2060 lines
83 KiB
Python
2060 lines
83 KiB
Python
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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import functools
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from pathlib import Path
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from collections import defaultdict
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from collections.abc import Iterable
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from typing import Optional, 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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from numpy import 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 (DAMASK HDF5) file.
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A DADF5 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 DADF5 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 14 <= self.version_minor <= 14) and self.version_major != 1:
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raise TypeError(f'unsupported DADF5 version "{self.version_major}.{self.version_minor}"')
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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 = np.around([f[i].attrs['t/s'] for i in self.increments],12)
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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[None, int, Sequence[int], str, Sequence[str], bool] = None,
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times: Union[None, float, Sequence[float], str, Sequence[str], bool] = None,
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phases: Union[None, str, Sequence[str], bool] = None,
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homogenizations: Union[None, str, Sequence[str], bool] = None,
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fields: Union[None, str, Sequence[str], bool] = None) -> "Result":
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"""
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Manage 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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atol = 1e-2 * np.min(np.diff(self.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 = np.array(choice).astype(float)
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choice = []
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for c in iterator:
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idx = np.searchsorted(self.times,c,side='left')
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if idx<len(self.times) and np.isclose(c,self.times[idx],rtol=0,atol=atol):
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choice.append(self.increments[idx])
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elif idx>0 and np.isclose(c,self.times[idx-1],rtol=0,atol=atol):
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choice.append(self.increments[idx-1])
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valid = _match(choice,getattr(self,what))
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existing = set(self.visible[what])
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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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dup.visible[what] = sorted(existing.union(valid), key=util.natural_sort)
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elif action == 'del':
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dup.visible[what] = sorted(existing.difference(valid), 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[None, str, int] = None,
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end: Union[None, 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.split(prefix_inc)[-1] 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: Optional[float] = None,
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end: Optional[float] = None) -> Sequence[float]:
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"""
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Get times of 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 time of first.
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end : float, optional
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Time of end increment. Defaults to time of 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[None, int, Sequence[int], str, Sequence[str], bool] = None,
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times: Union[None, float, Sequence[float], str, Sequence[str], bool] = None,
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phases: Union[None, str, Sequence[str], bool] = None,
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homogenizations: Union[None, str, Sequence[str], bool] = None,
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fields: Union[None, str, Sequence[str], bool] = None,
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protected: Optional[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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Numbers of increments to select.
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times: (list of) float, (list of) str, or bool, optional.
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Simulation times of increments to select.
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phases: (list of) str, or bool, optional.
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Names of phases to select.
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homogenizations: (list of) str, or bool, optional.
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Names of homogenizations to select.
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fields: (list of) str, or bool, optional.
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Names 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[None, int, Sequence[int], str, Sequence[str], bool] = None,
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times: Union[None, float, Sequence[float], str, Sequence[str], bool] = None,
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phases: Union[None, str, Sequence[str], bool] = None,
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homogenizations: Union[None, str, Sequence[str], bool] = None,
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fields: Union[None, 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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Numbers of increments to select.
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times: (list of) float, (list of) str, or bool, optional.
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Simulation times of increments to select.
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phases: (list of) str, or bool, optional.
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Names of phases to select.
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homogenizations: (list of) str, or bool, optional.
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Names of homogenizations to select.
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fields: (list of) str, or bool, optional.
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Names 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[None, int, Sequence[int], str, Sequence[str], bool] = None,
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times: Union[None, float, Sequence[float], str, Sequence[str], bool] = None,
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phases: Union[None, str, Sequence[str], bool] = None,
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homogenizations: Union[None, str, Sequence[str], bool] = None,
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fields: Union[None, 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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Numbers of increments to select.
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times: (list of) float, (list of) str, or bool, optional.
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Simulation times of increments to select.
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phases: (list of) str, or bool, optional.
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Names of phases to select.
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homogenizations: (list of) str, or bool, optional.
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Names of homogenizations to select.
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fields: (list of) str, or bool, optional.
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Names 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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"""
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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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|
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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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|
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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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|
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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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|
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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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"""
|
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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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|
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|
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def remove(self, name: str):
|
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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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|
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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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|
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Examples
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--------
|
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Delete the deformation gradient 'F':
|
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|
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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.remove('F')
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|
|
|
"""
|
|
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 simulation_setup_files(self):
|
|
"""Simulation setup files used to generate the Result object."""
|
|
files = []
|
|
with h5py.File(self.fname,'r') as f_in:
|
|
f_in['setup'].visititems(lambda name,obj: files.append(name) if isinstance(obj,h5py.Dataset) else None)
|
|
return files
|
|
|
|
@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())
|
|
|
|
|
|
def add_absolute(self, x: str):
|
|
"""
|
|
Add absolute value.
|
|
|
|
Parameters
|
|
----------
|
|
x : str
|
|
Name of scalar, vector, or tensor dataset to take absolute value of.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(absolute,{'x':x})
|
|
|
|
|
|
def add_calculation(self,
|
|
formula: str,
|
|
name: str,
|
|
unit: str = 'n/a',
|
|
description: Optional[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')
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
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(calculation,dataset_mapping,args)
|
|
|
|
|
|
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'.
|
|
|
|
"""
|
|
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(stress_Cauchy,{'P':P,'F':F})
|
|
|
|
|
|
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')
|
|
|
|
"""
|
|
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(determinant,{'T':T})
|
|
|
|
|
|
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')
|
|
|
|
"""
|
|
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(deviator,{'T':T})
|
|
|
|
|
|
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'}, optional
|
|
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')
|
|
|
|
"""
|
|
|
|
def eigenval(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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(eigenval,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
|
|
|
|
|
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'}, optional
|
|
Eigenvalue to which the eigenvector corresponds.
|
|
Defaults to 'max'.
|
|
|
|
"""
|
|
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
|
|
|
|
|
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, optional
|
|
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]))
|
|
|
|
"""
|
|
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(IPF_color,{'q':q},{'l':l})
|
|
|
|
|
|
def add_maximum_shear(self, T_sym: str):
|
|
"""
|
|
Add maximum shear components of symmetric tensor.
|
|
|
|
Parameters
|
|
----------
|
|
T_sym : str
|
|
Name of symmetric tensor dataset.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(maximum_shear,{'T_sym':T_sym})
|
|
|
|
|
|
def add_equivalent_Mises(self,
|
|
T_sym: str,
|
|
kind: Optional[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)')
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
|
|
|
|
|
|
def add_norm(self,
|
|
x: str,
|
|
ord: Union[None, int, float, Literal['fro', 'nuc']] = None):
|
|
"""
|
|
Add the norm of a 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.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(norm,{'x':x},{'ord':ord})
|
|
|
|
|
|
def add_stress_second_Piola_Kirchhoff(self,
|
|
P: str = 'P',
|
|
F: str = 'F'):
|
|
r"""
|
|
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
|
|
:math:`\vb{S} = \left(\vb{F}^{-1} \vb{P}\right)_\text{sym}`
|
|
follows the standard definition in nonlinear continuum mechanics.
|
|
As such, no intermediate configuration, for instance that reached by :math:`\vb{F}_\text{p}`,
|
|
is taken into account.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(stress_second_Piola_Kirchhoff,{'P':P,'F':F})
|
|
|
|
|
|
|
|
def add_pole(self,
|
|
q: str = 'O',
|
|
*,
|
|
uvw: Optional[FloatSequence] = None,
|
|
hkl: Optional[FloatSequence] = None,
|
|
with_symmetry: bool = False,
|
|
normalize: bool = True):
|
|
"""
|
|
Add lab frame vector along lattice direction [uvw] or plane normal (hkl).
|
|
|
|
Parameters
|
|
----------
|
|
q : str, optional
|
|
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.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(pole,{'q':q},{'uvw':uvw,'hkl':hkl,'with_symmetry':with_symmetry,'normalize':normalize})
|
|
|
|
|
|
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')
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(rotation,{'F':F})
|
|
|
|
|
|
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')
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(spherical,{'T':T})
|
|
|
|
|
|
def add_strain(self,
|
|
F: str = 'F',
|
|
t: Literal['V', 'U'] = 'V',
|
|
m: float = 0.0):
|
|
r"""
|
|
Add strain tensor (Seth-Hill family) of a deformation gradient.
|
|
|
|
By default, the logarithmic strain based on the
|
|
left stretch tensor is added.
|
|
|
|
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 Euler-Almansi strain:
|
|
|
|
>>> import damask
|
|
>>> r = damask.Result('my_file.hdf5')
|
|
>>> r.add_strain(t='V',m=-1.0)
|
|
|
|
Add the plastic Biot strain:
|
|
|
|
>>> import damask
|
|
>>> r = damask.Result('my_file.hdf5')
|
|
>>> r.add_strain('F_p','U',0.5)
|
|
|
|
Notes
|
|
-----
|
|
The presence of rotational parts in the elastic and plastic deformation gradient
|
|
calls for the use of
|
|
material/Lagragian strain measures (based on 'U') for plastic strains and
|
|
spatial/Eulerian strain measures (based on 'V') for elastic strains
|
|
when calculating averages.
|
|
|
|
The strain is defined as:
|
|
|
|
.. math::
|
|
|
|
m = 0 \\\\
|
|
\vb*{\epsilon}_V^{(0)} = \ln (\vb{V}) \\\\
|
|
\vb*{\epsilon}_U^{(0)} = \ln (\vb{U}) \\\\
|
|
|
|
m \neq 0 \\\\
|
|
\vb*{\epsilon}_V^{(m)} = \frac{1}{2m} (\vb{V}^{2m} - \vb{I}) \\\\
|
|
\vb*{\epsilon}_U^{(m)} = \frac{1}{2m} (\vb{U}^{2m} - \vb{I})
|
|
|
|
References
|
|
----------
|
|
| https://en.wikipedia.org/wiki/Finite_strain_theory
|
|
| https://de.wikipedia.org/wiki/Verzerrungstensor
|
|
|
|
"""
|
|
def strain(F: Dict[str, Any], t: Literal['V', 'U'], m: float) -> Dict[str, Any]:
|
|
side = 'left' if t == 'V' else 'right'
|
|
return {
|
|
'data': mechanics.strain(F['data'],t,m),
|
|
'label': f"epsilon_{t}^{m}({F['label']})",
|
|
'meta': {
|
|
'unit': F['meta']['unit'],
|
|
'description': f'Seth-Hill strain tensor of order {m} based on {side} stretch tensor '+\
|
|
f"of {F['label']} ({F['meta']['description']})",
|
|
'creator': 'add_strain'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(strain,{'F':F},{'t':t,'m':m})
|
|
|
|
|
|
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'.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_pointwise(stretch_tensor,{'F':F},{'t':t})
|
|
|
|
|
|
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. fields resulting from the grid solver.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_grid(curl,{'f':f},{'size':self.size})
|
|
|
|
|
|
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. fields resulting from the grid solver.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_grid(divergence,{'f':f},{'size':self.size})
|
|
|
|
|
|
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. fields resulting from the grid solver.
|
|
|
|
"""
|
|
def 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'
|
|
}
|
|
}
|
|
|
|
self._add_generic_grid(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 DADF5 group.
|
|
datasets : dictionary
|
|
Details of the datasets to be used:
|
|
{arg (name to which the data is passed in func): label (in DADF5 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 _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 DADF5 group.
|
|
datasets : dictionary
|
|
Details of the datasets to be used:
|
|
{arg (name to which the data is passed in func): label (in DADF5 file)}.
|
|
args : dictionary, optional
|
|
Arguments parsed to func.
|
|
|
|
"""
|
|
|
|
def job_pointwise(group: str,
|
|
callback: Callable,
|
|
datasets: Dict[str, str],
|
|
args: Dict[str, str]) -> Union[None, Any]:
|
|
try:
|
|
datasets_in = {}
|
|
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()}}
|
|
return callback(**datasets_in,**args)
|
|
except Exception as err:
|
|
print(f'Error during calculation: {err}.')
|
|
return None
|
|
|
|
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
|
|
|
|
|
|
for group in util.show_progress(groups):
|
|
if not (result := job_pointwise(group, callback=func, datasets=datasets, args=args)): # type: ignore
|
|
continue
|
|
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}.')
|
|
|
|
|
|
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 get(self,
|
|
output: Union[str, List[str]] = '*',
|
|
flatten: bool = True,
|
|
prune: bool = True) -> Optional[Dict[str,Any]]:
|
|
"""
|
|
Collect data per phase/homogenization reflecting the group/folder structure in the DADF5 file.
|
|
|
|
Parameters
|
|
----------
|
|
output : (list of) str, optional
|
|
Names of the datasets to read.
|
|
Defaults to '*', in which case all datasets are read.
|
|
flatten : bool, optional
|
|
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, optional
|
|
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: Dict[str,Any] = {}
|
|
|
|
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: Optional[IntSequence] = None,
|
|
fill_float: float = np.nan,
|
|
fill_int: int = 0) -> Optional[Dict[str,Any]]:
|
|
"""
|
|
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 visible datasets are placed.
|
|
flatten : bool, optional
|
|
Remove singular levels of the folder hierarchy.
|
|
This might be beneficial in case of single increment or field.
|
|
Defaults to True.
|
|
prune : bool, optional
|
|
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, optional
|
|
Fill value for non-existent entries of floating point type.
|
|
Defaults to NaN.
|
|
fill_int : int, optional
|
|
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: Dict[str,Any] = {}
|
|
|
|
constituents_ = 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_XDMF(self,
|
|
output: Union[str, List[str]] = '*',
|
|
target_dir: Union[None, str, Path] = None,
|
|
absolute_path: bool = False):
|
|
"""
|
|
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, optional
|
|
Names of the datasets included in the XDMF file.
|
|
Defaults to '*', in which case all datasets are considered.
|
|
target_dir : str or pathlib.Path, optional
|
|
Directory to save XDMF file. Will be created if non-existent.
|
|
absolute_path : bool, optional
|
|
Store absolute (instead of relative) path to DADF5 file.
|
|
Defaults to False, i.e. the XDMF file expects the
|
|
DADF5 file at a stable relative path.
|
|
|
|
"""
|
|
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 = []
|
|
|
|
hdf5_name = self.fname.name
|
|
hdf5_dir = self.fname.parent
|
|
xdmf_dir = Path.cwd() if target_dir is None else Path(target_dir)
|
|
hdf5_link = (hdf5_dir if absolute_path else Path(os.path.relpath(hdf5_dir,xdmf_dir.resolve())))/hdf5_name
|
|
|
|
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'{hdf5_link}:/{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'{hdf5_link}:{name}'
|
|
|
|
xdmf_dir.mkdir(parents=True,exist_ok=True)
|
|
with util.open_text((xdmf_dir/hdf5_name).with_suffix('.xdmf'),'w') as f:
|
|
f.write(xml.dom.minidom.parseString(ET.tostring(xdmf).decode()).toprettyxml())
|
|
|
|
|
|
def export_VTK(self,
|
|
output: Union[str,List[str]] = '*',
|
|
mode: str = 'cell',
|
|
constituents: Optional[IntSequence] = None,
|
|
target_dir: Union[None, str, Path] = 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 PolyData (.vtp).
|
|
For cell data, the file format is either ImageData (.vti)
|
|
or UnstructuredGrid (.vtu) 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 visible datasets are exported.
|
|
mode : {'cell', 'point'}, optional
|
|
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.
|
|
target_dir : str or pathlib.Path, optional
|
|
Directory to save VTK files. Will be created if non-existent.
|
|
fill_float : float, optional
|
|
Fill value for non-existent entries of floating point type.
|
|
Defaults to NaN.
|
|
fill_int : int, optional
|
|
Fill value for non-existent entries of integer type.
|
|
Defaults to 0.
|
|
parallel : bool, optional
|
|
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()
|
|
|
|
vtk_dir = Path.cwd() if target_dir is None else Path(target_dir)
|
|
vtk_dir.mkdir(parents=True,exist_ok=True)
|
|
|
|
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(vtk_dir/f'{self.fname.stem}_inc{inc.split(prefix_inc)[-1].zfill(N_digits)}',
|
|
parallel=parallel)
|
|
|
|
def export_DADF5(self,
|
|
fname,
|
|
output: Union[str, List[str]] = '*',
|
|
mapping = None):
|
|
"""
|
|
Export visible components into a new DADF5 file.
|
|
|
|
A DADF5 (DAMASK HDF5) file contains DAMASK results.
|
|
Its group/folder structure reflects the layout in material.yaml.
|
|
|
|
Parameters
|
|
----------
|
|
fname : str or pathlib.Path
|
|
Name of the DADF5 file to be created.
|
|
output : (list of) str, optional
|
|
Names of the datasets to export.
|
|
Defaults to '*', in which case all visible datasets are exported.
|
|
mapping : numpy.ndarray of int, shape (:,:,:), optional
|
|
Indices for regridding.
|
|
|
|
"""
|
|
if Path(fname).expanduser().absolute() == self.fname:
|
|
raise PermissionError(f'cannot overwrite {self.fname}')
|
|
|
|
def cp(path_in,path_out,label,mapping):
|
|
if mapping is None:
|
|
path_in.copy(label,path_out)
|
|
else:
|
|
path_out.create_dataset(label,data=path_in[label][()][mapping])
|
|
path_out[label].attrs.update(path_in[label].attrs)
|
|
|
|
|
|
with h5py.File(self.fname,'r') as f_in, h5py.File(fname,'w') as f_out:
|
|
f_out.attrs.update(f_in.attrs)
|
|
for g in ['setup','geometry'] + (['cell_to'] if mapping is None else []):
|
|
f_in.copy(g,f_out)
|
|
|
|
if mapping is not None:
|
|
cells = mapping.shape
|
|
mapping_flat = mapping.flatten(order='F')
|
|
f_out['geometry'].attrs['cells'] = cells
|
|
f_out.create_group('cell_to') # ToDo: attribute missing
|
|
mappings = {'phase':{},'homogenization':{}} # type: ignore
|
|
|
|
mapping_phase = f_in['cell_to']['phase'][()][mapping_flat]
|
|
for p in np.unique(mapping_phase['label']):
|
|
m = mapping_phase['label'] == p
|
|
mappings['phase'][p] = mapping_phase[m]['entry']
|
|
c = np.count_nonzero(m)
|
|
mapping_phase[m] = list(zip((p,)*c,tuple(np.arange(c))))
|
|
f_out['cell_to'].create_dataset('phase',data=mapping_phase.reshape(np.prod(mapping_flat.shape),-1))
|
|
|
|
mapping_homog = f_in['cell_to']['homogenization'][()][mapping]
|
|
for h in np.unique(mapping_homog['label']):
|
|
m = mapping_homog['label'] == h
|
|
mappings['homogenization'][h] = mapping_homog[m]['entry']
|
|
c = np.count_nonzero(m)
|
|
mapping_homog[mapping_homog['label'] == h] = list(zip((h,)*c,tuple(np.arange(c))))
|
|
f_out['cell_to'].create_dataset('homogenization',data=mapping_homog.flatten())
|
|
|
|
|
|
for inc in util.show_progress(self.visible['increments']):
|
|
f_in.copy(inc,f_out,shallow=True)
|
|
if mapping is None:
|
|
for label in ['u_p','u_n']:
|
|
f_in[inc]['geometry'].copy(label,f_out[inc]['geometry'])
|
|
else:
|
|
u_p = f_in[inc]['geometry']['u_p'][()][mapping_flat]
|
|
f_out[inc]['geometry'].create_dataset('u_p',data=u_p)
|
|
u_n = np.zeros((len(mapping_flat),3)) # ToDo: needs implementation
|
|
f_out[inc]['geometry'].create_dataset('u_n',data=u_n)
|
|
|
|
|
|
for label in self.homogenizations:
|
|
f_in[inc]['homogenization'].copy(label,f_out[inc]['homogenization'],shallow=True)
|
|
for label in self.phases:
|
|
f_in[inc]['phase'].copy(label,f_out[inc]['phase'],shallow=True)
|
|
|
|
for ty in ['phase','homogenization']:
|
|
for label in self.visible[ty+'s']:
|
|
for field in _match(self.visible['fields'],f_in['/'.join([inc,ty,label])].keys()):
|
|
p = '/'.join([inc,ty,label,field])
|
|
for out in _match(output,f_in[p].keys()):
|
|
cp(f_in[p],f_out[p],out,None if mapping is None else mappings[ty][label.encode()])
|
|
|
|
|
|
def export_simulation_setup(self,
|
|
output: Union[str, List[str]] = '*',
|
|
target_dir: Union[None, str, Path] = None,
|
|
overwrite: bool = False,
|
|
):
|
|
"""
|
|
Export original simulation setup of the Result object.
|
|
|
|
Parameters
|
|
----------
|
|
output : (list of) str, optional
|
|
Names of the datasets to export to the file.
|
|
Defaults to '*', in which case all setup files are exported.
|
|
target_dir : str or pathlib.Path, optional
|
|
Directory to save setup files. Will be created if non-existent.
|
|
overwrite : bool, optional
|
|
Overwrite any existing setup files.
|
|
Defaults to False.
|
|
|
|
"""
|
|
def export(name: str,
|
|
obj: Union[h5py.Dataset,h5py.Group],
|
|
output: Union[str,List[str]],
|
|
cfg_dir: Path,
|
|
overwrite: bool):
|
|
|
|
cfg = cfg_dir/name
|
|
|
|
if type(obj) == h5py.Dataset and _match(output,[name]):
|
|
if cfg.exists() and not overwrite:
|
|
raise PermissionError(f'"{cfg}" exists')
|
|
else:
|
|
cfg.parent.mkdir(parents=True,exist_ok=True)
|
|
with util.open_text(cfg,'w') as f_out: f_out.write(obj[0].decode())
|
|
|
|
cfg_dir = (Path.cwd() if target_dir is None else Path(target_dir))
|
|
with h5py.File(self.fname,'r') as f_in:
|
|
f_in['setup'].visititems(functools.partial(export,
|
|
output=output,
|
|
cfg_dir=cfg_dir,
|
|
overwrite=overwrite))
|