1920 lines
76 KiB
Python
1920 lines
76 KiB
Python
import multiprocessing as mp
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from multiprocessing.synchronize import Lock
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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 # noqa
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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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from typing import Union, Callable, Any, Sequence, Literal, Dict, List, Tuple
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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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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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from ._typehints import FloatSequence, IntSequence
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h5py3 = h5py.__version__[0] == '3'
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chunk_size = 1024**2//8 # for compression in HDF5
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prefix_inc = 'increment_'
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def _read(dataset: h5py._hl.dataset.Dataset) -> np.ndarray:
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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) # type: ignore
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return np.array(dataset,dtype=dtype)
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def _match(requested,
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existing: h5py._hl.base.KeysViewHDF5) -> List[Any]:
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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: np.ma.core.MaskedArray,
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N_materialpoints: int,
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fill_float: float,
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fill_int: int) -> np.ma.core.MaskedArray:
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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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Add data to and export data from a DADF5 file.
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A DADF5 (DAMASK HDF5) file contains DAMASK results.
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Its group/folder structure reflects the layout in material.yaml.
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This class provides a customizable view on the DADF5 file.
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Upon initialization, all attributes are visible.
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Derived quantities are added to the file and existing data is
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exported based on the current view.
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Examples
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--------
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Open 'my_file.hdf5', which is assumed to contain deformation gradient 'F'
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and first Piola-Kirchhoff stress 'P', add the Mises equivalent of the
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Cauchy stress, and export it to VTK (file) and numpy.ndarray (memory).
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>>> import damask
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>>> r = damask.Result('my_file.hdf5')
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>>> r.add_stress_Cauchy()
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>>> r.add_equivalent_Mises('sigma')
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>>> r.export_VTK()
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>>> r_last = r.view(increments=-1)
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>>> sigma_vM_last = r_last.get('sigma_vM')
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"""
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def __init__(self, fname: Union[str, Path]):
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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 12 <= self.version_minor <= 14:
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raise TypeError(f'unsupported DADF5 version "{self.version_major}.{self.version_minor}"')
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if self.version_major == 0 and self.version_minor < 14:
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self.export_setup = None # type: ignore
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self.structured = 'cells' in f['geometry'].attrs.keys()
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if self.structured:
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self.cells = f['geometry'].attrs['cells']
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self.size = f['geometry'].attrs['size']
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self.origin = f['geometry'].attrs['origin']
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else:
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self.add_curl = self.add_divergence = self.add_gradient = None # type: ignore
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r = re.compile(rf'{prefix_inc}([0-9]+)')
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self.increments = sorted([i for i in f.keys() if r.match(i)],key=util.natural_sort)
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self.times = [round(f[i].attrs['t/s'],12) for i in self.increments]
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if len(self.increments) == 0:
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raise ValueError('incomplete DADF5 file')
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self.N_materialpoints, self.N_constituents = np.shape(f['cell_to/phase'])
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self.homogenization = f['cell_to/homogenization']['label'].astype('str')
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self.homogenizations = sorted(np.unique(self.homogenization),key=util.natural_sort)
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self.phase = f['cell_to/phase']['label'].astype('str')
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self.phases = sorted(np.unique(self.phase),key=util.natural_sort)
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self.fields: List[str] = []
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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).expanduser().absolute()
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self._protected = True
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def __copy__(self) -> "Result":
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"""
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Return deepcopy(self).
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Create deep copy.
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"""
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return copy.deepcopy(self)
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copy = __copy__
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def __repr__(self) -> str:
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"""
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Return repr(self).
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Give short human-readable summary.
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"""
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with h5py.File(self.fname,'r') as f:
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header = [f'Created by {f.attrs["creator"]}',
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f' on {f.attrs["created"]}',
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f' executing "{f.attrs["call"]}"']
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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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[f'\n{inc}\n ...' for inc in visible_increments[1:-1]]
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return util.srepr([util.deemph(header)] + first + in_between + last)
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def _manage_view(self,
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action: Literal['set', 'add', 'del'],
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increments: Union[int, Sequence[int], str, Sequence[str], bool] = None,
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times: Union[float, Sequence[float], str, Sequence[str], bool] = None,
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phases: Union[str, Sequence[str], bool] = None,
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homogenizations: Union[str, Sequence[str], bool] = None,
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fields: Union[str, Sequence[str], bool] = None) -> "Result":
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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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Returns
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-------
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view : damask.Result
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Modified or new view on the DADF5 file.
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"""
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if increments is not None and times is not None:
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raise ValueError('"increments" and "times" are mutually exclusive')
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dup = self.copy()
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for what,datasets in zip(['increments','times','phases','homogenizations','fields'],
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[ increments, times, phases, homogenizations, fields ]):
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if datasets is None:
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continue
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# allow True/False and string arguments
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elif 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 not hasattr(datasets,'__iter__') or isinstance(datasets,str) else list(datasets) # type: ignore
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if what == 'increments':
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choice = [c if isinstance(c,str) and c.startswith(prefix_inc) else
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self.increments[c] if isinstance(c,int) and c<0 else
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f'{prefix_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) # type: ignore
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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]) # type: ignore
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valid = _match(choice,getattr(self,what))
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existing = set(self.visible[what])
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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 increments_in_range(self,
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start: Union[str, int] = None,
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end: Union[str, int] = None) -> Sequence[int]:
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"""
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Get all increments within a given range.
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Parameters
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----------
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start : int or str, optional
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Start increment. Defaults to first.
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end : int or str, optional
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End increment. Defaults to last.
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Returns
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-------
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increments : list of ints
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Increment number of all increments within the given bounds.
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"""
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s,e = map(lambda x: int(x[10:] if isinstance(x,str) and x.startswith(prefix_inc) else x),
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(self.incs[ 0] if start is None else start,
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self.incs[-1] if end is None else end))
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return [i for i in self.incs if s <= i <= e]
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def times_in_range(self,
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start: float = None,
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end: float = None) -> Sequence[int]:
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"""
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Get all increments within a given time range.
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Parameters
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----------
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start : float, optional
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Time of start increment. Defaults to first.
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end : float, optional
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Time of end increment. Defaults to last.
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Returns
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-------
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times : list of float
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Time of each increment within the given bounds.
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"""
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s,e = (self.times[ 0] if start is None else start,
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self.times[-1] if end is None else end)
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return [t for t in self.times if s <= t <= e]
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def view(self,*,
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increments: Union[int, Sequence[int], str, Sequence[str], bool] = None,
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times: Union[float, Sequence[float], str, Sequence[str], bool] = None,
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phases: Union[str, Sequence[str], bool] = None,
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homogenizations: Union[str, Sequence[str], bool] = None,
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fields: Union[str, Sequence[str], bool] = None,
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protected: bool = None) -> "Result":
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"""
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Set view.
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Wildcard matching with '?' and '*' is supported.
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True is equivalent to '*', False is equivalent to [].
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Parameters
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----------
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increments: (list of) int, (list of) str, or bool, optional.
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Number(s) of increments to select.
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times: (list of) float, (list of) str, or bool, optional.
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Simulation time(s) of increments to select.
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phases: (list of) str, or bool, optional.
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Name(s) of phases to select.
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homogenizations: (list of) str, or bool, optional.
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Name(s) of homogenizations to select.
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fields: (list of) str, or bool, optional.
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Name(s) of fields to select.
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protected: bool, optional.
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Protection status of existing data.
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Returns
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-------
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view : damask.Result
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View with only the selected attributes being visible.
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Examples
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--------
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Get a view that shows only results from the initial configuration:
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>>> import damask
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>>> r = damask.Result('my_file.hdf5')
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>>> r_first = r.view(increments=0)
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Get a view that shows all results between simulation times of 10 to 40:
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>>> import damask
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>>> r = damask.Result('my_file.hdf5')
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>>> r_t10to40 = r.view(times=r.times_in_range(10.0,40.0))
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"""
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dup = self._manage_view('set',increments,times,phases,homogenizations,fields)
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if protected is not None:
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if not protected:
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print(util.warn('Warning: Modification of existing datasets allowed!'))
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dup._protected = protected
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return dup
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def view_more(self,*,
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increments: Union[int, Sequence[int], str, Sequence[str], bool] = None,
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times: Union[float, Sequence[float], str, Sequence[str], bool] = None,
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phases: Union[str, Sequence[str], bool] = None,
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homogenizations: Union[str, Sequence[str], bool] = None,
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fields: Union[str, Sequence[str], bool] = None) -> "Result":
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"""
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Add to view.
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Wildcard matching with '?' and '*' is supported.
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True is equivalent to '*', False is equivalent to [].
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Parameters
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----------
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increments: (list of) int, (list of) str, or bool, optional.
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Number(s) of increments to select.
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times: (list of) float, (list of) str, or bool, optional.
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Simulation time(s) of increments to select.
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phases: (list of) str, or bool, optional.
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Name(s) of phases to select.
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homogenizations: (list of) str, or bool, optional.
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Name(s) of homogenizations to select.
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fields: (list of) str, or bool, optional.
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Name(s) of fields to select.
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Returns
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-------
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modified_view : damask.Result
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View with additional visible attributes.
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Examples
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--------
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Get a view that shows only results from first and last increment:
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>>> import damask
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>>> r_empty = damask.Result('my_file.hdf5').view(increments=False)
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>>> r_first = r_empty.view_more(increments=0)
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>>> r_first_and_last = r.first.view_more(increments=-1)
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"""
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return self._manage_view('add',increments,times,phases,homogenizations,fields)
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def view_less(self,*,
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increments: Union[int, Sequence[int], str, Sequence[str], bool] = None,
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times: Union[float, Sequence[float], str, Sequence[str], bool] = None,
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phases: Union[str, Sequence[str], bool] = None,
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homogenizations: Union[str, Sequence[str], bool] = None,
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fields: Union[str, Sequence[str], bool] = None) -> "Result":
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"""
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Remove from view.
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Wildcard matching with '?' and '*' is supported.
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True is equivalent to '*', False is equivalent to [].
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Parameters
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----------
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increments: (list of) int, (list of) str, or bool, optional.
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Number(s) of increments to select.
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times: (list of) float, (list of) str, or bool, optional.
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Simulation time(s) of increments to select.
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phases: (list of) str, or bool, optional.
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Name(s) of phases to select.
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homogenizations: (list of) str, or bool, optional.
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Name(s) of homogenizations to select.
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fields: (list of) str, or bool, optional.
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Name(s) of fields to select.
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Returns
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-------
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modified_view : damask.Result
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View with fewer visible attributes.
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Examples
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--------
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Get a view that omits the undeformed configuration:
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>>> import damask
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>>> r_all = damask.Result('my_file.hdf5')
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>>> r_deformed = r_all.view_less(increments=0)
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"""
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return self._manage_view('del',increments,times,phases,homogenizations,fields)
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def rename(self,
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name_src: str,
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name_dst: str):
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"""
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Rename/move datasets (within the same group/folder).
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This operation is discouraged because the history of the
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data becomes untraceable and data integrity is not ensured.
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Parameters
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----------
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name_src : str
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Name of the datasets to be renamed.
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name_dst : str
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New name of the datasets.
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Examples
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--------
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Rename datasets containing the deformation gradient from 'F' to 'def_grad':
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>>> import damask
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>>> r = damask.Result('my_file.hdf5')
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>>> r_unprotected = r.view(protected=False)
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>>> r_unprotected.rename('F','def_grad')
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"""
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if self._protected:
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raise PermissionError('rename datasets')
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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 _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
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path_src = '/'.join([inc,ty,label,field,name_src])
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path_dst = '/'.join([inc,ty,label,field,name_dst])
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if path_src in f.keys():
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f[path_dst] = f[path_src]
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f[path_dst].attrs['renamed'] = f'original name: {name_src}' if h5py3 else \
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f'original name: {name_src}'.encode()
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del f[path_src]
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def remove(self, name: str):
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"""
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Remove/delete datasets.
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This operation is discouraged because the history of the
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data becomes untraceable and data integrity is not ensured.
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Parameters
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----------
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name : str
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Name of the datasets to be deleted.
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Examples
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--------
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Delete the deformation gradient 'F':
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>>> import damask
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>>> r = damask.Result('my_file.hdf5')
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>>> r_unprotected = r.view(protected=False)
|
||
>>> r_unprotected.remove('F')
|
||
|
||
"""
|
||
if self._protected:
|
||
raise PermissionError('delete datasets')
|
||
|
||
with h5py.File(self.fname,'a') as f:
|
||
for inc in self.visible['increments']:
|
||
for ty in ['phase','homogenization']:
|
||
for label in self.visible[ty+'s']:
|
||
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
|
||
path = '/'.join([inc,ty,label,field,name])
|
||
if path in f.keys(): del f[path]
|
||
|
||
|
||
def list_data(self) -> List[str]:
|
||
"""
|
||
Collect information on all active datasets in the file.
|
||
|
||
Returns
|
||
-------
|
||
data : list of str
|
||
Line-formatted information about active datasets.
|
||
|
||
"""
|
||
msg = []
|
||
with h5py.File(self.fname,'r') as f:
|
||
for inc in self.visible['increments']:
|
||
msg += [f'\n{inc} ({self.times[self.increments.index(inc)]} s)']
|
||
for ty in ['phase','homogenization']:
|
||
msg += [f' {ty}']
|
||
for label in self.visible[ty+'s']:
|
||
msg += [f' {label}']
|
||
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
|
||
msg += [f' {field}']
|
||
for d in f['/'.join([inc,ty,label,field])].keys():
|
||
dataset = f['/'.join([inc,ty,label,field,d])]
|
||
unit = dataset.attrs["unit"] if h5py3 else \
|
||
dataset.attrs["unit"].decode()
|
||
description = dataset.attrs['description'] if h5py3 else \
|
||
dataset.attrs['description'].decode()
|
||
msg += [f' {d} / {unit}: {description}']
|
||
|
||
return msg
|
||
|
||
|
||
def enable_user_function(self,
|
||
func: Callable):
|
||
globals()[func.__name__]=func
|
||
print(f'Function {func.__name__} enabled in add_calculation.')
|
||
|
||
|
||
@property
|
||
def incs(self):
|
||
return [int(i.split(prefix_inc)[-1]) for i in self.increments]
|
||
|
||
|
||
@property
|
||
def coordinates0_point(self) -> np.ndarray:
|
||
"""Initial/undeformed cell center coordinates."""
|
||
if self.structured:
|
||
return grid_filters.coordinates0_point(self.cells,self.size,self.origin).reshape(-1,3,order='F')
|
||
else:
|
||
with h5py.File(self.fname,'r') as f:
|
||
return f['geometry/x_p'][()]
|
||
|
||
@property
|
||
def coordinates0_node(self) -> np.ndarray:
|
||
"""Initial/undeformed nodal coordinates."""
|
||
if self.structured:
|
||
return grid_filters.coordinates0_node(self.cells,self.size,self.origin).reshape(-1,3,order='F')
|
||
else:
|
||
with h5py.File(self.fname,'r') as f:
|
||
return f['geometry/x_n'][()]
|
||
|
||
@property
|
||
def geometry0(self) -> VTK:
|
||
"""Initial/undeformed geometry."""
|
||
if self.structured:
|
||
return VTK.from_image_data(self.cells,self.size,self.origin)
|
||
else:
|
||
with h5py.File(self.fname,'r') as f:
|
||
return VTK.from_unstructured_grid(f['/geometry/x_n'][()],
|
||
f['/geometry/T_c'][()]-1,
|
||
f['/geometry/T_c'].attrs['VTK_TYPE'] if h5py3 else \
|
||
f['/geometry/T_c'].attrs['VTK_TYPE'].decode())
|
||
|
||
|
||
@staticmethod
|
||
def _add_absolute(x: Dict[str, Any]) -> Dict[str, Any]:
|
||
return {
|
||
'data': np.abs(x['data']),
|
||
'label': f'|{x["label"]}|',
|
||
'meta': {
|
||
'unit': x['meta']['unit'],
|
||
'description': f"absolute value of {x['label']} ({x['meta']['description']})",
|
||
'creator': 'add_absolute'
|
||
}
|
||
}
|
||
def add_absolute(self, x: str):
|
||
"""
|
||
Add absolute value.
|
||
|
||
Parameters
|
||
----------
|
||
x : str
|
||
Name of scalar, vector, or tensor dataset to take absolute value of.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_absolute,{'x':x})
|
||
|
||
|
||
@staticmethod
|
||
def _add_calculation(**kwargs) -> Dict[str, Any]:
|
||
formula = kwargs['formula']
|
||
for d in re.findall(r'#(.*?)#',formula):
|
||
formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
|
||
data = eval(formula)
|
||
|
||
if not hasattr(data,'shape') or data.shape[0] != kwargs[d]['data'].shape[0]:
|
||
raise ValueError('"{}" results in invalid shape'.format(kwargs['formula']))
|
||
|
||
return {
|
||
'data': data,
|
||
'label': kwargs['label'],
|
||
'meta': {
|
||
'unit': kwargs['unit'],
|
||
'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
|
||
'creator': 'add_calculation'
|
||
}
|
||
}
|
||
def add_calculation(self,
|
||
formula: str,
|
||
name: str,
|
||
unit: str = 'n/a',
|
||
description: str = None):
|
||
"""
|
||
Add result of a general formula.
|
||
|
||
Parameters
|
||
----------
|
||
formula : str
|
||
Formula to calculate resulting dataset.
|
||
Existing datasets are referenced by '#TheirName#'.
|
||
name : str
|
||
Name of resulting dataset.
|
||
unit : str, optional
|
||
Physical unit of the result.
|
||
description : str, optional
|
||
Human-readable description of the result.
|
||
|
||
Examples
|
||
--------
|
||
Add total dislocation density, i.e. the sum of mobile dislocation
|
||
density 'rho_mob' and dislocation dipole density 'rho_dip' over
|
||
all slip systems:
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_calculation('np.sum(#rho_mob#,axis=1)','rho_mob_total',
|
||
... '1/m²','total mobile dislocation density')
|
||
>>> r.add_calculation('np.sum(#rho_dip#,axis=1)','rho_dip_total',
|
||
... '1/m²','total dislocation dipole density')
|
||
>>> r.add_calculation('#rho_dip_total#+#rho_mob_total','rho_total',
|
||
... '1/m²','total dislocation density')
|
||
|
||
Add Mises equivalent of the Cauchy stress without storage of
|
||
intermediate results. Define a user function for better readability:
|
||
|
||
>>> import damask
|
||
>>> def equivalent_stress(F,P):
|
||
... sigma = damask.mechanics.stress_Cauchy(F=F,P=P)
|
||
... return damask.mechanics.equivalent_stress_Mises(sigma)
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.enable_user_function(equivalent_stress)
|
||
>>> r.add_calculation('equivalent_stress(#F#,#P#)','sigma_vM','Pa',
|
||
... 'Mises equivalent of the Cauchy stress')
|
||
|
||
"""
|
||
dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
|
||
args = {'formula':formula,'label':name,'unit':unit,'description':description}
|
||
self._add_generic_pointwise(self._add_calculation,dataset_mapping,args)
|
||
|
||
|
||
@staticmethod
|
||
def _add_stress_Cauchy(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
|
||
return {
|
||
'data': mechanics.stress_Cauchy(P['data'],F['data']),
|
||
'label': 'sigma',
|
||
'meta': {
|
||
'unit': P['meta']['unit'],
|
||
'description': "Cauchy stress calculated "
|
||
f"from {P['label']} ({P['meta']['description']})"
|
||
f" and {F['label']} ({F['meta']['description']})",
|
||
'creator': 'add_stress_Cauchy'
|
||
}
|
||
}
|
||
def add_stress_Cauchy(self,
|
||
P: str = 'P',
|
||
F: str = 'F'):
|
||
"""
|
||
Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
P : str, optional
|
||
Name of the dataset containing the first Piola-Kirchhoff stress. Defaults to 'P'.
|
||
F : str, optional
|
||
Name of the dataset containing the deformation gradient. Defaults to 'F'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_stress_Cauchy,{'P':P,'F':F})
|
||
|
||
|
||
@staticmethod
|
||
def _add_determinant(T: Dict[str, Any]) -> Dict[str, Any]:
|
||
return {
|
||
'data': np.linalg.det(T['data']),
|
||
'label': f"det({T['label']})",
|
||
'meta': {
|
||
'unit': T['meta']['unit'],
|
||
'description': f"determinant of tensor {T['label']} ({T['meta']['description']})",
|
||
'creator': 'add_determinant'
|
||
}
|
||
}
|
||
def add_determinant(self, T: str):
|
||
"""
|
||
Add the determinant of a tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T : str
|
||
Name of tensor dataset.
|
||
|
||
Examples
|
||
--------
|
||
Add the determinant of plastic deformation gradient 'F_p':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_determinant('F_p')
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_determinant,{'T':T})
|
||
|
||
|
||
@staticmethod
|
||
def _add_deviator(T: Dict[str, Any]) -> Dict[str, Any]:
|
||
return {
|
||
'data': tensor.deviatoric(T['data']),
|
||
'label': f"s_{T['label']}",
|
||
'meta': {
|
||
'unit': T['meta']['unit'],
|
||
'description': f"deviator of tensor {T['label']} ({T['meta']['description']})",
|
||
'creator': 'add_deviator'
|
||
}
|
||
}
|
||
def add_deviator(self, T: str):
|
||
"""
|
||
Add the deviatoric part of a tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T : str
|
||
Name of tensor dataset.
|
||
|
||
Examples
|
||
--------
|
||
Add the deviatoric part of Cauchy stress 'sigma':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_deviator('sigma')
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_deviator,{'T':T})
|
||
|
||
|
||
@staticmethod
|
||
def _add_eigenvalue(T_sym: Dict[str, Any], eigenvalue: Literal['max, mid, min']) -> Dict[str, Any]:
|
||
if eigenvalue == 'max':
|
||
label,p = 'maximum',2
|
||
elif eigenvalue == 'mid':
|
||
label,p = 'intermediate',1
|
||
elif eigenvalue == 'min':
|
||
label,p = 'minimum',0
|
||
else:
|
||
raise ValueError(f'invalid eigenvalue: {eigenvalue}')
|
||
|
||
return {
|
||
'data': tensor.eigenvalues(T_sym['data'])[:,p],
|
||
'label': f"lambda_{eigenvalue}({T_sym['label']})",
|
||
'meta' : {
|
||
'unit': T_sym['meta']['unit'],
|
||
'description': f"{label} eigenvalue of {T_sym['label']} ({T_sym['meta']['description']})",
|
||
'creator': 'add_eigenvalue'
|
||
}
|
||
}
|
||
def add_eigenvalue(self,
|
||
T_sym: str,
|
||
eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
|
||
"""
|
||
Add eigenvalues of symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Name of symmetric tensor dataset.
|
||
eigenvalue : {'max', 'mid', 'min'}
|
||
Eigenvalue. Defaults to 'max'.
|
||
|
||
Examples
|
||
--------
|
||
Add the minimum eigenvalue of Cauchy stress 'sigma':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_eigenvalue('sigma','min')
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
||
|
||
|
||
@staticmethod
|
||
def _add_eigenvector(T_sym: Dict[str, Any], eigenvalue: Literal['max', 'mid', 'min']) -> Dict[str, Any]:
|
||
if eigenvalue == 'max':
|
||
label,p = 'maximum',2
|
||
elif eigenvalue == 'mid':
|
||
label,p = 'intermediate',1
|
||
elif eigenvalue == 'min':
|
||
label,p = 'minimum',0
|
||
else:
|
||
raise ValueError(f'invalid eigenvalue: {eigenvalue}')
|
||
|
||
return {
|
||
'data': tensor.eigenvectors(T_sym['data'])[:,p],
|
||
'label': f"v_{eigenvalue}({T_sym['label']})",
|
||
'meta' : {
|
||
'unit': '1',
|
||
'description': f"eigenvector corresponding to {label} eigenvalue"
|
||
f" of {T_sym['label']} ({T_sym['meta']['description']})",
|
||
'creator': 'add_eigenvector'
|
||
}
|
||
}
|
||
def add_eigenvector(self,
|
||
T_sym: str,
|
||
eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
|
||
"""
|
||
Add eigenvector of symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Name of symmetric tensor dataset.
|
||
eigenvalue : {'max', 'mid', 'min'}
|
||
Eigenvalue to which the eigenvector corresponds.
|
||
Defaults to 'max'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
||
|
||
|
||
@staticmethod
|
||
def _add_IPF_color(l: FloatSequence, q: Dict[str, Any]) -> Dict[str, Any]:
|
||
m = util.scale_to_coprime(np.array(l))
|
||
lattice = q['meta']['lattice']
|
||
o = Orientation(rotation = q['data'],lattice=lattice)
|
||
|
||
return {
|
||
'data': np.uint8(o.IPF_color(l)*255),
|
||
'label': 'IPFcolor_({} {} {})'.format(*m),
|
||
'meta' : {
|
||
'unit': '8-bit RGB',
|
||
'lattice': q['meta']['lattice'],
|
||
'description': 'Inverse Pole Figure (IPF) colors along sample direction ({} {} {})'.format(*m),
|
||
'creator': 'add_IPF_color'
|
||
}
|
||
}
|
||
def add_IPF_color(self,
|
||
l: FloatSequence,
|
||
q: str = 'O'):
|
||
"""
|
||
Add RGB color tuple of inverse pole figure (IPF) color.
|
||
|
||
Parameters
|
||
----------
|
||
l : numpy.array of shape (3)
|
||
Lab frame direction for inverse pole figure.
|
||
q : str
|
||
Name of the dataset containing the crystallographic orientation as quaternions.
|
||
Defaults to 'O'.
|
||
|
||
Examples
|
||
--------
|
||
Add the IPF color along [0,1,1] for orientation 'O':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_IPF_color(np.array([0,1,1]))
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_IPF_color,{'q':q},{'l':l})
|
||
|
||
|
||
@staticmethod
|
||
def _add_maximum_shear(T_sym: Dict[str, Any]) -> Dict[str, Any]:
|
||
return {
|
||
'data': mechanics.maximum_shear(T_sym['data']),
|
||
'label': f"max_shear({T_sym['label']})",
|
||
'meta': {
|
||
'unit': T_sym['meta']['unit'],
|
||
'description': f"maximum shear component of {T_sym['label']} ({T_sym['meta']['description']})",
|
||
'creator': 'add_maximum_shear'
|
||
}
|
||
}
|
||
def add_maximum_shear(self, T_sym: str):
|
||
"""
|
||
Add maximum shear components of symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Name of symmetric tensor dataset.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym})
|
||
|
||
|
||
@staticmethod
|
||
def _add_equivalent_Mises(T_sym: Dict[str, Any], kind: str) -> Dict[str, Any]:
|
||
k = kind
|
||
if k is None:
|
||
if T_sym['meta']['unit'] == '1':
|
||
k = 'strain'
|
||
elif T_sym['meta']['unit'] == 'Pa':
|
||
k = 'stress'
|
||
if k not in ['stress', 'strain']:
|
||
raise ValueError(f'invalid von Mises kind "{kind}"')
|
||
|
||
return {
|
||
'data': (mechanics.equivalent_strain_Mises if k=='strain' else \
|
||
mechanics.equivalent_stress_Mises)(T_sym['data']),
|
||
'label': f"{T_sym['label']}_vM",
|
||
'meta': {
|
||
'unit': T_sym['meta']['unit'],
|
||
'description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['description']})",
|
||
'creator': 'add_Mises'
|
||
}
|
||
}
|
||
def add_equivalent_Mises(self,
|
||
T_sym: str,
|
||
kind: str = None):
|
||
"""
|
||
Add the equivalent Mises stress or strain of a symmetric tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T_sym : str
|
||
Name of symmetric tensorial stress or strain dataset.
|
||
kind : {'stress', 'strain', None}, optional
|
||
Kind of the von Mises equivalent. Defaults to None, in which case
|
||
it is selected based on the unit of the dataset ('1' -> strain, 'Pa' -> stress).
|
||
|
||
Examples
|
||
--------
|
||
Add the Mises equivalent of the Cauchy stress 'sigma':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_equivalent_Mises('sigma')
|
||
|
||
Add the Mises equivalent of the spatial logarithmic strain 'epsilon_V^0.0(F)':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_equivalent_Mises('epsilon_V^0.0(F)')
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
|
||
|
||
|
||
@staticmethod
|
||
def _add_norm(x: Dict[str, Any], ord: Union[int, float, Literal['fro', 'nuc']]) -> Dict[str, Any]:
|
||
o = ord
|
||
if len(x['data'].shape) == 2:
|
||
axis: Union[int, Tuple[int, int]] = 1
|
||
t = 'vector'
|
||
if o is None: o = 2
|
||
elif len(x['data'].shape) == 3:
|
||
axis = (1,2)
|
||
t = 'tensor'
|
||
if o is None: o = 'fro'
|
||
else:
|
||
raise ValueError(f'invalid shape of {x["label"]}')
|
||
|
||
return {
|
||
'data': np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True),
|
||
'label': f"|{x['label']}|_{o}",
|
||
'meta': {
|
||
'unit': x['meta']['unit'],
|
||
'description': f"{o}-norm of {t} {x['label']} ({x['meta']['description']})",
|
||
'creator': 'add_norm'
|
||
}
|
||
}
|
||
def add_norm(self,
|
||
x: str,
|
||
ord: Union[int, float, Literal['fro', 'nuc']] = None):
|
||
"""
|
||
Add the norm of vector or tensor.
|
||
|
||
Parameters
|
||
----------
|
||
x : str
|
||
Name of vector or tensor dataset.
|
||
ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
|
||
Order of the norm. inf means NumPy’s inf object. For details refer to numpy.linalg.norm.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_norm,{'x':x},{'ord':ord})
|
||
|
||
|
||
@staticmethod
|
||
def _add_stress_second_Piola_Kirchhoff(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
|
||
return {
|
||
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
|
||
'label': 'S',
|
||
'meta': {
|
||
'unit': P['meta']['unit'],
|
||
'description': "second Piola-Kirchhoff stress calculated "
|
||
f"from {P['label']} ({P['meta']['description']})"
|
||
f" and {F['label']} ({F['meta']['description']})",
|
||
'creator': 'add_stress_second_Piola_Kirchhoff'
|
||
}
|
||
}
|
||
def add_stress_second_Piola_Kirchhoff(self,
|
||
P: str = 'P',
|
||
F: str = 'F'):
|
||
"""
|
||
Add second Piola-Kirchhoff stress calculated from first Piola-Kirchhoff stress and deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
P : str, optional
|
||
Name of first Piola-Kirchhoff stress dataset. Defaults to 'P'.
|
||
F : str, optional
|
||
Name of deformation gradient dataset. Defaults to 'F'.
|
||
|
||
Notes
|
||
-----
|
||
The definition of the second Piola-Kirchhoff stress (S = [F^-1 P]_sym)
|
||
follows the standard definition in nonlinear continuum mechanics.
|
||
As such, no intermediate configuration, for instance that reached by F_p,
|
||
is taken into account.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_stress_second_Piola_Kirchhoff,{'P':P,'F':F})
|
||
|
||
|
||
|
||
@staticmethod
|
||
def _add_pole(q: Dict[str, Any],
|
||
uvw: FloatSequence,
|
||
hkl: FloatSequence,
|
||
with_symmetry: bool,
|
||
normalize: bool) -> Dict[str, Any]:
|
||
c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1
|
||
brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2]
|
||
label = 'p^' + '{}{} {} {}{}'.format(brackets[0],
|
||
*(uvw if uvw else hkl),
|
||
brackets[-1],)
|
||
ori = Orientation(q['data'],lattice=q['meta']['lattice'],a=1,c=c)
|
||
|
||
return {
|
||
'data': ori.to_pole(uvw=uvw,hkl=hkl,with_symmetry=with_symmetry,normalize=normalize),
|
||
'label': label,
|
||
'meta' : {
|
||
'unit': '1',
|
||
'description': f'{"normalized " if normalize else ""}lab frame vector along lattice ' \
|
||
+ ('direction' if uvw is not None else 'plane') \
|
||
+ ('s' if with_symmetry else ''),
|
||
'creator': 'add_pole'
|
||
}
|
||
}
|
||
def add_pole(self,
|
||
q: str = 'O',
|
||
*,
|
||
uvw: FloatSequence = None,
|
||
hkl: FloatSequence = None,
|
||
with_symmetry: bool = False,
|
||
normalize: bool = True):
|
||
"""
|
||
Add lab frame vector along lattice direction [uvw] or plane normal (hkl).
|
||
|
||
Parameters
|
||
----------
|
||
q : str
|
||
Name of the dataset containing the crystallographic orientation as quaternions.
|
||
Defaults to 'O'.
|
||
uvw|hkl : numpy.ndarray of shape (3)
|
||
Miller indices of crystallographic direction or plane normal.
|
||
with_symmetry : bool, optional
|
||
Calculate all N symmetrically equivalent vectors.
|
||
Defaults to True.
|
||
normalize : bool, optional
|
||
Normalize output vector.
|
||
Defaults to True.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_pole,
|
||
{'q':q},
|
||
{'uvw':uvw,'hkl':hkl,'with_symmetry':with_symmetry,'normalize':normalize})
|
||
|
||
|
||
@staticmethod
|
||
def _add_rotation(F: Dict[str, Any]) -> Dict[str, Any]:
|
||
return {
|
||
'data': mechanics.rotation(F['data']).as_matrix(),
|
||
'label': f"R({F['label']})",
|
||
'meta': {
|
||
'unit': F['meta']['unit'],
|
||
'description': f"rotational part of {F['label']} ({F['meta']['description']})",
|
||
'creator': 'add_rotation'
|
||
}
|
||
}
|
||
def add_rotation(self, F: str):
|
||
"""
|
||
Add rotational part of a deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
F : str
|
||
Name of deformation gradient dataset.
|
||
|
||
Examples
|
||
--------
|
||
Add the rotational part of deformation gradient 'F':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_rotation('F')
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_rotation,{'F':F})
|
||
|
||
|
||
@staticmethod
|
||
def _add_spherical(T: Dict[str, Any]) -> Dict[str, Any]:
|
||
return {
|
||
'data': tensor.spherical(T['data'],False),
|
||
'label': f"p_{T['label']}",
|
||
'meta': {
|
||
'unit': T['meta']['unit'],
|
||
'description': f"spherical component of tensor {T['label']} ({T['meta']['description']})",
|
||
'creator': 'add_spherical'
|
||
}
|
||
}
|
||
def add_spherical(self, T: str):
|
||
"""
|
||
Add the spherical (hydrostatic) part of a tensor.
|
||
|
||
Parameters
|
||
----------
|
||
T : str
|
||
Name of tensor dataset.
|
||
|
||
Examples
|
||
--------
|
||
Add the hydrostatic part of the Cauchy stress 'sigma':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_spherical('sigma')
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_spherical,{'T':T})
|
||
|
||
|
||
@staticmethod
|
||
def _add_strain(F: Dict[str, Any], t: Literal['V', 'U'], m: float) -> Dict[str, Any]:
|
||
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: str = 'F',
|
||
t: Literal['V', 'U'] = 'V',
|
||
m: float = 0.0):
|
||
"""
|
||
Add strain tensor of a deformation gradient.
|
||
|
||
For details, see damask.mechanics.strain.
|
||
|
||
Parameters
|
||
----------
|
||
F : str, optional
|
||
Name of deformation gradient dataset. Defaults to 'F'.
|
||
t : {'V', 'U'}, optional
|
||
Type of the polar decomposition, 'V' for left stretch tensor and 'U' for right stretch tensor.
|
||
Defaults to 'V'.
|
||
m : float, optional
|
||
Order of the strain calculation. Defaults to 0.0.
|
||
|
||
Examples
|
||
--------
|
||
Add the Biot strain based on the deformation gradient 'F':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_strain(t='U',m=0.5)
|
||
|
||
Add the plastic Euler-Almansi strain based on the
|
||
plastic deformation gradient 'F_p':
|
||
|
||
>>> import damask
|
||
>>> r = damask.Result('my_file.hdf5')
|
||
>>> r.add_strain('F_p','V',-1)
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_strain,{'F':F},{'t':t,'m':m})
|
||
|
||
|
||
@staticmethod
|
||
def _add_stretch_tensor(F: Dict[str, Any], t: str) -> Dict[str, Any]:
|
||
return {
|
||
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
|
||
'label': f"{t}({F['label']})",
|
||
'meta': {
|
||
'unit': F['meta']['unit'],
|
||
'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
|
||
+f"of {F['label']} ({F['meta']['description']})", # noqa
|
||
'creator': 'add_stretch_tensor'
|
||
}
|
||
}
|
||
def add_stretch_tensor(self,
|
||
F: str = 'F',
|
||
t: Literal['V', 'U'] = 'V'):
|
||
"""
|
||
Add stretch tensor of a deformation gradient.
|
||
|
||
Parameters
|
||
----------
|
||
F : str, optional
|
||
Name of deformation gradient dataset. Defaults to 'F'.
|
||
t : {'V', 'U'}, optional
|
||
Type of the polar decomposition, 'V' for left stretch tensor and 'U' for right stretch tensor.
|
||
Defaults to 'V'.
|
||
|
||
"""
|
||
self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
|
||
|
||
|
||
@staticmethod
|
||
def _add_curl(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||
return {
|
||
'data': grid_filters.curl(size,f['data']),
|
||
'label': f"curl({f['label']})",
|
||
'meta': {
|
||
'unit': f['meta']['unit']+'/m',
|
||
'description': f"curl of {f['label']} ({f['meta']['description']})",
|
||
'creator': 'add_curl'
|
||
}
|
||
}
|
||
def add_curl(self, f: str):
|
||
"""
|
||
Add curl of a field.
|
||
|
||
Parameters
|
||
----------
|
||
f : str
|
||
Name of vector or tensor field dataset.
|
||
|
||
Notes
|
||
-----
|
||
This function is only available for structured grids,
|
||
i.e. results from the grid solver.
|
||
|
||
"""
|
||
self._add_generic_grid(self._add_curl,{'f':f},{'size':self.size})
|
||
|
||
|
||
@staticmethod
|
||
def _add_divergence(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||
return {
|
||
'data': grid_filters.divergence(size,f['data']),
|
||
'label': f"divergence({f['label']})",
|
||
'meta': {
|
||
'unit': f['meta']['unit']+'/m',
|
||
'description': f"divergence of {f['label']} ({f['meta']['description']})",
|
||
'creator': 'add_divergence'
|
||
}
|
||
}
|
||
def add_divergence(self, f: str):
|
||
"""
|
||
Add divergence of a field.
|
||
|
||
Parameters
|
||
----------
|
||
f : str
|
||
Name of vector or tensor field dataset.
|
||
|
||
Notes
|
||
-----
|
||
This function is only available for structured grids,
|
||
i.e. results from the grid solver.
|
||
|
||
"""
|
||
self._add_generic_grid(self._add_divergence,{'f':f},{'size':self.size})
|
||
|
||
|
||
@staticmethod
|
||
def _add_gradient(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||
return {
|
||
'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
|
||
f['data'].reshape(f['data'].shape+(1,))),
|
||
'label': f"gradient({f['label']})",
|
||
'meta': {
|
||
'unit': f['meta']['unit']+'/m',
|
||
'description': f"gradient of {f['label']} ({f['meta']['description']})",
|
||
'creator': 'add_gradient'
|
||
}
|
||
}
|
||
def add_gradient(self, f: str):
|
||
"""
|
||
Add gradient of a field.
|
||
|
||
Parameters
|
||
----------
|
||
f : str
|
||
Name of scalar or vector field dataset.
|
||
|
||
Notes
|
||
-----
|
||
This function is only available for structured grids,
|
||
i.e. results from the grid solver.
|
||
|
||
"""
|
||
self._add_generic_grid(self._add_gradient,{'f':f},{'size':self.size})
|
||
|
||
|
||
def _add_generic_grid(self,
|
||
func: Callable,
|
||
datasets: Dict[str, str],
|
||
args: Dict[str, str] = {},
|
||
constituents = None):
|
||
"""
|
||
General function to add data on a regular grid.
|
||
|
||
Parameters
|
||
----------
|
||
func : function
|
||
Callback function that calculates a new dataset from one or
|
||
more datasets per HDF5 group.
|
||
datasets : dictionary
|
||
Details of the datasets to be used:
|
||
{arg (name to which the data is passed in func): label (in HDF5 file)}.
|
||
args : dictionary, optional
|
||
Arguments parsed to func.
|
||
|
||
"""
|
||
if len(datasets) != 1 or self.N_constituents != 1:
|
||
raise NotImplementedError
|
||
|
||
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
|
||
|
||
increments = self.place(list(datasets.values()),False)
|
||
if not increments: raise RuntimeError("received invalid dataset")
|
||
with h5py.File(self.fname, 'a') as f:
|
||
for increment in increments.items():
|
||
for ty in increment[1].items():
|
||
for field in ty[1].items():
|
||
d: np.ma.MaskedArray = list(field[1].values())[0]
|
||
if np.any(d.mask): continue
|
||
dataset = {'f':{'data':np.reshape(d.data,tuple(self.cells)+d.data.shape[1:]),
|
||
'label':list(datasets.values())[0],
|
||
'meta':d.data.dtype.metadata}}
|
||
r = func(**dataset,**args)
|
||
result = r['data'].reshape((-1,)+r['data'].shape[3:])
|
||
for x in self.visible[ty[0]+'s']:
|
||
if ty[0] == 'phase':
|
||
result1 = result[at_cell_ph[0][x]]
|
||
if ty[0] == 'homogenization':
|
||
result1 = result[at_cell_ho[x]]
|
||
|
||
path = '/'.join(['/',increment[0],ty[0],x,field[0]])
|
||
h5_dataset = f[path].create_dataset(r['label'],data=result1)
|
||
|
||
now = datetime.datetime.now().astimezone()
|
||
h5_dataset.attrs['created'] = now.strftime('%Y-%m-%d %H:%M:%S%z') if h5py3 else \
|
||
now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
|
||
|
||
for l,v in r['meta'].items():
|
||
h5_dataset.attrs[l.lower()]=v if h5py3 else v.encode()
|
||
creator = h5_dataset.attrs['creator'] if h5py3 else \
|
||
h5_dataset.attrs['creator'].decode()
|
||
h5_dataset.attrs['creator'] = f'damask.Result.{creator} v{damask.version}' if h5py3 else \
|
||
f'damask.Result.{creator} v{damask.version}'.encode()
|
||
|
||
|
||
def _job_pointwise(self,
|
||
group: str,
|
||
callback: Callable,
|
||
datasets: Dict[str, str],
|
||
args: Dict[str, str],
|
||
lock: Lock) -> List[Union[None, Any]]:
|
||
"""Execute job for _add_generic_pointwise."""
|
||
try:
|
||
datasets_in = {}
|
||
lock.acquire()
|
||
with h5py.File(self.fname,'r') as f:
|
||
for arg,label in datasets.items():
|
||
loc = f[group+'/'+label]
|
||
datasets_in[arg]={'data' :loc[()],
|
||
'label':label,
|
||
'meta': {k:(v.decode() if not h5py3 and type(v) is bytes else v) \
|
||
for k,v in loc.attrs.items()}}
|
||
lock.release()
|
||
r = callback(**datasets_in,**args)
|
||
return [group,r]
|
||
except Exception as err:
|
||
print(f'Error during calculation: {err}.')
|
||
return [None,None]
|
||
|
||
|
||
def _add_generic_pointwise(self,
|
||
func: Callable,
|
||
datasets: Dict[str, Any],
|
||
args: Dict[str, Any] = {}):
|
||
"""
|
||
General function to add pointwise data.
|
||
|
||
Parameters
|
||
----------
|
||
callback : function
|
||
Callback function that calculates a new dataset from one or
|
||
more datasets per HDF5 group.
|
||
datasets : dictionary
|
||
Details of the datasets to be used:
|
||
{arg (name to which the data is passed in func): label (in HDF5 file)}.
|
||
args : dictionary, optional
|
||
Arguments parsed to func.
|
||
|
||
"""
|
||
pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',4)))
|
||
lock = mp.Manager().Lock()
|
||
|
||
groups = []
|
||
with h5py.File(self.fname,'r') as f:
|
||
for inc in self.visible['increments']:
|
||
for ty in ['phase','homogenization']:
|
||
for label in self.visible[ty+'s']:
|
||
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
|
||
group = '/'.join([inc,ty,label,field])
|
||
if set(datasets.values()).issubset(f[group].keys()): groups.append(group)
|
||
|
||
if len(groups) == 0:
|
||
print('No matching dataset found, no data was added.')
|
||
return
|
||
|
||
default_arg = partial(self._job_pointwise,callback=func,datasets=datasets,args=args,lock=lock)
|
||
|
||
for group,result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):# type: ignore
|
||
if not result:
|
||
continue
|
||
lock.acquire()
|
||
with h5py.File(self.fname, 'a') as f:
|
||
try:
|
||
if not self._protected and '/'.join([group,result['label']]) in f:
|
||
dataset = f['/'.join([group,result['label']])]
|
||
dataset[...] = result['data']
|
||
dataset.attrs['overwritten'] = True
|
||
else:
|
||
shape = result['data'].shape
|
||
if compress := (result['data'].size >= chunk_size*2):
|
||
chunks = (chunk_size//np.prod(shape[1:]),)+shape[1:]
|
||
else:
|
||
chunks = shape
|
||
dataset = f[group].create_dataset(result['label'],data=result['data'],
|
||
maxshape=shape, chunks=chunks,
|
||
compression = 'gzip' if compress else None,
|
||
compression_opts = 6 if compress else None,
|
||
shuffle=True,fletcher32=True)
|
||
|
||
now = datetime.datetime.now().astimezone()
|
||
dataset.attrs['created'] = now.strftime('%Y-%m-%d %H:%M:%S%z') if h5py3 else \
|
||
now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
|
||
|
||
for l,v in result['meta'].items():
|
||
dataset.attrs[l.lower()]=v.encode() if not h5py3 and type(v) is str else v
|
||
creator = dataset.attrs['creator'] if h5py3 else \
|
||
dataset.attrs['creator'].decode()
|
||
dataset.attrs['creator'] = f'damask.Result.{creator} v{damask.version}' if h5py3 else \
|
||
f'damask.Result.{creator} v{damask.version}'.encode()
|
||
|
||
except (OSError,RuntimeError) as err:
|
||
print(f'Could not add dataset: {err}.')
|
||
lock.release()
|
||
|
||
pool.close()
|
||
pool.join()
|
||
|
||
|
||
def export_XDMF(self,
|
||
output: Union[str, List[str]] = '*'):
|
||
"""
|
||
Write XDMF file to directly visualize data from DADF5 file.
|
||
|
||
The XDMF format is only supported for structured grids
|
||
with single phase and single constituent.
|
||
For other cases use `export_VTK`.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str
|
||
Names of the datasets included in the XDMF file.
|
||
Defaults to '*', in which case all datasets are considered.
|
||
|
||
"""
|
||
if self.N_constituents != 1 or len(self.phases) != 1 or not self.structured:
|
||
raise TypeError('XDMF output requires structured grid with single phase and single constituent.')
|
||
|
||
|
||
attribute_type_map = defaultdict(lambda:'Matrix', ( ((),'Scalar'), ((3,),'Vector'), ((3,3),'Tensor')) )
|
||
|
||
def number_type_map(dtype):
|
||
if dtype in np.sctypes['int']: return 'Int'
|
||
if dtype in np.sctypes['uint']: return 'UInt'
|
||
if dtype in np.sctypes['float']: return 'Float'
|
||
|
||
|
||
xdmf = ET.Element('Xdmf')
|
||
xdmf.attrib={'Version': '2.0',
|
||
'xmlns:xi': 'http://www.w3.org/2001/XInclude'}
|
||
|
||
domain = ET.SubElement(xdmf, 'Domain')
|
||
|
||
collection = ET.SubElement(domain, 'Grid')
|
||
collection.attrib={'GridType': 'Collection',
|
||
'CollectionType': 'Temporal',
|
||
'Name': 'Increments'}
|
||
|
||
time = ET.SubElement(collection, 'Time')
|
||
time.attrib={'TimeType': 'List'}
|
||
|
||
time_data = ET.SubElement(time, 'DataItem')
|
||
times = [self.times[self.increments.index(i)] for i in self.visible['increments']]
|
||
time_data.attrib={'Format': 'XML',
|
||
'NumberType': 'Float',
|
||
'Dimensions': f'{len(times)}'}
|
||
time_data.text = ' '.join(map(str,times))
|
||
|
||
attributes = []
|
||
data_items = []
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
for inc in self.visible['increments']:
|
||
|
||
grid = ET.SubElement(collection,'Grid')
|
||
grid.attrib = {'GridType': 'Uniform',
|
||
'Name': inc}
|
||
|
||
topology = ET.SubElement(grid, 'Topology')
|
||
topology.attrib = {'TopologyType': '3DCoRectMesh',
|
||
'Dimensions': '{} {} {}'.format(*(self.cells[::-1]+1))}
|
||
|
||
geometry = ET.SubElement(grid, 'Geometry')
|
||
geometry.attrib = {'GeometryType':'Origin_DxDyDz'}
|
||
|
||
origin = ET.SubElement(geometry, 'DataItem')
|
||
origin.attrib = {'Format': 'XML',
|
||
'NumberType': 'Float',
|
||
'Dimensions': '3'}
|
||
origin.text = "{} {} {}".format(*self.origin[::-1])
|
||
|
||
delta = ET.SubElement(geometry, 'DataItem')
|
||
delta.attrib = {'Format': 'XML',
|
||
'NumberType': 'Float',
|
||
'Dimensions': '3'}
|
||
delta.text="{} {} {}".format(*(self.size/self.cells)[::-1])
|
||
|
||
attributes.append(ET.SubElement(grid, 'Attribute'))
|
||
attributes[-1].attrib = {'Name': 'u / m',
|
||
'Center': 'Node',
|
||
'AttributeType': 'Vector'}
|
||
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
|
||
data_items[-1].attrib = {'Format': 'HDF',
|
||
'Precision': '8',
|
||
'Dimensions': '{} {} {} 3'.format(*(self.cells[::-1]+1))}
|
||
data_items[-1].text = f'{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 _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
|
||
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
|
||
name = '/'.join([inc,ty,label,field,out])
|
||
shape = f[name].shape[1:]
|
||
dtype = f[name].dtype
|
||
|
||
unit = f[name].attrs['unit'] if h5py3 else \
|
||
f[name].attrs['unit'].decode()
|
||
|
||
attributes.append(ET.SubElement(grid, 'Attribute'))
|
||
attributes[-1].attrib = {'Name': '/'.join([ty,field,out])+f' / {unit}',
|
||
'Center': 'Cell',
|
||
'AttributeType': attribute_type_map[shape]}
|
||
data_items.append(ET.SubElement(attributes[-1], 'DataItem'))
|
||
data_items[-1].attrib = {'Format': 'HDF',
|
||
'NumberType': number_type_map(dtype),
|
||
'Precision': f'{dtype.itemsize}',
|
||
'Dimensions': '{} {} {} {}'.format(*self.cells[::-1],1 if shape == () else
|
||
np.prod(shape))}
|
||
data_items[-1].text = f'{os.path.split(self.fname)[1]}:{name}'
|
||
|
||
with util.open_text(self.fname.with_suffix('.xdmf').name,'w') as f:
|
||
f.write(xml.dom.minidom.parseString(ET.tostring(xdmf).decode()).toprettyxml())
|
||
|
||
|
||
def _mappings(self):
|
||
"""Mappings to place data spatially."""
|
||
with h5py.File(self.fname,'r') as f:
|
||
|
||
at_cell_ph = []
|
||
in_data_ph = []
|
||
for c in range(self.N_constituents):
|
||
at_cell_ph.append({label: np.where(self.phase[:,c] == label)[0] \
|
||
for label in self.visible['phases']})
|
||
in_data_ph.append({label: f['/'.join(['cell_to','phase'])]['entry'][at_cell_ph[c][label]][:,c] \
|
||
for label in self.visible['phases']})
|
||
|
||
at_cell_ho = {label: np.where(self.homogenization[:] == label)[0] \
|
||
for label in self.visible['homogenizations']}
|
||
in_data_ho = {label: f['/'.join(['cell_to','homogenization'])]['entry'][at_cell_ho[label]] \
|
||
for label in self.visible['homogenizations']}
|
||
|
||
return at_cell_ph,in_data_ph,at_cell_ho,in_data_ho
|
||
|
||
|
||
def export_VTK(self,
|
||
output: Union[str,list] = '*',
|
||
mode: str = 'cell',
|
||
constituents: IntSequence = None,
|
||
fill_float: float = np.nan,
|
||
fill_int: int = 0,
|
||
parallel: bool = True):
|
||
"""
|
||
Export to VTK cell/point data.
|
||
|
||
One VTK file per visible increment is created.
|
||
For point data, the VTK format is poly data (.vtp).
|
||
For cell data, either an image (.vti) or unstructured (.vtu) dataset
|
||
is written for grid-based or mesh-based simulations, respectively.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str, optional
|
||
Names of the datasets to export to the VTK file.
|
||
Defaults to '*', in which case all 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 VTK files in parallel in a separate background process.
|
||
Defaults to True.
|
||
|
||
"""
|
||
if mode.lower()=='cell':
|
||
v = self.geometry0
|
||
elif mode.lower()=='point':
|
||
v = VTK.from_poly_data(self.coordinates0_point)
|
||
else:
|
||
raise ValueError(f'invalid mode "{mode}"')
|
||
|
||
v.comments = [util.execution_stamp('Result','export_VTK')]
|
||
|
||
N_digits = int(np.floor(np.log10(max(1,self.incs[-1]))))+1
|
||
|
||
constituents_ = constituents if isinstance(constituents,Iterable) else \
|
||
(range(self.N_constituents) if constituents is None else [constituents]) # type: ignore
|
||
|
||
suffixes = [''] if self.N_constituents == 1 or isinstance(constituents,int) else \
|
||
[f'#{c}' for c in constituents_]
|
||
|
||
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
if self.version_minor >= 13:
|
||
creator = f.attrs['creator'] if h5py3 else f.attrs['creator'].decode()
|
||
created = f.attrs['created'] if h5py3 else f.attrs['created'].decode()
|
||
v.comments += f'{creator} ({created})'
|
||
|
||
for inc in util.show_progress(self.visible['increments']):
|
||
|
||
u = _read(f['/'.join([inc,'geometry','u_n' if mode.lower() == 'cell' else 'u_p'])])
|
||
v = v.set('u',u)
|
||
|
||
for ty in ['phase','homogenization']:
|
||
for field in self.visible['fields']:
|
||
outs: Dict[str, np.ma.core.MaskedArray] = {}
|
||
for label in self.visible[ty+'s']:
|
||
if field not in f['/'.join([inc,ty,label])].keys(): continue
|
||
|
||
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
|
||
data = ma.array(_read(f['/'.join([inc,ty,label,field,out])]))
|
||
|
||
if ty == 'phase':
|
||
if out+suffixes[0] not in outs.keys():
|
||
for c,suffix in zip(constituents_,suffixes):
|
||
outs[out+suffix] = \
|
||
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
|
||
|
||
for c,suffix in zip(constituents_,suffixes):
|
||
outs[out+suffix][at_cell_ph[c][label]] = data[in_data_ph[c][label]]
|
||
|
||
if ty == 'homogenization':
|
||
if out not in outs.keys():
|
||
outs[out] = _empty_like(data,self.N_materialpoints,fill_float,fill_int)
|
||
|
||
outs[out][at_cell_ho[label]] = data[in_data_ho[label]]
|
||
|
||
for label,dataset in outs.items():
|
||
v = v.set(' / '.join(['/'.join([ty,field,label]),dataset.dtype.metadata['unit']]),dataset)
|
||
|
||
v.save(f'{self.fname.stem}_inc{inc[10:].zfill(N_digits)}',parallel=parallel)
|
||
|
||
|
||
def get(self,
|
||
output: Union[str, List[str]] = '*',
|
||
flatten: bool = True,
|
||
prune: bool = True):
|
||
"""
|
||
Collect data per phase/homogenization reflecting the group/folder structure in the DADF5 file.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str
|
||
Names 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 = {} # type: ignore
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
for inc in util.show_progress(self.visible['increments']):
|
||
r[inc] = {'phase':{},'homogenization':{},'geometry':{}}
|
||
|
||
for out in _match(output,f['/'.join([inc,'geometry'])].keys()):
|
||
r[inc]['geometry'][out] = _read(f['/'.join([inc,'geometry',out])])
|
||
|
||
for ty in ['phase','homogenization']:
|
||
for label in self.visible[ty+'s']:
|
||
r[inc][ty][label] = {}
|
||
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
|
||
r[inc][ty][label][field] = {}
|
||
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
|
||
r[inc][ty][label][field][out] = _read(f['/'.join([inc,ty,label,field,out])])
|
||
|
||
if prune: r = util.dict_prune(r)
|
||
if flatten: r = util.dict_flatten(r)
|
||
|
||
return None if (type(r) == dict and r == {}) else r
|
||
|
||
|
||
def place(self,
|
||
output: Union[str, List[str]] = '*',
|
||
flatten: bool = True,
|
||
prune: bool = True,
|
||
constituents: IntSequence = None,
|
||
fill_float: float = np.nan,
|
||
fill_int: 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 `get` if only one phase/homogenization
|
||
and one constituent is present.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str, optional
|
||
Names of the datasets to read.
|
||
Defaults to '*', in which case all 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 = {} # type: ignore
|
||
|
||
constituents_ = list(map(int,constituents)) if isinstance(constituents,Iterable) else \
|
||
(range(self.N_constituents) if constituents is None else [constituents]) # type: ignore
|
||
|
||
suffixes = [''] if self.N_constituents == 1 or isinstance(constituents,int) else \
|
||
[f'#{c}' for c in constituents_]
|
||
|
||
at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
|
||
|
||
with h5py.File(self.fname,'r') as f:
|
||
|
||
for inc in util.show_progress(self.visible['increments']):
|
||
r[inc] = {'phase':{},'homogenization':{},'geometry':{}}
|
||
|
||
for out in _match(output,f['/'.join([inc,'geometry'])].keys()):
|
||
r[inc]['geometry'][out] = ma.array(_read(f['/'.join([inc,'geometry',out])]),fill_value = fill_float)
|
||
|
||
for ty in ['phase','homogenization']:
|
||
for label in self.visible[ty+'s']:
|
||
for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()):
|
||
if field not in r[inc][ty].keys():
|
||
r[inc][ty][field] = {}
|
||
|
||
for out in _match(output,f['/'.join([inc,ty,label,field])].keys()):
|
||
data = ma.array(_read(f['/'.join([inc,ty,label,field,out])]))
|
||
|
||
if ty == 'phase':
|
||
if out+suffixes[0] not in r[inc][ty][field].keys():
|
||
for c,suffix in zip(constituents_,suffixes):
|
||
r[inc][ty][field][out+suffix] = \
|
||
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
|
||
|
||
for c,suffix in zip(constituents_,suffixes):
|
||
r[inc][ty][field][out+suffix][at_cell_ph[c][label]] = data[in_data_ph[c][label]]
|
||
|
||
if ty == 'homogenization':
|
||
if out not in r[inc][ty][field].keys():
|
||
r[inc][ty][field][out] = \
|
||
_empty_like(data,self.N_materialpoints,fill_float,fill_int)
|
||
|
||
r[inc][ty][field][out][at_cell_ho[label]] = data[in_data_ho[label]]
|
||
|
||
if prune: r = util.dict_prune(r)
|
||
if flatten: r = util.dict_flatten(r)
|
||
|
||
return None if (type(r) == dict and r == {}) else r
|
||
|
||
|
||
def export_setup(self,
|
||
output: Union[str, List[str]] = '*',
|
||
overwrite: bool = False):
|
||
"""
|
||
Export configuration files.
|
||
|
||
Parameters
|
||
----------
|
||
output : (list of) str, optional
|
||
Names of the datasets to export to the file.
|
||
Defaults to '*', in which case all datasets are exported.
|
||
overwrite : bool, optional
|
||
Overwrite existing configuration files.
|
||
Defaults to False.
|
||
|
||
"""
|
||
def export(name: str, obj: Union[h5py.Dataset,h5py.Group], output: Union[str,List[str]], overwrite: bool):
|
||
if type(obj) == h5py.Dataset and _match(output,[name]):
|
||
d = obj.attrs['description'] if h5py3 else obj.attrs['description'].decode()
|
||
if not Path(name).exists() or overwrite:
|
||
with util.open_text(name,'w') as f_out: f_out.write(obj[0].decode())
|
||
print(f'Exported {d} to "{name}".')
|
||
else:
|
||
print(f'"{name}" exists, {d} not exported.')
|
||
elif type(obj) == h5py.Group:
|
||
os.makedirs(name, exist_ok=True)
|
||
|
||
with h5py.File(self.fname,'r') as f_in:
|
||
f_in['setup'].visititems(partial(export,output=output,overwrite=overwrite))
|