DAMASK_EICMD/python/damask/result.py

import multiprocessing
import re
import glob
import os
from functools import partial

import h5py
import numpy as np

from . import VTK
from . import Table
from . import Rotation
from . import Orientation
from . import Environment
from . import grid_filters
from . import mechanics
from . import util
from . import version


class Result:
    """
    Read and write to DADF5 files.

    DADF5 (DAMASK HDF5) files contain DAMASK results.
    """

    def __init__(self,fname):
        """
        Open 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 <= 6:
              raise TypeError('Unsupported DADF5 version {}.{} '.format(self.version_major,
                                                                        self.version_minor))

          self.structured = 'grid' in f['geometry'].attrs.keys()

          if self.structured:
              self.grid   = f['geometry'].attrs['grid']
              self.size   = f['geometry'].attrs['size']
              self.origin = f['geometry'].attrs['origin'] if self.version_major == 0 and self.version_minor >= 5 else \
                            np.zeros(3)

          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.selection= {'increments':     self.increments,
                         'constituents':   self.constituents,'materialpoints': self.materialpoints,
                         'con_physics':    self.con_physics, 'mat_physics':    self.mat_physics
                        }

        self.fname = fname


    def __repr__(self):
        """Show selected data."""
        all_selected_increments = self.selection['increments']
        self.pick('increments',all_selected_increments[0:1])
        first = self.list_data()
        self.pick('increments',all_selected_increments[-1:])
        last  = self.list_data()
        self.pick('increments',all_selected_increments)
        in_between = ''.join(['\n{}\n  ...\n'.format(inc) for inc in all_selected_increments[1:-2]])
        return util.srepr(first+ in_between + last)


    def _manage_selection(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.selection)
        datasets : list of str or Boolean
           name of datasets as list, supports ? and * wildcards.
            True is equivalent to [*], False is equivalent to []

        """
        # allow True/False and string arguments
        if datasets is True:
            datasets = ['*']
        elif datasets is False:
            datasets = []
        choice = datasets if hasattr(datasets,'__iter__') and not isinstance(datasets,str) else \
                [datasets]

        if   what == 'increments':
            choice = [c if isinstance(c,str) and c.startswith('inc') else
                      'inc{}'.format(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   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 = [e for e_ in [glob.fnmatch.filter(getattr(self,what),s) for s in choice] for e in e_]
        existing = set(self.selection[what])

        if   action == 'set':
            self.selection[what] = valid
        elif action == 'add':
            add=existing.union(valid)
            add_sorted=sorted(add, key=lambda x: int("".join([i for i in x if i.isdigit()])))
            self.selection[what] = add_sorted
        elif action == 'del':
            diff=existing.difference(valid)
            diff_sorted=sorted(diff, key=lambda x: int("".join([i for i in x if i.isdigit()])))
            self.selection[what] = diff_sorted


    def incs_in_range(self,start,end):
        selected = []
        for i,inc in enumerate([int(i[3:]) for i in self.increments]):
            s,e = map(lambda x: int(x[3:] 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):
        selected = []
        for i,time in enumerate(self.times):
            if start <= time <= end:
                selected.append(self.times[i])
        return selected


    def iterate(self,what):
        """
        Iterate over selection items by setting each one selected.

        Parameters
        ----------
        what : str
            attribute to change (must be from self.selection)

        """
        datasets = self.selection[what]
        last_selection = datasets.copy()
        for dataset in datasets:
            if last_selection != self.selection[what]:
                self._manage_selection('set',what,datasets)
                raise Exception
            self._manage_selection('set',what,dataset)
            last_selection = self.selection[what]
            yield dataset
        self._manage_selection('set',what,datasets)


    def pick(self,what,datasets):
        """
        Set selection.

        Parameters
        ----------
        what : str
            attribute to change (must be from self.selection)
        datasets : list of str or Boolean
            name of datasets as list, supports ? and * wildcards.
            True is equivalent to [*], False is equivalent to []

        """
        self._manage_selection('set',what,datasets)


    def pick_more(self,what,datasets):
        """
        Add to selection.

        Parameters
        ----------
        what : str
            attribute to change (must be from self.selection)
        datasets : list of str or Boolean
            name of datasets as list, supports ? and * wildcards.
            True is equivalent to [*], False is equivalent to []

        """
        self._manage_selection('add',what,datasets)


    def pick_less(self,what,datasets):
        """
        Delete from selection.

        Parameters
        ----------
        what : str
            attribute to change (must be from self.selection)
        datasets : list of str or Boolean
            name of datasets as list, supports ? and * wildcards.
            True is equivalent to [*], False is equivalent to []

        """
        self._manage_selection('del',what,datasets)

  # def datamerger(regular expression to filter groups into one copy)


    def place(self,datasets,component=0,tagged=False,split=True):
        """
        Distribute datasets onto geometry and return Table or (split) dictionary of Tables.

        Must not mix nodal end cell data.

        Only data within
        - inc?????/constituent/*_*/*
        - inc?????/materialpoint/*_*/*
        - inc?????/geometry/*
        are considered.

        Parameters
        ----------
          datasets : iterable or str
          component : int
              homogenization component to consider for constituent data
          tagged : Boolean
              tag Table.column name with '#component'
              defaults to False
          split : Boolean
              split Table by increment and return dictionary of Tables
              defaults to True

        """
        sets = datasets if hasattr(datasets,'__iter__') and not isinstance(datasets,str) \
         else [datasets]
        tag = f'#{component}' if tagged else ''
        tbl = {} if split else None
        inGeom = {}
        inData = {}
        with h5py.File(self.fname,'r') as f:
            for dataset in sets:
                for group in self.groups_with_datasets(dataset):
                    path = os.path.join(group,dataset)
                    inc,prop,name,cat,item = (path.split('/') + ['']*5)[:5]
                    key = '/'.join([prop,name+tag])
                    if key not in inGeom:
                        if prop == 'geometry':
                            inGeom[key] = inData[key] = np.arange(self.Nmaterialpoints)
                        elif prop == 'constituent':
                            inGeom[key] = np.where(f['mapping/cellResults/constituent'][:,component]['Name'] == str.encode(name))[0]
                            inData[key] =          f['mapping/cellResults/constituent'][inGeom[key],component]['Position']
                        else:
                            inGeom[key] = np.where(f['mapping/cellResults/materialpoint']['Name'] == str.encode(name))[0]
                            inData[key] =          f['mapping/cellResults/materialpoint'][inGeom[key].tolist()]['Position']
                    shape = np.shape(f[path])
                    data = np.full((self.Nmaterialpoints,) + (shape[1:] if len(shape)>1 else (1,)),
                                    np.nan,
                                    dtype=np.dtype(f[path]))
                    data[inGeom[key]] = (f[path] if len(shape)>1 else np.expand_dims(f[path],1))[inData[key]]
                    path = (os.path.join(*([prop,name]+([cat] if cat else [])+([item] if item else []))) if split else path)+tag
                    if split:
                        try:
                            tbl[inc].add(path,data)
                        except KeyError:
                            tbl[inc] = Table(data.reshape(self.Nmaterialpoints,-1),{path:data.shape[1:]})
                    else:
                        try:
                            tbl.add(path,data)
                        except AttributeError:
                            tbl = Table(data.reshape(self.Nmaterialpoints,-1),{path:data.shape[1:]})

        return tbl


    def groups_with_datasets(self,datasets):
        """
        Return groups that contain all requested datasets.

        Only groups within
          - inc?????/constituent/*_*/*
          - inc?????/materialpoint/*_*/*
          - inc?????/geometry/*
        are considered as they contain user-relevant data.
        Single strings will be treated as list with one entry.

        Wild card matching is allowed, but the number of arguments need to fit.

        Parameters
        ----------
            datasets : iterable or str or 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,bool) or (hasattr(datasets,'__iter__') and not isinstance(datasets,str)) else \
              [datasets]

        groups = []

        with h5py.File(self.fname,'r') as f:
            for i in self.iterate('increments'):
                for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
                  for oo in self.iterate(o):
                    for pp in self.iterate(p):
                      group = '/'.join([i,o[:-1],oo,pp])                                            # o[:-1]: plural/singular issue
                      if sets is True:
                        groups.append(group)
                      else:
                        match = [e for e_ in [glob.fnmatch.filter(f[group].keys(),s) for s in sets] for e in e_]
                        if len(set(match)) == len(sets) : groups.append(group)
        return groups


    def list_data(self):
        """Return information on all active datasets in the file."""
        message = ''
        with h5py.File(self.fname,'r') as f:
            for i in self.iterate('increments'):
                message+='\n{} ({}s)\n'.format(i,self.times[self.increments.index(i)])
                for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
                  for oo in self.iterate(o):
                    message+='  {}\n'.format(oo)
                    for pp in self.iterate(p):
                      message+='    {}\n'.format(pp)
                      group = '/'.join([i,o[:-1],oo,pp])                                            # o[:-1]: plural/singular issue
                      for d in f[group].keys():
                        try:
                          dataset = f['/'.join([group,d])]
                          message+='      {} / ({}): {}\n'.\
                                    format(d,dataset.attrs['Unit'].decode(),dataset.attrs['Description'].decode())
                        except KeyError:
                          pass
        return message


    def get_dataset_location(self,label):
        """Return the location of all active datasets with given label."""
        path = []
        with h5py.File(self.fname,'r') as f:
            for i in self.iterate('increments'):
                k = '/'.join([i,'geometry',label])
                try:
                    f[k]
                    path.append(k)
                except KeyError as e:
                    pass
                for o,p in zip(['constituents','materialpoints'],['con_physics','mat_physics']):
                    for oo in self.iterate(o):
                        for pp in self.iterate(p):
                            k = '/'.join([i,o[:-1],oo,pp,label])
                            try:
                                f[k]
                                path.append(k)
                            except KeyError 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:
            return grid_filters.cell_coord0(self.grid,self.size,self.origin).reshape(-1,3)
        else:
            with h5py.File(self.fname,'r') as f:
                return f['geometry/x_c'][()]


    @staticmethod
    def _add_absolute(x):
        return {
                'data':  np.abs(x['data']),
                'label': '|{}|'.format(x['label']),
                'meta':  {
                          'Unit':        x['meta']['Unit'],
                          'Description': 'Absolute value of {} ({})'.format(x['label'],x['meta']['Description']),
                          'Creator':     'result.py:add_abs v{}'.format(version)
                          }
                 }
    def add_absolute(self,x):
        """
        Add absolute value.

        Parameters
        ----------
        x : str
            Label of scalar, vector, or tensor dataset to take absolute value of.

        """
        self._add_generic_pointwise(self._add_absolute,{'x':x})


    @staticmethod
    def _add_calculation(**kwargs):
        formula = kwargs['formula']
        for d in re.findall(r'#(.*?)#',formula):
            formula = formula.replace('#{}#'.format(d),"kwargs['{}']['data']".format(d))

        return {
                'data':  eval(formula),
                'label': kwargs['label'],
                'meta':  {
                          'Unit':        kwargs['unit'],
                          'Description': '{} (formula: {})'.format(kwargs['description'],kwargs['formula']),
                          'Creator':     'result.py:add_calculation v{}'.format(version)
                          }
                 }
    def add_calculation(self,label,formula,unit='n/a',description=None,vectorized=True):
        """
        Add result of a general formula.

        Parameters
        ----------
        label : str
          Label of resulting dataset.
        formula : str
            Formula to calculate resulting dataset. Existing datasets are referenced by ‘#TheirLabel#‘.
        unit : str, optional
            Physical unit of the result.
        description : str, optional
            Human-readable description of the result.
        vectorized : bool, optional
            Indicate whether the formula can be used in vectorized form. Defaults to ‘True’.

        """
        if not vectorized:
          raise NotImplementedError

        dataset_mapping  = {d:d for d in set(re.findall(r'#(.*?)#',formula))}                       # datasets used in the formula
        args             = {'formula':formula,'label':label,'unit':unit,'description':description}
        self._add_generic_pointwise(self._add_calculation,dataset_mapping,args)


    @staticmethod
    def _add_Cauchy(P,F):
        return {
                'data':  mechanics.Cauchy(P['data'],F['data']),
                'label': 'sigma',
                'meta':  {
                          'Unit':        P['meta']['Unit'],
                          'Description': 'Cauchy stress calculated from {} ({}) '.format(P['label'],
                                                                                         P['meta']['Description'])+\
                                         'and {} ({})'.format(F['label'],F['meta']['Description']),
                          'Creator':     'result.py:add_Cauchy v{}'.format(version)
                          }
                }
    def add_Cauchy(self,P='P',F='F'):
        """
        Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.

        Parameters
        ----------
        P : str, optional
            Label of the dataset containing the first Piola-Kirchhoff stress. Defaults to ‘P’.
        F : str, optional
            Label of the dataset containing the deformation gradient. Defaults to ‘F’.

        """
        self._add_generic_pointwise(self._add_Cauchy,{'P':P,'F':F})


    @staticmethod
    def _add_determinant(T):
        return {
                'data':  np.linalg.det(T['data']),
                'label': 'det({})'.format(T['label']),
                'meta':  {
                          'Unit':        T['meta']['Unit'],
                          'Description': 'Determinant of tensor {} ({})'.format(T['label'],T['meta']['Description']),
                          'Creator':     'result.py:add_determinant v{}'.format(version)
                          }
                }
    def add_determinant(self,T):
        """
        Add the determinant of a tensor.

        Parameters
        ----------
        T : str
            Label of tensor dataset.

        """
        self._add_generic_pointwise(self._add_determinant,{'T':T})


    @staticmethod
    def _add_deviator(T):
        if not T['data'].shape[1:] == (3,3):
            raise ValueError

        return {
                'data':  mechanics.deviatoric_part(T['data']),
                'label': 's_{}'.format(T['label']),
                'meta':  {
                          'Unit':        T['meta']['Unit'],
                          'Description': 'Deviator of tensor {} ({})'.format(T['label'],T['meta']['Description']),
                          'Creator':     'result.py:add_deviator v{}'.format(version)
                          }
                 }
    def add_deviator(self,T):
        """
        Add the deviatoric part of a tensor.

        Parameters
        ----------
        T : str
            Label of tensor dataset.

        """
        self._add_generic_pointwise(self._add_deviator,{'T':T})


    @staticmethod
    def _add_eigenvalue(T_sym):
        return {
                'data': mechanics.eigenvalues(T_sym['data']),
                'label': 'lambda({})'.format(T_sym['label']),
                'meta' : {
                          'Unit':         T_sym['meta']['Unit'],
                          'Description': 'Eigenvalues of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
                          'Creator':     'result.py:add_eigenvalues v{}'.format(version)
                         }
                }
    def add_eigenvalues(self,T_sym):
        """
        Add eigenvalues of symmetric tensor.

        Parameters
        ----------
        T_sym : str
            Label of symmetric tensor dataset.

        """
        self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym})


    @staticmethod
    def _add_eigenvector(T_sym):
        return {
                'data': mechanics.eigenvectors(T_sym['data']),
                'label': 'v({})'.format(T_sym['label']),
                'meta' : {
                          'Unit':        '1',
                          'Description': 'Eigenvectors of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
                          'Creator':     'result.py:add_eigenvectors v{}'.format(version)
                         }
                }
    def add_eigenvectors(self,T_sym):
        """
        Add eigenvectors of symmetric tensor.

        Parameters
        ----------
        T_sym : str
            Label of symmetric tensor dataset.

        """
        self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym})


    @staticmethod
    def _add_IPFcolor(q,l):
        d      = np.array(l)
        d_unit = d/np.linalg.norm(d)
        m      = util.scale_to_coprime(d)
        colors = np.empty((len(q['data']),3),np.uint8)

        lattice   = q['meta']['Lattice']

        for i,q in enumerate(q['data']):
             o = Orientation(np.array([q['w'],q['x'],q['y'],q['z']]),lattice).reduced()
             colors[i] = np.uint8(o.IPFcolor(d_unit)*255)

        return {
                'data': colors,
                'label': 'IPFcolor_[{} {} {}]'.format(*m),
                'meta' : {
                          'Unit':        'RGB (8bit)',
                          'Lattice':     lattice,
                          'Description': 'Inverse Pole Figure (IPF) colors along sample direction [{} {} {}]'.format(*m),
                          'Creator':     'result.py:add_IPFcolor v{}'.format(version)
                         }
               }
    def add_IPFcolor(self,q,l):
        """
        Add RGB color tuple of inverse pole figure (IPF) color.

        Parameters
        ----------
        q : str
            Label of the dataset containing the crystallographic orientation as quaternions.
        l : numpy.array of shape (3)
            Lab frame direction for inverse pole figure.

        """
        self._add_generic_pointwise(self._add_IPFcolor,{'q':q},{'l':l})


    @staticmethod
    def _add_maximum_shear(T_sym):
        return {
                'data':  mechanics.maximum_shear(T_sym['data']),
                'label': 'max_shear({})'.format(T_sym['label']),
                'meta':  {
                          'Unit':        T_sym['meta']['Unit'],
                          'Description': 'Maximum shear component of {} ({})'.format(T_sym['label'],T_sym['meta']['Description']),
                          'Creator':     'result.py:add_maximum_shear v{}'.format(version)
                          }
                 }
    def add_maximum_shear(self,T_sym):
        """
        Add maximum shear components of symmetric tensor.

        Parameters
        ----------
        T_sym : str
            Label of symmetric tensor dataset.

        """
        self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym})


    @staticmethod
    def _add_Mises(T_sym):
        t = 'strain' if T_sym['meta']['Unit'] == '1' else \
            'stress'

        return {
                'data':  mechanics.Mises_strain(T_sym['data']) if t=='strain' else mechanics.Mises_stress(T_sym['data']),
                'label': '{}_vM'.format(T_sym['label']),
                'meta':  {
                          'Unit':        T_sym['meta']['Unit'],
                          'Description': 'Mises equivalent {} of {} ({})'.format(t,T_sym['label'],T_sym['meta']['Description']),
                          'Creator':     'result.py:add_Mises v{}'.format(version)
                          }
                }
    def add_Mises(self,T_sym):
        """
        Add the equivalent Mises stress or strain of a symmetric tensor.

        Parameters
        ----------
        T_sym : str
            Label of symmetric tensorial stress or strain dataset.

        """
        self._add_generic_pointwise(self._add_Mises,{'T_sym':T_sym})


    @staticmethod
    def _add_norm(x,ord):
        o = ord
        if len(x['data'].shape) == 2:
            axis = 1
            t = 'vector'
            if o is None: o = 2
        elif len(x['data'].shape) == 3:
            axis = (1,2)
            t = 'tensor'
            if o is None: o = 'fro'
        else:
            raise ValueError

        return {
                'data':  np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True),
                'label': '|{}|_{}'.format(x['label'],o),
                'meta':  {
                          'Unit':        x['meta']['Unit'],
                          'Description': '{}-norm of {} {} ({})'.format(o,t,x['label'],x['meta']['Description']),
                          'Creator':     'result.py:add_norm v{}'.format(version)
                          }
                 }
    def add_norm(self,x,ord=None):
        """
        Add the norm of vector or tensor.

        Parameters
        ----------
        x : str
            Label of vector or tensor dataset.
        ord : {non-zero int, inf, -inf, ‘fro’, ‘nuc’}, optional
            Order of the norm. inf means NumPy’s inf object. For details refer to numpy.linalg.norm.

        """
        self._add_generic_pointwise(self._add_norm,{'x':x},{'ord':ord})


    @staticmethod
    def _add_PK2(P,F):
        return {
                'data':  mechanics.PK2(P['data'],F['data']),
                'label': 'S',
                'meta':  {
                          'Unit':        P['meta']['Unit'],
                          'Description': '2. Kirchhoff stress calculated from {} ({}) '.format(P['label'],
                                                                                               P['meta']['Description'])+\
                                         'and {} ({})'.format(F['label'],F['meta']['Description']),
                          'Creator':     'result.py:add_PK2 v{}'.format(version)
                          }
                }
    def add_PK2(self,P='P',F='F'):
        """
        Add second Piola-Kirchhoff calculated from first Piola-Kirchhoff stress and deformation gradient.

        Parameters
        ----------
        P : str, optional
            Label first  Piola-Kirchhoff stress dataset. Defaults to ‘P’.
        F : str, optional
            Label of deformation gradient dataset. Defaults to ‘F’.

        """
        self._add_generic_pointwise(self._add_PK2,{'P':P,'F':F})


    @staticmethod
    def _add_pole(q,p,polar):
        pole      = np.array(p)
        unit_pole = pole/np.linalg.norm(pole)
        m         = util.scale_to_coprime(pole)
        coords    = np.empty((len(q['data']),2))

        for i,q in enumerate(q['data']):
            o = Rotation(np.array([q['w'],q['x'],q['y'],q['z']]))
            rotatedPole = o*unit_pole                                                               # rotate pole according to crystal orientation
            (x,y) = rotatedPole[0:2]/(1.+abs(unit_pole[2]))                                         # stereographic projection
            coords[i] = [np.sqrt(x*x+y*y),np.arctan2(y,x)] if polar else [x,y]

        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' : 'result.py:add_pole v{}'.format(version)
                         }
               }
    def add_pole(self,q,p,polar=False):
        """
        Add coordinates of stereographic projection of given pole in crystal frame.

        Parameters
        ----------
        q : str
            Label of the dataset containing the crystallographic orientation as quaternions.
        p : numpy.array of shape (3)
            Crystallographic direction or plane.
        polar : bool, optional
            Give pole in polar coordinates. Defaults to False.

        """
        self._add_generic_pointwise(self._add_pole,{'q':q},{'p':p,'polar':polar})


    @staticmethod
    def _add_rotational_part(F):
        if not F['data'].shape[1:] == (3,3):
            raise ValueError
        return {
                'data':  mechanics.rotational_part(F['data']),
                'label': 'R({})'.format(F['label']),
                'meta':  {
                          'Unit':        F['meta']['Unit'],
                          'Description': 'Rotational part of {} ({})'.format(F['label'],F['meta']['Description']),
                          'Creator':     'result.py:add_rotational_part v{}'.format(version)
                          }
                 }
    def add_rotational_part(self,F):
        """
        Add rotational part of a deformation gradient.

        Parameters
        ----------
        F : str, optional
            Label of deformation gradient dataset.

        """
        self._add_generic_pointwise(self._add_rotational_part,{'F':F})


    @staticmethod
    def _add_spherical(T):
        if not T['data'].shape[1:] == (3,3):
            raise ValueError

        return {
                'data':  mechanics.spherical_part(T['data']),
                'label': 'p_{}'.format(T['label']),
                'meta':  {
                          'Unit':        T['meta']['Unit'],
                          'Description': 'Spherical component of tensor {} ({})'.format(T['label'],T['meta']['Description']),
                          'Creator':     'result.py:add_spherical v{}'.format(version)
                          }
                 }
    def add_spherical(self,T):
        """
        Add the spherical (hydrostatic) part of a tensor.

        Parameters
        ----------
        T : str
            Label of tensor dataset.

        """
        self._add_generic_pointwise(self._add_spherical,{'T':T})


    @staticmethod
    def _add_strain_tensor(F,t,m):
        if not F['data'].shape[1:] == (3,3):
            raise ValueError

        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':     'result.py:add_strain_tensor v{}'.format(version)
                          }
                 }
    def add_strain_tensor(self,F='F',t='V',m=0.0):
        """
        Add strain tensor of a deformation gradient.

        For details refer to damask.mechanics.strain_tensor

        Parameters
        ----------
        F : str, optional
            Label of deformation gradient dataset. Defaults to ‘F’.
        t : {‘V’, ‘U’}, optional
            Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor.
            Defaults to ‘V’.
        m : float, optional
            Order of the strain calculation. Defaults to ‘0.0’.

        """
        self._add_generic_pointwise(self._add_strain_tensor,{'F':F},{'t':t,'m':m})


    @staticmethod
    def _add_stretch_tensor(F,t):
        if not F['data'].shape[1:] == (3,3):
            raise ValueError

        return {
                'data':  mechanics.left_stretch(F['data']) if t == 'V' else mechanics.right_stretch(F['data']),
                'label': '{}({})'.format(t,F['label']),
                'meta':  {
                          'Unit':        F['meta']['Unit'],
                          'Description': '{} stretch tensor of {} ({})'.format('Left' if t == 'V' else 'Right',
                                                                               F['label'],F['meta']['Description']),
                          'Creator':     'result.py:add_stretch_tensor v{}'.format(version)
                          }
                 }
    def add_stretch_tensor(self,F='F',t='V'):
        """
        Add stretch tensor of a deformation gradient.

        Parameters
        ----------
        F : str, optional
            Label of deformation gradient dataset. Defaults to ‘F’.
        t : {‘V’, ‘U’}, optional
            Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor.
            Defaults to ‘V’.

        """
        self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})


    def _job(self,group,func,datasets,args,lock):
        """Execute job for _add_generic_pointwise."""
        try:
            datasets_in = {}
            lock.acquire()
            with h5py.File(self.fname,'r') as f:
                for arg,label in datasets.items():
                    loc  = f[group+'/'+label]
                    datasets_in[arg]={'data' :loc[()],
                                      'label':label,
                                      'meta': {k:v.decode() for k,v in loc.attrs.items()}}
            lock.release()
            r = func(**datasets_in,**args)
            return [group,r]
        except Exception as err:
            print('Error during calculation: {}.'.format(err))
            return None


    def _add_generic_pointwise(self,func,datasets,args={}):
        """
        General function to add pointwise data.

        Parameters
        ----------
        func : function
            Callback function that calculates a new dataset from one or more datasets per HDF5 group.
        datasets : dictionary
            Details of the datasets to be used: label (in HDF5 file) and arg (argument to which the data is parsed in func).
        args : dictionary, optional
            Arguments parsed to func.

        """
        pool = multiprocessing.Pool(int(Environment().options['DAMASK_NUM_THREADS']))
        lock = multiprocessing.Manager().Lock()

        groups = self.groups_with_datasets(datasets.values())
        default_arg = partial(self._job,func=func,datasets=datasets,args=args,lock=lock)

        for result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):
            if not result:
                continue
            lock.acquire()
            with h5py.File(self.fname, 'a') as f:
                try:
                    dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'])
                    for l,v in result[1]['meta'].items():
                        dataset.attrs[l]=v.encode()
                except OSError as err:
                    print('Could not add dataset: {}.'.format(err))
            lock.release()

        pool.close()
        pool.join()


    def to_vtk(self,labels=[],mode='cell'):
        """
        Export to vtk cell/point data.

        Parameters
        ----------
        labels : str or list of, optional
            Labels of the datasets to be exported.
        mode : str, either 'cell' or 'point'
            Export in cell format or point format.
            Defaults to 'cell'.

        """
        if mode.lower()=='cell':

          if self.structured:
              v = VTK.from_rectilinearGrid(self.grid,self.size,self.origin)
          else:
              with h5py.File(self.fname,'r') as f:
                  v = VTK.from_unstructuredGrid(f['/geometry/x_n'][()],
                                                f['/geometry/T_c'][()]-1,
                                                f['/geometry/T_c'].attrs['VTK_TYPE'].decode())
        elif mode.lower()=='point':
            v = VTK.from_polyData(self.cell_coordinates())

        N_digits = int(np.floor(np.log10(int(self.increments[-1][3:]))))+1

        for i,inc in enumerate(util.show_progress(self.iterate('increments'),len(self.selection['increments']))):

          materialpoints_backup = self.selection['materialpoints'].copy()
          self.pick('materialpoints',False)
          for label in (labels if isinstance(labels,list) else [labels]):
              for p in self.iterate('con_physics'):
                  if p != 'generic':
                      for c in self.iterate('constituents'):
                          x = self.get_dataset_location(label)
                          if len(x) == 0:
                              continue
                          array = self.read_dataset(x,0)
                          v.add(array,'1_'+x[0].split('/',1)[1]) #ToDo: hard coded 1!
              else:
                  x = self.get_dataset_location(label)
                  if len(x) == 0:
                      continue
                  array = self.read_dataset(x,0)
                  ph_name = re.compile(r'(?<=(constituent\/))(.*?)(?=(generic))')                   # identify  phase name
                  dset_name = '1_' + re.sub(ph_name,r'',x[0].split('/',1)[1])                       # removing phase name
                  v.add(array,dset_name)
          self.pick('materialpoints',materialpoints_backup)

          constituents_backup = self.selection['constituents'].copy()
          self.pick('constituents',False)
          for label in (labels if isinstance(labels,list) else [labels]):
              for p in self.iterate('mat_physics'):
                  if p != 'generic':
                      for m in self.iterate('materialpoints'):
                          x = self.get_dataset_location(label)
                          if len(x) == 0:
                              continue
                          array = self.read_dataset(x,0)
                          v.add(array,'1_'+x[0].split('/',1)[1]) #ToDo: why 1_?
              else:
                  x = self.get_dataset_location(label)
                  if len(x) == 0:
                      continue
                  array = self.read_dataset(x,0)
                  v.add(array,'1_'+x[0].split('/',1)[1])
          self.pick('constituents',constituents_backup)

          u = self.read_dataset(self.get_dataset_location('u_n' if mode.lower() == 'cell' else 'u_p'))
          v.add(u,'u')

          file_out = '{}_inc{}'.format(os.path.splitext(os.path.basename(self.fname))[0],
                                          inc[3:].zfill(N_digits))

          v.write(file_out)

###################################################################################################
# BEGIN DEPRECATED
    iter_visible   = iterate


    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_selection('set','increments',self._time_to_inc(start,end))