'.
|
||
|
||
"""
|
||
if mode.lower()=='cell':
|
||
|
||
if self.structured:
|
||
v = VTK.from_rectilinearGrid(self.grid,self.size,self.origin)
|
||
else:
|
||
with h5py.File(self.fname,'r') as f:
|
||
v = VTK.from_unstructuredGrid(f['/geometry/x_n'][()],
|
||
f['/geometry/T_c'][()]-1,
|
||
f['/geometry/T_c'].attrs['VTK_TYPE'].decode())
|
||
elif mode.lower()=='point':
|
||
v = VTK.from_polyData(self.cell_coordinates())
|
||
|
||
N_digits = int(np.floor(np.log10(int(self.increments[-1][3:]))))+1
|
||
|
||
for inc in util.show_progress(self.iterate('increments'),len(self.selection['increments'])):
|
||
|
||
materialpoints_backup = self.selection['materialpoints'].copy()
|
||
self.pick('materialpoints',False)
|
||
for label in (labels if isinstance(labels,list) else [labels]):
|
||
for p in self.iterate('con_physics'):
|
||
if p != 'generic':
|
||
for c in self.iterate('constituents'):
|
||
x = self.get_dataset_location(label)
|
||
if len(x) == 0:
|
||
continue
|
||
array = self.read_dataset(x,0)
|
||
v.add(array,'1_'+x[0].split('/',1)[1]) #ToDo: hard coded 1!
|
||
else:
|
||
x = self.get_dataset_location(label)
|
||
if len(x) == 0:
|
||
continue
|
||
array = self.read_dataset(x,0)
|
||
ph_name = re.compile(r'(?<=(constituent\/))(.*?)(?=(generic))') # identify phase name
|
||
dset_name = '1_' + re.sub(ph_name,r'',x[0].split('/',1)[1]) # removing phase name
|
||
v.add(array,dset_name)
|
||
self.pick('materialpoints',materialpoints_backup)
|
||
|
||
constituents_backup = self.selection['constituents'].copy()
|
||
self.pick('constituents',False)
|
||
for label in (labels if isinstance(labels,list) else [labels]):
|
||
for p in self.iterate('mat_physics'):
|
||
if p != 'generic':
|
||
for m in self.iterate('materialpoints'):
|
||
x = self.get_dataset_location(label)
|
||
if len(x) == 0:
|
||
continue
|
||
array = self.read_dataset(x,0)
|
||
v.add(array,'1_'+x[0].split('/',1)[1]) #ToDo: why 1_?
|
||
else:
|
||
x = self.get_dataset_location(label)
|
||
if len(x) == 0:
|
||
continue
|
||
array = self.read_dataset(x,0)
|
||
v.add(array,'1_'+x[0].split('/',1)[1])
|
||
self.pick('constituents',constituents_backup)
|
||
|
||
u = self.read_dataset(self.get_dataset_location('u_n' if mode.lower() == 'cell' else 'u_p'))
|
||
v.add(u,'u')
|
||
|
||
file_out = '{}_inc{}'.format(os.path.splitext(os.path.basename(self.fname))[0],
|
||
inc[3:].zfill(N_digits))
|
||
|
||
v.write(file_out)
|
||
|
||
###################################################################################################
|
||
# BEGIN DEPRECATED
|
||
|
||
def set_by_time(self,start,end):
|
||
"""
|
||
Set active increments based on start and end time.
|
||
|
||
Parameters
|
||
----------
|
||
start : float
|
||
start time (included)
|
||
end : float
|
||
end time (included)
|
||
|
||
"""
|
||
self._manage_selection('set','times',self.times_in_range(start,end))
|