from queue import Queue import re import glob import os import vtk from vtk.util import numpy_support import h5py import numpy as np from . import util from . import version from . import mechanics # ------------------------------------------------------------------ class DADF5(): """ Read and write to DADF5 files. DADF5 files contain DAMASK results. """ # ------------------------------------------------------------------ def __init__(self,fname): """ Opens an existing DADF5 file. Parameters ---------- fname : str name of the DADF5 file to be openend. """ with h5py.File(fname,'r') as f: try: self.version_major = f.attrs['DADF5_version_major'] self.version_minor = f.attrs['DADF5_version_minor'] except KeyError: self.version_major = f.attrs['DADF5-major'] self.version_minor = f.attrs['DADF5-minor'] if self.version_major != 0 or not 2 <= self.version_minor <= 4: raise TypeError('Unsupported DADF5 version {} '.format(f.attrs['DADF5-version'])) self.structured = 'grid' in f['geometry'].attrs.keys() if self.structured: self.grid = f['geometry'].attrs['grid'] self.size = f['geometry'].attrs['size'] r=re.compile('inc[0-9]+') increments_unsorted = {int(i[3:]):i for i in f.keys() if r.match(i)} self.increments = [increments_unsorted[i] for i in sorted(increments_unsorted)] self.times = [round(f[i].attrs['time/s'],12) for i in self.increments] self.Nmaterialpoints, self.Nconstituents = np.shape(f['mapping/cellResults/constituent']) self.materialpoints = [m.decode() for m in np.unique(f['mapping/cellResults/materialpoint']['Name'])] self.constituents = [c.decode() for c in np.unique(f['mapping/cellResults/constituent'] ['Name'])] self.con_physics = [] for c in self.constituents: self.con_physics += f['/'.join([self.increments[0],'constituent',c])].keys() self.con_physics = list(set(self.con_physics)) # make unique self.mat_physics = [] for m in self.materialpoints: self.mat_physics += f['/'.join([self.increments[0],'materialpoint',m])].keys() self.mat_physics = list(set(self.mat_physics)) # make unique self.visible= {'increments': self.increments, 'constituents': self.constituents, 'materialpoints': self.materialpoints, 'constituent': range(self.Nconstituents), # ToDo: stupid naming 'con_physics': self.con_physics, 'mat_physics': self.mat_physics} self.fname = fname def __manage_visible(self,datasets,what,action): """ Manages the visibility of the groups. Parameters ---------- datasets : list of str or Boolean name of datasets as list, supports ? and * wildcards. True is equivalent to [*], False is equivalent to [] what : str attribute to change (must be in self.visible) action : str select from 'set', 'add', and 'del' """ # allow True/False and string arguments if datasets is True: datasets = ['*'] elif datasets is False: datasets = [] choice = [datasets] if isinstance(datasets,str) else datasets valid = [e for e_ in [glob.fnmatch.filter(getattr(self,what),s) for s in choice] for e in e_] existing = set(self.visible[what]) if action == 'set': self.visible[what] = valid elif action == 'add': self.visible[what] = list(existing.union(valid)) elif action == 'del': self.visible[what] = list(existing.difference_update(valid)) def __time_to_inc(self,start,end): selected = [] for i,time in enumerate(self.times): if start <= time <= end: selected.append(self.increments[i]) return selected def set_by_time(self,start,end): """ Set active increments based on start and end time. Parameters ---------- start : float start time (included) end : float end time (included) """ self.__manage_visible(self.__time_to_inc(start,end),'increments','set') def add_by_time(self,start,end): """ Add to active increments based on start and end time. Parameters ---------- start : float start time (included) end : float end time (included) """ self.__manage_visible(self.__time_to_inc(start,end),'increments','add') def del_by_time(self,start,end): """ Delete from active increments based on start and end time. Parameters ---------- start : float start time (included) end : float end time (included) """ self.__manage_visible(self.__time_to_inc(start,end),'increments','del') def set_by_increment(self,start,end): """ Set active time increments based on start and end increment. Parameters ---------- start : int start increment (included) end : int end increment (included) """ if self.version_minor >= 4: self.__manage_visible([ 'inc{}'.format(i) for i in range(start,end+1)],'increments','set') else: self.__manage_visible(['inc{:05d}'.format(i) for i in range(start,end+1)],'increments','set') def add_by_increment(self,start,end): """ Add to active time increments based on start and end increment. Parameters ---------- start : int start increment (included) end : int end increment (included) """ if self.version_minor >= 4: self.__manage_visible([ 'inc{}'.format(i) for i in range(start,end+1)],'increments','add') else: self.__manage_visible(['inc{:05d}'.format(i) for i in range(start,end+1)],'increments','add') def del_by_increment(self,start,end): """ Delet from active time increments based on start and end increment. Parameters ---------- start : int start increment (included) end : int end increment (included) """ if self.version_minor >= 4: self.__manage_visible([ 'inc{}'.format(i) for i in range(start,end+1)],'increments','del') else: self.__manage_visible(['inc{:05d}'.format(i) for i in range(start,end+1)],'increments','del') def iter_visible(self,what): """ Iterate over visible items by setting each one visible. Parameters ---------- what : str attribute to change (must be in self.visible) """ datasets = self.visible[what] last_datasets = datasets.copy() for dataset in datasets: if last_datasets != self.visible[what]: self.__manage_visible(datasets,what,'set') raise Exception self.__manage_visible(dataset,what,'set') last_datasets = self.visible[what] yield dataset self.__manage_visible(datasets,what,'set') def set_visible(self,what,datasets): """ Set active groups. Parameters ---------- datasets : list of str or Boolean name of datasets as list, supports ? and * wildcards. True is equivalent to [*], False is equivalent to [] what : str attribute to change (must be in self.visible) """ self.__manage_visible(datasets,what,'set') def add_visible(self,what,datasets): """ Add to active groups. Parameters ---------- datasets : list of str or Boolean name of datasets as list, supports ? and * wildcards. True is equivalent to [*], False is equivalent to [] what : str attribute to change (must be in self.visible) """ self.__manage_visible(datasets,what,'add') def del_visible(self,what,datasets): """ Delete from active groupse. Parameters ---------- datasets : list of str or Boolean name of datasets as list, supports ? and * wildcards. True is equivalent to [*], False is equivalent to [] what : str attribute to change (must be in self.visible) """ self.__manage_visible(datasets,what,'del') def groups_with_datasets(self,datasets): """ Get groups that contain all requested datasets. Only groups within inc?????/constituent/*_*/* inc?????/materialpoint/*_*/* are considered as they contain the data. Single strings will be treated as list with one entry. Wild card matching is allowed, but the number of arguments need to fit. Parameters ---------- datasets : iterable or str or boolean Examples -------- datasets = False matches no group datasets = True matches all groups datasets = ['F','P'] matches a group with ['F','P','sigma'] datasets = ['*','P'] matches a group with ['F','P'] datasets = ['*'] does not match a group with ['F','P','sigma'] datasets = ['*','*'] does not match a group with ['F','P','sigma'] datasets = ['*','*','*'] matches a group with ['F','P','sigma'] """ if datasets is False: return [] sets = [datasets] if isinstance(datasets,str) else datasets groups = [] with h5py.File(self.fname,'r') as f: for i in self.iter_visible('increments'): for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']): for oo in self.iter_visible(o): for pp in self.iter_visible(p): group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue if sets is True: groups.append(group) else: match = [e for e_ in [glob.fnmatch.filter(f[group].keys(),s) for s in sets] for e in e_] if len(set(match)) == len(sets) : groups.append(group) return groups def list_data(self): """Return information on all active datasets in the file.""" message = '' with h5py.File(self.fname,'r') as f: for s,i in enumerate(self.iter_visible('increments')): message+='\n{} ({}s)\n'.format(i,self.times[s]) for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']): for oo in self.iter_visible(o): message+=' {}\n'.format(oo) for pp in self.iter_visible(p): message+=' {}\n'.format(pp) group = '/'.join([i,o[:-1],oo,pp]) # o[:-1]: plural/singular issue for d in f[group].keys(): try: dataset = f['/'.join([group,d])] message+=' {} / ({}): {}\n'.format(d,dataset.attrs['Unit'].decode(),dataset.attrs['Description'].decode()) except KeyError: pass return message def get_dataset_location(self,label): """Return the location of all active datasets with given label.""" path = [] with h5py.File(self.fname,'r') as f: for i in self.iter_visible('increments'): k = '/'.join([i,'geometry',label]) try: f[k] path.append(k) except KeyError as e: pass for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']): for oo in self.iter_visible(o): for pp in self.iter_visible(p): k = '/'.join([i,o[:-1],oo,pp,label]) try: f[k] path.append(k) except KeyError as e: pass return path def get_constituent_ID(self,c=0): """Pointwise constituent ID.""" with h5py.File(self.fname,'r') as f: names = f['/mapping/cellResults/constituent']['Name'][:,c].astype('str') return np.array([int(n.split('_')[0]) for n in names.tolist()],dtype=np.int32) def get_crystal_structure(self): # ToDo: extension to multi constituents/phase """Info about the crystal structure.""" with h5py.File(self.fname,'r') as f: return f[self.get_dataset_location('orientation')[0]].attrs['Lattice'].astype('str') # np.bytes_ to string def read_dataset(self,path,c=0,plain=False): """ Dataset for all points/cells. If more than one path is given, the dataset is composed of the individual contributions. """ with h5py.File(self.fname,'r') as f: shape = (self.Nmaterialpoints,) + np.shape(f[path[0]])[1:] if len(shape) == 1: shape = shape +(1,) dataset = np.full(shape,np.nan,dtype=np.dtype(f[path[0]])) for pa in path: label = pa.split('/')[2] if (pa.split('/')[1] == 'geometry'): dataset = np.array(f[pa]) continue p = np.where(f['mapping/cellResults/constituent'][:,c]['Name'] == str.encode(label))[0] if len(p)>0: u = (f['mapping/cellResults/constituent']['Position'][p,c]) a = np.array(f[pa]) if len(a.shape) == 1: a=a.reshape([a.shape[0],1]) dataset[p,:] = a[u,:] p = np.where(f['mapping/cellResults/materialpoint']['Name'] == str.encode(label))[0] if len(p)>0: u = (f['mapping/cellResults/materialpoint']['Position'][p.tolist()]) a = np.array(f[pa]) if len(a.shape) == 1: a=a.reshape([a.shape[0],1]) dataset[p,:] = a[u,:] if plain and dataset.dtype.names is not None: return dataset.view(('float64',len(dataset.dtype.names))) else: return dataset def cell_coordinates(self): """Return initial coordinates of the cell centers.""" if self.structured: delta = self.size/self.grid*0.5 z, y, x = np.meshgrid(np.linspace(delta[2],self.size[2]-delta[2],self.grid[2]), np.linspace(delta[1],self.size[1]-delta[1],self.grid[1]), np.linspace(delta[0],self.size[0]-delta[0],self.grid[0]), ) return np.concatenate((x[:,:,:,None],y[:,:,:,None],z[:,:,:,None]),axis = 3).reshape([np.product(self.grid),3]) else: with h5py.File(self.fname,'r') as f: return f['geometry/x_c'][()] def add_absolute(self,x): """ Add absolute value. Parameters ---------- x : str Label of the dataset containing a scalar, vector, or tensor. """ def __add_absolute(x): return { 'data': np.abs(x['data']), 'label': '|{}|'.format(x['label']), 'meta': { 'Unit': x['meta']['Unit'], 'Description': 'Absolute value of {} ({})'.format(x['label'],x['meta']['Description']), 'Creator': 'dadf5.py:add_abs v{}'.format(version) } } requested = [{'label':x,'arg':'x'}] self.__add_generic_pointwise(__add_absolute,requested) def add_calculation(self,formula,label,unit='n/a',description=None,vectorized=True): """ Add result of a general formula. Parameters ---------- formula : str Formula, refer to datasets by ‘#Label#‘. label : str Label of the dataset containing the result of the calculation. unit : str, optional Physical unit of the result. description : str, optional Human readable description of the result. vectorized : bool, optional Indicate whether the formula is written in vectorized form. Default is ‘True’. """ if vectorized is not True: raise NotImplementedError def __add_calculation(**kwargs): formula = kwargs['formula'] for d in re.findall(r'#(.*?)#',formula): formula = formula.replace('#{}#'.format(d),"kwargs['{}']['data']".format(d)) return { 'data': eval(formula), 'label': kwargs['label'], 'meta': { 'Unit': kwargs['unit'], 'Description': '{} (formula: {})'.format(kwargs['description'],kwargs['formula']), 'Creator': 'dadf5.py:add_calculation v{}'.format(version) } } requested = [{'label':d,'arg':d} for d in set(re.findall(r'#(.*?)#',formula))] # datasets used in the formula pass_through = {'formula':formula,'label':label,'unit':unit,'description':description} self.__add_generic_pointwise(__add_calculation,requested,pass_through) def add_Cauchy(self,P='P',F='F'): """ Add Cauchy stress calculated from 1. Piola-Kirchhoff stress and deformation gradient. Parameters ---------- P : str, optional Label of the dataset containing the 1. Piola-Kirchhoff stress. Default value is ‘P’. F : str, optional Label of the dataset containing the deformation gradient. Default value is ‘F’. """ def __add_Cauchy(F,P): return { 'data': mechanics.Cauchy(F['data'],P['data']), 'label': 'sigma', 'meta': { 'Unit': P['meta']['Unit'], 'Description': 'Cauchy stress calculated from {} ({}) '.format(P['label'],P['meta']['Description'])+\ 'and deformation gradient {} ({})'.format(F['label'],F['meta']['Description']), 'Creator': 'dadf5.py:add_Cauchy v{}'.format(version) } } requested = [{'label':F,'arg':'F'}, {'label':P,'arg':'P'} ] self.__add_generic_pointwise(__add_Cauchy,requested) def add_determinant(self,x): """ Add the determinant of a tensor. Parameters ---------- x : str Label of the dataset containing a tensor. """ def __add_determinant(x): return { 'data': np.linalg.det(x['data']), 'label': 'det({})'.format(x['label']), 'meta': { 'Unit': x['meta']['Unit'], 'Description': 'Determinant of tensor {} ({})'.format(x['label'],x['meta']['Description']), 'Creator': 'dadf5.py:add_determinant v{}'.format(version) } } requested = [{'label':x,'arg':'x'}] self.__add_generic_pointwise(__add_determinant,requested) def add_deviator(self,x): """ Add the deviatoric part of a tensor. Parameters ---------- x : str Label of the dataset containing a tensor. """ def __add_deviator(x): if not np.all(np.array(x['data'].shape[1:]) == np.array([3,3])): raise ValueError return { 'data': mechanics.deviatoric_part(x['data']), 'label': 's_{}'.format(x['label']), 'meta': { 'Unit': x['meta']['Unit'], 'Description': 'Deviator of tensor {} ({})'.format(x['label'],x['meta']['Description']), 'Creator': 'dadf5.py:add_deviator v{}'.format(version) } } requested = [{'label':x,'arg':'x'}] self.__add_generic_pointwise(__add_deviator,requested) def add_maximum_shear(self,x): """ Add maximum shear components of symmetric tensor. Parameters ---------- x : str Label of the dataset containing a symmetric tensor. """ def __add_maximum_shear(x): return { 'data': mechanics.maximum_shear(x['data']), 'label': 'max_shear({})'.format(x['label']), 'meta': { 'Unit': x['meta']['Unit'], 'Description': 'Maximum shear component of of {} ({})'.format(x['label'],x['meta']['Description']), 'Creator': 'dadf5.py:add_maximum_shear v{}'.format(version) } } requested = [{'label':x,'arg':'x'}] self.__add_generic_pointwise(__add_maximum_shear,requested) def add_Mises(self,x): """ Add the equivalent Mises stress or strain of a symmetric tensor. Parameters ---------- x : str Label of the dataset containing a symmetric stress or strain tensor. """ def __add_Mises(x): t = 'strain' if x['meta']['Unit'] == '1' else \ 'stress' return { 'data': mechanics.Mises_strain(x['data']) if t=='strain' else mechanics.Mises_stress(x['data']), 'label': '{}_vM'.format(x['label']), 'meta': { 'Unit': x['meta']['Unit'], 'Description': 'Mises equivalent {} of {} ({})'.format(t,x['label'],x['meta']['Description']), 'Creator': 'dadf5.py:add_Mises v{}'.format(version) } } requested = [{'label':x,'arg':'x'}] self.__add_generic_pointwise(__add_Mises,requested) def add_norm(self,x,ord=None): """ Add the norm of vector or tensor. Parameters ---------- x : str Label of the dataset containing a vector or tensor. 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 __add_norm(x,ord): o = ord if len(x['data'].shape) == 2: axis = 1 t = 'vector' if o is None: o = 2 elif len(x['data'].shape) == 3: axis = (1,2) t = 'tensor' if o is None: o = 'fro' else: raise ValueError return { 'data': np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True), 'label': '|{}|_{}'.format(x['label'],o), 'meta': { 'Unit': x['meta']['Unit'], 'Description': '{}-Norm of {} {} ({})'.format(ord,t,x['label'],x['meta']['Description']), 'Creator': 'dadf5.py:add_norm v{}'.format(version) } } requested = [{'label':x,'arg':'x'}] self.__add_generic_pointwise(__add_norm,requested,{'ord':ord}) def add_principal_components(self,x): """ Add principal components of symmetric tensor. The principal components are sorted in descending order, each repeated according to its multiplicity. Parameters ---------- x : str Label of the dataset containing a symmetric tensor. """ def __add_principal_components(x): return { 'data': mechanics.principal_components(x['data']), 'label': 'lambda_{}'.format(x['label']), 'meta': { 'Unit': x['meta']['Unit'], 'Description': 'Pricipal components of {} ({})'.format(x['label'],x['meta']['Description']), 'Creator': 'dadf5.py:add_principal_components v{}'.format(version) } } requested = [{'label':x,'arg':'x'}] self.__add_generic_pointwise(__add_principal_components,requested) def add_spherical(self,x): """ Add the spherical (hydrostatic) part of a tensor. Parameters ---------- x : str Label of the dataset containing a tensor. """ def __add_spherical(x): if not np.all(np.array(x['data'].shape[1:]) == np.array([3,3])): raise ValueError return { 'data': mechanics.spherical_part(x['data']), 'label': 'p_{}'.format(x['label']), 'meta': { 'Unit': x['meta']['Unit'], 'Description': 'Spherical component of tensor {} ({})'.format(x['label'],x['meta']['Description']), 'Creator': 'dadf5.py:add_spherical v{}'.format(version) } } requested = [{'label':x,'arg':'x'}] self.__add_generic_pointwise(__add_spherical,requested) def add_strain_tensor(self,F='F',t='U',m=0): """ Add strain tensor calculated from a deformation gradient. For details refer to damask.mechanics.strain_tensor Parameters ---------- F : str, optional Label of the dataset containing the deformation gradient. Default value is ‘F’. t : {‘V’, ‘U’}, optional Type of the polar decomposition, ‘V’ for right stretch tensor and ‘U’ for left stretch tensor. Defaults value is ‘U’. m : float, optional Order of the strain calculation. Default value is ‘0.0’. """ def __add_strain_tensor(F,t,m): return { 'data': mechanics.strain_tensor(F['data'],t,m), 'label': 'epsilon_{}^{}({})'.format(t,m,F['label']), 'meta': { 'Unit': F['meta']['Unit'], 'Description': 'Strain tensor of {} ({})'.format(F['label'],F['meta']['Description']), 'Creator': 'dadf5.py:add_strain_tensor v{}'.format(version) } } requested = [{'label':F,'arg':'F'}] self.__add_generic_pointwise(__add_strain_tensor,requested,{'t':t,'m':m}) def __add_generic_pointwise(self,func,datasets_requested,extra_args={}): """ General function to add pointwise data. Parameters ---------- func : function Function that calculates a new dataset from one or more datasets per HDF5 group. datasets_requested : list of dictionaries Details of the datasets to be used: label (in HDF5 file) and arg (argument to which the data is parsed in func). extra_args : dictionary, optional Any extra arguments parsed to func. """ def job(args): """Call function with input data + extra arguments, returns results + group.""" args['results'].put({**args['func'](**args['in']),'group':args['group']}) N_threads = 1 # ToDo: should be a parameter results = Queue(N_threads) pool = util.ThreadPool(N_threads) N_added = N_threads + 1 todo = [] # ToDo: It would be more memory efficient to read only from file when required, i.e. do to it in pool.add_task for group in self.groups_with_datasets([d['label'] for d in datasets_requested]): with h5py.File(self.fname,'r') as f: datasets_in = {} for d in datasets_requested: loc = f[group+'/'+d['label']] data = loc[()] meta = {k:loc.attrs[k].decode() for k in loc.attrs.keys()} datasets_in[d['arg']] = {'data': data, 'meta' : meta, 'label' : d['label']} todo.append({'in':{**datasets_in,**extra_args},'func':func,'group':group,'results':results}) pool.map(job, todo[:N_added]) # initialize N_not_calculated = len(todo) while N_not_calculated > 0: result = results.get() with h5py.File(self.fname,'a') as f: # write to file dataset_out = f[result['group']].create_dataset(result['label'],data=result['data']) for k in result['meta'].keys(): dataset_out.attrs[k] = result['meta'][k].encode() N_not_calculated-=1 if N_added < len(todo): # add more jobs pool.add_task(job,todo[N_added]) N_added +=1 pool.wait_completion() def to_vtk(self,labels,mode='Cell'): """ Export to vtk cell/point data. Parameters ---------- labels : list of str Labels of the datasets to be exported. mode : str, either 'Cell' or 'Point' Export in cell format or point format. Default value is 'Cell'. """ if mode=='Cell': if self.structured: coordArray = [vtk.vtkDoubleArray(),vtk.vtkDoubleArray(),vtk.vtkDoubleArray()] for dim in [0,1,2]: for c in np.linspace(0,self.size[dim],1+self.grid[dim]): coordArray[dim].InsertNextValue(c) vtk_geom = vtk.vtkRectilinearGrid() vtk_geom.SetDimensions(*(self.grid+1)) vtk_geom.SetXCoordinates(coordArray[0]) vtk_geom.SetYCoordinates(coordArray[1]) vtk_geom.SetZCoordinates(coordArray[2]) else: nodes = vtk.vtkPoints() with h5py.File(self.fname) as f: nodes.SetData(numpy_support.numpy_to_vtk(f['/geometry/x_n'][()],deep=True)) vtk_geom = vtk.vtkUnstructuredGrid() vtk_geom.SetPoints(nodes) vtk_geom.Allocate(f['/geometry/T_c'].shape[0]) for i in f['/geometry/T_c']: vtk_geom.InsertNextCell(vtk.VTK_HEXAHEDRON,8,i-1) # not for all elements! elif mode == 'Point': Points = vtk.vtkPoints() Vertices = vtk.vtkCellArray() for c in self.cell_coordinates(): pointID = Points.InsertNextPoint(c) Vertices.InsertNextCell(1) Vertices.InsertCellPoint(pointID) vtk_geom = vtk.vtkPolyData() vtk_geom.SetPoints(Points) vtk_geom.SetVerts(Vertices) vtk_geom.Modified() N_digits = int(np.floor(np.log10(int(self.increments[-1][3:]))))+1 for i,inc in enumerate(self.iter_visible('increments')): vtk_data = [] materialpoints_backup = self.visible['materialpoints'].copy() self.set_visible('materialpoints',False) for label in labels: for p in self.iter_visible('con_physics'): if p != 'generic': for c in self.iter_visible('constituents'): x = self.get_dataset_location(label) if len(x) == 0: continue array = self.read_dataset(x,0) shape = [array.shape[0],np.product(array.shape[1:])] vtk_data.append(numpy_support.numpy_to_vtk(num_array=array.reshape(shape), deep=True,array_type= vtk.VTK_DOUBLE)) vtk_data[-1].SetName('1_'+x[0].split('/',1)[1]) #ToDo: hard coded 1! vtk_geom.GetCellData().AddArray(vtk_data[-1]) else: x = self.get_dataset_location(label) if len(x) == 0: continue array = self.read_dataset(x,0) shape = [array.shape[0],np.product(array.shape[1:])] vtk_data.append(numpy_support.numpy_to_vtk(num_array=array.reshape(shape), deep=True,array_type= vtk.VTK_DOUBLE)) ph_name = re.compile(r'(?<=(constituent\/))(.*?)(?=(generic))') # identify phase name dset_name = '1_' + re.sub(ph_name,r'',x[0].split('/',1)[1]) # removing phase name vtk_data[-1].SetName(dset_name) vtk_geom.GetCellData().AddArray(vtk_data[-1]) self.set_visible('materialpoints',materialpoints_backup) constituents_backup = self.visible['constituents'].copy() self.set_visible('constituents',False) for label in labels: for p in self.iter_visible('mat_physics'): if p != 'generic': for m in self.iter_visible('materialpoints'): x = self.get_dataset_location(label) if len(x) == 0: continue array = self.read_dataset(x,0) shape = [array.shape[0],np.product(array.shape[1:])] vtk_data.append(numpy_support.numpy_to_vtk(num_array=array.reshape(shape), deep=True,array_type= vtk.VTK_DOUBLE)) vtk_data[-1].SetName('1_'+x[0].split('/',1)[1]) #ToDo: why 1_? vtk_geom.GetCellData().AddArray(vtk_data[-1]) else: x = self.get_dataset_location(label) if len(x) == 0: continue array = self.read_dataset(x,0) shape = [array.shape[0],np.product(array.shape[1:])] vtk_data.append(numpy_support.numpy_to_vtk(num_array=array.reshape(shape), deep=True,array_type= vtk.VTK_DOUBLE)) vtk_data[-1].SetName('1_'+x[0].split('/',1)[1]) vtk_geom.GetCellData().AddArray(vtk_data[-1]) self.set_visible('constituents',constituents_backup) if mode=='Cell': writer = vtk.vtkXMLRectilinearGridWriter() if self.structured else \ vtk.vtkXMLUnstructuredGridWriter() x = self.get_dataset_location('u_n') vtk_data.append(numpy_support.numpy_to_vtk(num_array=self.read_dataset(x,0), deep=True,array_type=vtk.VTK_DOUBLE)) vtk_data[-1].SetName('u') vtk_geom.GetPointData().AddArray(vtk_data[-1]) elif mode == 'Point': writer = vtk.vtkXMLPolyDataWriter() file_out = '{}_inc{}.{}'.format(os.path.splitext(os.path.basename(self.fname))[0], inc[3:].zfill(N_digits), writer.GetDefaultFileExtension()) writer.SetCompressorTypeToZLib() writer.SetDataModeToBinary() writer.SetFileName(file_out) writer.SetInputData(vtk_geom) writer.Write()