import multiprocessing as mp import re import fnmatch import os import copy import datetime import xml.etree.ElementTree as ET import xml.dom.minidom from pathlib import Path from functools import partial from collections import defaultdict from collections.abc import Iterable import h5py import numpy as np import numpy.ma as ma import damask from . import VTK from . import Orientation from . import grid_filters from . import mechanics from . import tensor from . import util h5py3 = h5py.__version__[0] == '3' chunk_size = 1024**2//8 # for compression in HDF5 def _read(dataset): """Read a dataset and its metadata into a numpy.ndarray.""" metadata = {k:(v.decode() if not h5py3 and type(v) is bytes else v) for k,v in dataset.attrs.items()} dtype = np.dtype(dataset.dtype,metadata=metadata) return np.array(dataset,dtype=dtype) def _match(requested,existing): """Find matches among two sets of labels.""" def flatten_list(list_of_lists): return [e for e_ in list_of_lists for e in e_] if requested is True: requested = '*' elif requested is False or requested is None: requested = [] requested_ = requested if hasattr(requested,'__iter__') and not isinstance(requested,str) else \ [requested] return sorted(set(flatten_list([fnmatch.filter(existing,r) for r in requested_])), key=util.natural_sort) def _empty_like(dataset,N_materialpoints,fill_float,fill_int): """Create empty numpy.ma.MaskedArray.""" return ma.array(np.empty((N_materialpoints,)+dataset.shape[1:],dataset.dtype), fill_value = fill_float if dataset.dtype in np.sctypes['float'] else fill_int, mask = True) class Result: """ Add data to and export data from a DADF5 file. A DADF5 (DAMASK HDF5) file contains DAMASK results. Its group/folder structure reflects the layout in material.yaml. This class provides a customizable view on the DADF5 file. Upon initialization, all attributes are visible. Derived quantities are added to the file and existing data is exported based on the current view. Examples -------- Open 'my_file.hdf5', which is assumed to contain deformation gradient 'F' and first Piola-Kirchhoff stress 'P', add the Mises equivalent of the Cauchy stress, and export it to VTK (file) and numpy.ndarray (memory). >>> import damask >>> r = damask.Result('my_file.hdf5') >>> r.add_Cauchy() >>> r.add_equivalent_Mises('sigma') >>> r.export_VTK() >>> r_last = r.view('increments',-1) >>> sigma_vM_last = r_last.get('sigma_vM') """ def __init__(self,fname): """ New result view bound to a HDF5 file. Parameters ---------- fname : str or pathlib.Path Name of the DADF5 file to be opened. """ with h5py.File(fname,'r') as f: self.version_major = f.attrs['DADF5_version_major'] self.version_minor = f.attrs['DADF5_version_minor'] if self.version_major != 0 or not 12 <= self.version_minor <= 13: raise TypeError(f'Unsupported DADF5 version {self.version_major}.{self.version_minor}') self.structured = 'cells' in f['geometry'].attrs.keys() if self.structured: self.cells = f['geometry'].attrs['cells'] self.size = f['geometry'].attrs['size'] self.origin = f['geometry'].attrs['origin'] else: self.add_curl = self.add_divergence = self.add_gradient = None r=re.compile('increment_[0-9]+') self.increments = sorted([i for i in f.keys() if r.match(i)],key=util.natural_sort) self.times = [round(f[i].attrs['t/s'],12) for i in self.increments] if len(self.increments) == 0: raise ValueError('incomplete DADF5 file') self.N_materialpoints, self.N_constituents = np.shape(f['cell_to/phase']) self.homogenization = f['cell_to/homogenization']['label'].astype('str') self.homogenizations = sorted(np.unique(self.homogenization),key=util.natural_sort) self.phase = f['cell_to/phase']['label'].astype('str') self.phases = sorted(np.unique(self.phase),key=util.natural_sort) self.fields = [] for c in self.phases: self.fields += f['/'.join([self.increments[0],'phase',c])].keys() for m in self.homogenizations: self.fields += f['/'.join([self.increments[0],'homogenization',m])].keys() self.fields = sorted(set(self.fields),key=util.natural_sort) # make unique self.visible = {'increments': self.increments, 'phases': self.phases, 'homogenizations': self.homogenizations, 'fields': self.fields, } self.fname = Path(fname).absolute() self._allow_modification = False def __copy__(self): """Create deep copy.""" return copy.deepcopy(self) copy = __copy__ def __repr__(self): """Show summary of file content.""" visible_increments = self.visible['increments'] first = self.view('increments',visible_increments[0:1]).list_data() last = '' if len(visible_increments) < 2 else \ self.view('increments',visible_increments[-1:]).list_data() in_between = '' if len(visible_increments) < 3 else \ ''.join([f'\n{inc}\n ...\n' for inc in visible_increments[1:-1]]) return util.srepr(first + in_between + last) def _manage_view(self,action,what,datasets): """ Manages the visibility of the groups. Parameters ---------- action : str Select from 'set', 'add', and 'del'. what : str Attribute to change (must be from self.visible). datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool Name of datasets; supports '?' and '*' wildcards. True is equivalent to '*', False is equivalent to []. Returns ------- view : damask.Result Modified or new view on the DADF5 file. """ # allow True/False and string arguments if datasets is True: datasets = '*' elif datasets is False or datasets is None: datasets = [] choice = list(datasets).copy() if hasattr(datasets,'__iter__') and not isinstance(datasets,str) else \ [datasets] what_ = what if what.endswith('s') else what+'s' if what_ == 'increments': choice = [c if isinstance(c,str) and c.startswith('increment_') else self.increments[c] if isinstance(c,int) and c<0 else f'increment_{c}' for c in choice] elif what_ == 'times': what_ = 'increments' if choice == ['*']: choice = self.increments else: iterator = map(float,choice) choice = [] for c in iterator: idx = np.searchsorted(self.times,c) if idx >= len(self.times): continue if np.isclose(c,self.times[idx]): choice.append(self.increments[idx]) elif np.isclose(c,self.times[idx+1]): choice.append(self.increments[idx+1]) valid = _match(choice,getattr(self,what_)) existing = set(self.visible[what_]) dup = self.copy() if action == 'set': dup.visible[what_] = sorted(set(valid), key=util.natural_sort) elif action == 'add': add = existing.union(valid) dup.visible[what_] = sorted(add, key=util.natural_sort) elif action == 'del': diff = existing.difference(valid) dup.visible[what_] = sorted(diff, key=util.natural_sort) return dup def modification_enable(self): """ Allow modification of existing data. Returns ------- modified_view : damask.Result View without write-protection of existing data. """ print(util.warn('Warning: Modification of existing datasets allowed!')) dup = self.copy() dup._allow_modification = True return dup def modification_disable(self): """ Prevent modification of existing data (default case). Returns ------- modified_view : damask.Result View with write-protection of existing data. """ dup = self.copy() dup._allow_modification = False return dup def increments_in_range(self,start,end): """ Get all increments within a given range. Parameters ---------- start : int or str Start increment. end : int or str End increment. Returns ------- increments : list of ints Increment number of all increments within the given bounds. """ selected = [] for i,inc in enumerate([int(i[10:]) for i in self.increments]): s,e = map(lambda x: int(x[10:] if isinstance(x,str) and x.startswith('inc') else x), (start,end)) if s <= inc <= e: selected.append(self.increments[i]) return selected def times_in_range(self,start,end): """ Get all increments within a given time range. Parameters ---------- start : float Time of start increment. end : float Time of end increment. Returns ------- times : list of float Simulation time of all increments within the given bounds. """ selected = [] for i,time in enumerate(self.times): if start <= time <= end: selected.append(self.times[i]) return selected def view(self,what,datasets): """ Set view. Parameters ---------- what : {'increments', 'times', 'phases', 'homogenizations', 'fields'} Attribute to change. datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool Name of datasets; supports '?' and '*' wildcards. True is equivalent to '*', False is equivalent to []. Returns ------- view : damask.Result View with only the selected attributes being visible. Examples -------- Get a view that shows only results from the initial configuration: >>> import damask >>> r = damask.Result('my_file.hdf5') >>> r_first = r.view('increment',0) Get a view that shows all results between simulation times of 10 to 40: >>> import damask >>> r = damask.Result('my_file.hdf5') >>> r_t10to40 = r.view('times',r.times_in_range(10.0,40.0)) """ return self._manage_view('set',what,datasets) def view_more(self,what,datasets): """ Add to view. Parameters ---------- what : {'increments', 'times', 'phases', 'homogenizations', 'fields'} Attribute to change. datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool Name of datasets; supports '?' and '*' wildcards. True is equivalent to '*', False is equivalent to []. Returns ------- modified_view : damask.Result View with additional visible attributes. Examples -------- Get a view that shows only results from first and last increment: >>> import damask >>> r_empty = damask.Result('my_file.hdf5').view('increments',False) >>> r_first = r_empty.view_more('increments',0) >>> r_first_and_last = r.first.view_more('increments',-1) """ return self._manage_view('add',what,datasets) def view_less(self,what,datasets): """ Remove from view. Parameters ---------- what : {'increments', 'times', 'phases', 'homogenizations', 'fields'} Attribute to change. datasets : (list of) int (for increments), (list of) float (for times), (list of) str, or bool Name of datasets; supports '?' and '*' wildcards. True is equivalent to '*', False is equivalent to []. Returns ------- modified_view : damask.Result View with fewer visible attributes. Examples -------- Get a view that omits the undeformed configuration: >>> import damask >>> r_all = damask.Result('my_file.hdf5') >>> r_deformed = r_all.view_less('increments',0) """ return self._manage_view('del',what,datasets) def rename(self,name_src,name_dst): """ Rename/move datasets (within the same group/folder). This operation is discouraged because the history of the data becomes untraceable and data integrity is not ensured. Parameters ---------- name_src : str Name of the datasets to be renamed. name_dst : str New name of the datasets. Examples -------- Rename datasets containing the deformation gradient from 'F' to 'def_grad': >>> import damask >>> r = damask.Result('my_file.hdf5') >>> r_unprotected = r.modification_enable() >>> r_unprotected.rename('F','def_grad') """ if not self._allow_modification: raise PermissionError('Renaming datasets not permitted') 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_src = '/'.join([inc,ty,label,field,name_src]) path_dst = '/'.join([inc,ty,label,field,name_dst]) if path_src in f.keys(): f[path_dst] = f[path_src] f[path_dst].attrs['renamed'] = f'original name: {name_src}' if h5py3 else \ f'original name: {name_src}'.encode() del f[path_src] def remove(self,name): """ Remove/delete datasets. This operation is discouraged because the history of the data becomes untraceable and data integrity is not ensured. Parameters ---------- name : str Name of the datasets to be deleted. Examples -------- Delete the deformation gradient 'F': >>> import damask >>> r = damask.Result('my_file.hdf5') >>> r_unprotected = r.modification_enable() >>> r_unprotected.remove('F') """ if not self._allow_modification: raise PermissionError('Removing datasets not permitted') 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): """Return information on all active datasets in the file.""" msg = '' with h5py.File(self.fname,'r') as f: for inc in self.visible['increments']: msg = ''.join([msg,f'\n{inc} ({self.times[self.increments.index(inc)]}s)\n']) for ty in ['phase','homogenization']: msg = ' '.join([msg,f'{ty}\n']) for label in self.visible[ty+'s']: msg = ' '.join([msg,f'{label}\n']) for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()): msg = ' '.join([msg,f'{field}\n']) for d in f['/'.join([inc,ty,label,field])].keys(): dataset = f['/'.join([inc,ty,label,field,d])] unit = f' / {dataset.attrs["unit"]}' if h5py3 else \ f' / {dataset.attrs["unit"].decode()}' description = dataset.attrs['description'] if h5py3 else \ dataset.attrs['description'].decode() msg = ' '.join([msg,f'{d}{unit}: {description}\n']) return msg def enable_user_function(self,func): globals()[func.__name__]=func print(f'Function {func.__name__} enabled in add_calculation.') @property def coordinates0_point(self): """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): """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): """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): return { 'data': np.abs(x['data']), 'label': f'|{x["label"]}|', 'meta': { 'unit': x['meta']['unit'], 'description': f"absolute value of {x['label']} ({x['meta']['description']})", 'creator': 'add_absolute' } } def add_absolute(self,x): """ Add absolute value. Parameters ---------- x : str 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): formula = kwargs['formula'] for d in re.findall(r'#(.*?)#',formula): formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']") return { 'data': eval(formula), 'label': kwargs['label'], 'meta': { 'unit': kwargs['unit'], 'description': f"{kwargs['description']} (formula: {kwargs['formula']})", 'creator': 'add_calculation' } } def add_calculation(self,formula,name,unit='n/a',description=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,F): return { 'data': mechanics.stress_Cauchy(P['data'],F['data']), 'label': 'sigma', 'meta': { 'unit': P['meta']['unit'], 'description': "Cauchy stress calculated " f"from {P['label']} ({P['meta']['description']})" f" and {F['label']} ({F['meta']['description']})", 'creator': 'add_stress_Cauchy' } } def add_stress_Cauchy(self,P='P',F='F'): """ Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient. Parameters ---------- P : str, optional 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): return { 'data': np.linalg.det(T['data']), 'label': f"det({T['label']})", 'meta': { 'unit': T['meta']['unit'], 'description': f"determinant of tensor {T['label']} ({T['meta']['description']})", 'creator': 'add_determinant' } } def add_determinant(self,T): """ Add the determinant of a tensor. Parameters ---------- T : str 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): return { 'data': tensor.deviatoric(T['data']), 'label': f"s_{T['label']}", 'meta': { 'unit': T['meta']['unit'], 'description': f"deviator of tensor {T['label']} ({T['meta']['description']})", 'creator': 'add_deviator' } } def add_deviator(self,T): """ Add the deviatoric part of a tensor. Parameters ---------- T : str 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,eigenvalue): if eigenvalue == 'max': label,p = 'maximum',2 elif eigenvalue == 'mid': label,p = 'intermediate',1 elif eigenvalue == 'min': label,p = 'minimum',0 return { 'data': tensor.eigenvalues(T_sym['data'])[:,p], 'label': f"lambda_{eigenvalue}({T_sym['label']})", 'meta' : { 'unit': T_sym['meta']['unit'], 'description': f"{label} eigenvalue of {T_sym['label']} ({T_sym['meta']['description']})", 'creator': 'add_eigenvalue' } } def add_eigenvalue(self,T_sym,eigenvalue='max'): """ Add eigenvalues of symmetric tensor. Parameters ---------- T_sym : str 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,eigenvalue): if eigenvalue == 'max': label,p = 'maximum',2 elif eigenvalue == 'mid': label,p = 'intermediate',1 elif eigenvalue == 'min': label,p = 'minimum',0 return { 'data': tensor.eigenvectors(T_sym['data'])[:,p], 'label': f"v_{eigenvalue}({T_sym['label']})", 'meta' : { 'unit': '1', 'description': f"eigenvector corresponding to {label} eigenvalue" f" of {T_sym['label']} ({T_sym['meta']['description']})", 'creator': 'add_eigenvector' } } def add_eigenvector(self,T_sym,eigenvalue='max'): """ Add eigenvector of symmetric tensor. Parameters ---------- T_sym : str 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,q): 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,q='O'): """ Add RGB color tuple of inverse pole figure (IPF) color. Parameters ---------- l : numpy.array of shape (3) Lab frame direction for inverse pole figure. q : str 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): return { 'data': mechanics.maximum_shear(T_sym['data']), 'label': f"max_shear({T_sym['label']})", 'meta': { 'unit': T_sym['meta']['unit'], 'description': f"maximum shear component of {T_sym['label']} ({T_sym['meta']['description']})", 'creator': 'add_maximum_shear' } } def add_maximum_shear(self,T_sym): """ Add maximum shear components of symmetric tensor. Parameters ---------- T_sym : str Name of symmetric tensor dataset. """ self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym}) @staticmethod def _add_equivalent_Mises(T_sym,kind): k = kind if k is None: if T_sym['meta']['unit'] == '1': k = 'strain' elif T_sym['meta']['unit'] == 'Pa': k = 'stress' if k not in ['stress', 'strain']: raise ValueError(f'invalid von Mises kind {kind}') return { 'data': (mechanics.equivalent_strain_Mises if k=='strain' else \ mechanics.equivalent_stress_Mises)(T_sym['data']), 'label': f"{T_sym['label']}_vM", 'meta': { 'unit': T_sym['meta']['unit'], 'description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['description']})", 'creator': 'add_Mises' } } def add_equivalent_Mises(self,T_sym,kind=None): """ Add the equivalent Mises stress or strain of a symmetric tensor. Parameters ---------- T_sym : str 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,ord): o = ord if len(x['data'].shape) == 2: axis = 1 t = 'vector' if o is None: o = 2 elif len(x['data'].shape) == 3: axis = (1,2) t = 'tensor' if o is None: o = 'fro' else: raise ValueError(f'invalid norm order {ord}') return { 'data': np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True), 'label': f"|{x['label']}|_{o}", 'meta': { 'unit': x['meta']['unit'], 'description': f"{o}-norm of {t} {x['label']} ({x['meta']['description']})", 'creator': 'add_norm' } } def add_norm(self,x,ord=None): """ Add the norm of vector or tensor. Parameters ---------- x : str 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,F): return { 'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']), 'label': 'S', 'meta': { 'unit': P['meta']['unit'], 'description': "second Piola-Kirchhoff stress calculated " f"from {P['label']} ({P['meta']['description']})" f" and {F['label']} ({F['meta']['description']})", 'creator': 'add_stress_second_Piola_Kirchhoff' } } def add_stress_second_Piola_Kirchhoff(self,P='P',F='F'): """ Add second Piola-Kirchhoff stress calculated from first Piola-Kirchhoff stress and deformation gradient. Parameters ---------- P : str, optional 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}) # The add_pole functionality needs discussion. # The new Crystal object can perform such a calculation but the outcome depends on the lattice parameters # as well as on whether a direction or plane is concerned (see the DAMASK_examples/pole_figure notebook). # Below code appears to be too simplistic. # @staticmethod # def _add_pole(q,p,polar): # pole = np.array(p) # unit_pole = pole/np.linalg.norm(pole) # m = util.scale_to_coprime(pole) # rot = Rotation(q['data'].view(np.double).reshape(-1,4)) # # rotatedPole = rot @ np.broadcast_to(unit_pole,rot.shape+(3,)) # rotate pole according to crystal orientation # xy = rotatedPole[:,0:2]/(1.+abs(unit_pole[2])) # stereographic projection # coords = xy if not polar else \ # np.block([np.sqrt(xy[:,0:1]*xy[:,0:1]+xy[:,1:2]*xy[:,1:2]),np.arctan2(xy[:,1:2],xy[:,0:1])]) # return { # 'data': coords, # 'label': 'p^{}_[{} {} {})'.format(u'rφ' if polar else 'xy',*m), # 'meta' : { # 'unit': '1', # 'description': '{} coordinates of stereographic projection of pole (direction/plane) in crystal frame'\ # .format('Polar' if polar else 'Cartesian'), # 'creator': 'add_pole' # } # } # def add_pole(self,q,p,polar=False): # """ # Add coordinates of stereographic projection of given pole in crystal frame. # # Parameters # ---------- # q : str # Name of the dataset containing the crystallographic orientation as quaternions. # p : numpy.array of shape (3) # Crystallographic direction or plane. # polar : bool, optional # Give pole in polar coordinates. Defaults to False. # # """ # self._add_generic_pointwise(self._add_pole,{'q':q},{'p':p,'polar':polar}) @staticmethod def _add_rotation(F): return { 'data': mechanics.rotation(F['data']).as_matrix(), 'label': f"R({F['label']})", 'meta': { 'unit': F['meta']['unit'], 'description': f"rotational part of {F['label']} ({F['meta']['description']})", 'creator': 'add_rotation' } } def add_rotation(self,F): """ Add rotational part of a deformation gradient. Parameters ---------- F : str 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): return { 'data': tensor.spherical(T['data'],False), 'label': f"p_{T['label']}", 'meta': { 'unit': T['meta']['unit'], 'description': f"spherical component of tensor {T['label']} ({T['meta']['description']})", 'creator': 'add_spherical' } } def add_spherical(self,T): """ Add the spherical (hydrostatic) part of a tensor. Parameters ---------- T : str 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,t,m): return { 'data': mechanics.strain(F['data'],t,m), 'label': f"epsilon_{t}^{m}({F['label']})", 'meta': { 'unit': F['meta']['unit'], 'description': f"strain tensor of {F['label']} ({F['meta']['description']})", 'creator': 'add_strain' } } def add_strain(self,F='F',t='V',m=0.0): """ Add strain tensor of a deformation gradient. For details, see damask.mechanics.strain. Parameters ---------- F : str, optional 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.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.strain('F_p','V',-1) """ self._add_generic_pointwise(self._add_strain,{'F':F},{'t':t,'m':m}) @staticmethod def _add_stretch_tensor(F,t): return { 'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']), 'label': f"{t}({F['label']})", 'meta': { 'unit': F['meta']['unit'], 'description': 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='F',t='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,size): 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): """ 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,size): 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): """ 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,size): 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): """ 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,datasets,args={},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() with h5py.File(self.fname, 'a') as f: for increment in self.place(datasets.values(),False).items(): for ty in increment[1].items(): for field in ty[1].items(): d = 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]]) dataset = f[path].create_dataset(r['label'],data=result1) 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 r['meta'].items(): dataset.attrs[l.lower()]=v if h5py3 else v.encode() creator = dataset.attrs['creator'] if h5py3 else \ dataset.attrs['creator'].decode() dataset.attrs['creator'] = f'damask.Result.{creator} v{damask.version}' if h5py3 else \ f'damask.Result.{creator} v{damask.version}'.encode() def _job_pointwise(self,group,func,datasets,args,lock): """Execute job for _add_generic_pointwise.""" try: datasets_in = {} lock.acquire() with h5py.File(self.fname,'r') as f: for arg,label in datasets.items(): loc = f[group+'/'+label] datasets_in[arg]={'data' :loc[()], 'label':label, 'meta': {k:(v.decode() if not h5py3 and type(v) is bytes else v) \ for k,v in loc.attrs.items()}} lock.release() r = func(**datasets_in,**args) return [group,r] except Exception as err: print(f'Error during calculation: {err}.') return [None,None] def _add_generic_pointwise(self,func,datasets,args={}): """ General function to add pointwise data. Parameters ---------- func : function Callback function that calculates a new dataset from one or more datasets per HDF5 group. datasets : dictionary Details of the datasets to be used: {arg (name to which the data is passed in func): label (in HDF5 file)}. args : dictionary, optional Arguments parsed to func. """ 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,func=func,datasets=datasets,args=args,lock=lock) for group,result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)): if not result: continue lock.acquire() with h5py.File(self.fname, 'a') as f: try: if self._allow_modification and '/'.join([group,result['label']]) in f: dataset = f['/'.join([group,result['label']])] dataset[...] = result['data'] dataset.attrs['overwritten'] = True else: if result['data'].size >= chunk_size*2: shape = result['data'].shape chunks = (chunk_size//np.prod(shape[1:]),)+shape[1:] dataset = f[group].create_dataset(result['label'],data=result['data'], maxshape=shape, chunks=chunks, compression='gzip', compression_opts=6, shuffle=True,fletcher32=True) else: dataset = f[group].create_dataset(result['label'],data=result['data']) now = datetime.datetime.now().astimezone() dataset.attrs['created'] = now.strftime('%Y-%m-%d %H:%M:%S%z') if h5py3 else \ now.strftime('%Y-%m-%d %H:%M:%S%z').encode() for l,v in result['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='*'): """ Write XDMF file to directly visualize data in 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 open(self.fname.with_suffix('.xdmf').name,'w',newline='\n') as f: f.write(xml.dom.minidom.parseString(ET.tostring(xdmf).decode()).toprettyxml()) def _mappings(self): """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='*',mode='cell',constituents=None,fill_float=np.nan,fill_int=0,parallel=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.set_comments(util.execution_stamp('Result','export_VTK')) N_digits = int(np.floor(np.log10(max(1,int(self.increments[-1][10:])))))+1 constituents_ = constituents if isinstance(constituents,Iterable) else \ (range(self.N_constituents) if constituents is None else [constituents]) suffixes = [''] if self.N_constituents == 1 or isinstance(constituents,int) else \ [f'#{c}' for c in constituents_] at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings() with h5py.File(self.fname,'r') as f: 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.add_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.add(u,'u') for ty in ['phase','homogenization']: for field in self.visible['fields']: outs = {} for label in self.visible[ty+'s']: if field not in f['/'.join([inc,ty,label])].keys(): continue for out in _match(output,f['/'.join([inc,ty,label,field])].keys()): data = ma.array(_read(f['/'.join([inc,ty,label,field,out])])) if ty == 'phase': if out+suffixes[0] not in outs.keys(): for c,suffix in zip(constituents_,suffixes): outs[out+suffix] = \ _empty_like(data,self.N_materialpoints,fill_float,fill_int) for c,suffix in zip(constituents_,suffixes): outs[out+suffix][at_cell_ph[c][label]] = data[in_data_ph[c][label]] if ty == 'homogenization': if out not in outs.keys(): outs[out] = _empty_like(data,self.N_materialpoints,fill_float,fill_int) outs[out][at_cell_ho[label]] = data[in_data_ho[label]] for label,dataset in outs.items(): v.add(dataset,' / '.join(['/'.join([ty,field,label]),dataset.dtype.metadata['unit']])) v.save(f'{self.fname.stem}_inc{inc[10:].zfill(N_digits)}',parallel=parallel) def get(self,output='*',flatten=True,prune=True): """ Collect data per phase/homogenization reflecting the group/folder structure in the DADF5 file. Parameters ---------- output : (list of) str 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 = {} 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='*',flatten=True,prune=True,constituents=None,fill_float=np.nan,fill_int=0): """ Merge data into spatial order that is compatible with the damask.VTK geometry representation. The returned data structure reflects the group/folder structure in the DADF5 file. Multi-phase data is fused into a single output. `place` is equivalent to `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 = {} constituents_ = constituents if isinstance(constituents,Iterable) else \ (range(self.N_constituents) if constituents is None else [constituents]) suffixes = [''] if self.N_constituents == 1 or isinstance(constituents,int) else \ [f'#{c}' for c in constituents_] at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings() with h5py.File(self.fname,'r') as f: for inc in util.show_progress(self.visible['increments']): r[inc] = {'phase':{},'homogenization':{},'geometry':{}} for out in _match(output,f['/'.join([inc,'geometry'])].keys()): r[inc]['geometry'][out] = ma.array(_read(f['/'.join([inc,'geometry',out])]),fill_value = fill_float) for ty in ['phase','homogenization']: for label in self.visible[ty+'s']: for field in _match(self.visible['fields'],f['/'.join([inc,ty,label])].keys()): if field not in r[inc][ty].keys(): r[inc][ty][field] = {} for out in _match(output,f['/'.join([inc,ty,label,field])].keys()): data = ma.array(_read(f['/'.join([inc,ty,label,field,out])])) if ty == 'phase': if out+suffixes[0] not in r[inc][ty][field].keys(): for c,suffix in zip(constituents_,suffixes): r[inc][ty][field][out+suffix] = \ _empty_like(data,self.N_materialpoints,fill_float,fill_int) for c,suffix in zip(constituents_,suffixes): r[inc][ty][field][out+suffix][at_cell_ph[c][label]] = data[in_data_ph[c][label]] if ty == 'homogenization': if out not in r[inc][ty][field].keys(): r[inc][ty][field][out] = \ _empty_like(data,self.N_materialpoints,fill_float,fill_int) r[inc][ty][field][out][at_cell_ho[label]] = data[in_data_ho[label]] if prune: r = util.dict_prune(r) if flatten: r = util.dict_flatten(r) return None if (type(r) == dict and r == {}) else r