import os import copy import warnings import multiprocessing as mp from functools import partial import typing from typing import Optional, Union, TextIO, Sequence, Dict from pathlib import Path import numpy as np import pandas as pd import h5py from scipy import ndimage, spatial, interpolate from . import VTK from . import util from . import grid_filters from . import Rotation from . import Table from . import Colormap from ._typehints import FloatSequence, IntSequence, NumpyRngSeed try: import numba as nb # type: ignore except ImportError: nb = False def numba_njit_wrapper(**kwargs): return (lambda function: nb.njit(function) if nb else function) class GeomGrid: """ Geometry definition for grid solvers. Create and manipulate geometry definitions for storage as VTK ImageData files ('.vti' extension). A grid has a physical size, a coordinate origin, and contains the material ID (indexing an entry in 'material.yaml') as well as initial condition fields. """ def __init__(self, material: np.ndarray, size: FloatSequence, origin: FloatSequence = np.zeros(3), initial_conditions: Optional[Dict[str,np.ndarray]] = None, comments: Union[None, str, Sequence[str]] = None): """ New geometry definition for grid solvers. Parameters ---------- material : numpy.ndarray of int, shape (:,:,:) Material indices. The shape of the material array defines the number of cells. size : sequence of float, len (3) Physical size of grid in meter. origin : sequence of float, len (3), optional Coordinates of grid origin in meter. Defaults to [0.0,0.0,0.0]. initial_conditions : dictionary, optional Initial condition label and field values at each grid point. comments : (sequence of) str, optional Additional, human-readable information, e.g. history of operations. """ self.material = material self.size = size # type: ignore self.origin = origin # type: ignore self.initial_conditions = {} if initial_conditions is None else initial_conditions self.comments = [] if comments is None else \ [comments] if isinstance(comments,str) else \ [str(c) for c in comments] def __repr__(self) -> str: """ Return repr(self). Give short, human-readable summary. """ mat_min = np.nanmin(self.material) mat_max = np.nanmax(self.material) mat_N = self.N_materials return util.srepr([ f'cells: {util.srepr(self.cells, " × ")}', f'size: {util.srepr(self.size, " × ")} m³', f'origin: {util.srepr(self.origin," ")} m', f'# materials: {mat_N}' + ('' if mat_min == 0 and mat_max+1 == mat_N else f' (min: {mat_min}, max: {mat_max})') ]+(['initial_conditions:']+[f' - {f}' for f in self.initial_conditions] if self.initial_conditions else [])) def __copy__(self) -> 'GeomGrid': """ Return deepcopy(self). Create deep copy. """ return copy.deepcopy(self) copy = __copy__ def __eq__(self, other: object) -> bool: """ Return self==other. Test equality of other. Parameters ---------- other : damask.GeomGrid GeomGrid to compare self against. """ if not isinstance(other, GeomGrid): return NotImplemented return bool( np.allclose(other.size,self.size) and np.allclose(other.origin,self.origin) and np.all(other.cells == self.cells) and np.all(other.material == self.material)) @property def material(self) -> np.ndarray: """Material indices.""" return self._material @material.setter def material(self, material: np.ndarray): if len(material.shape) != 3: raise ValueError(f'invalid material shape {material.shape}') if material.dtype not in np.sctypes['float'] and material.dtype not in np.sctypes['int']: raise TypeError(f'invalid material data type "{material.dtype}"') self._material = np.copy(material) if self.material.dtype in np.sctypes['float'] and \ np.all(self.material == self.material.astype(np.int64).astype(float)): self._material = self.material.astype(np.int64) @property def size(self) -> np.ndarray: """Edge lengths of grid in meter.""" return self._size @size.setter def size(self, size: FloatSequence): if len(size) != 3 or any(np.array(size) < 0): raise ValueError(f'invalid size {size}') self._size = np.array(size) @property def origin(self) -> np.ndarray: """Vector to grid origin in meter.""" return self._origin @origin.setter def origin(self, origin: FloatSequence): if len(origin) != 3: raise ValueError(f'invalid origin {origin}') self._origin = np.array(origin) @property def initial_conditions(self) -> Dict[str,np.ndarray]: """Fields of initial conditions.""" self._ic = dict(zip(self._ic.keys(), # type: ignore [v if isinstance(v,np.ndarray) else np.broadcast_to(v,self.cells) for v in self._ic.values()])) # type: ignore return self._ic @initial_conditions.setter def initial_conditions(self, ic: Dict[str,np.ndarray]): if not isinstance(ic,dict): raise TypeError('initial conditions is not a dictionary') self._ic = ic @property def cells(self) -> np.ndarray: """Cell counts along x,y,z direction.""" return np.asarray(self.material.shape) @property def N_materials(self) -> int: """Number of (unique) material indices within grid.""" return np.unique(self.material).size @staticmethod def load(fname: Union[str, Path]) -> 'GeomGrid': """ Load from VTK ImageData file. Parameters ---------- fname : str or pathlib.Path GeomGrid file to read. Valid extension is .vti, which will be appended if not given. Returns ------- loaded : damask.GeomGrid GeomGrid-based geometry from file. """ v = VTK.load(fname if str(fname).endswith('.vti') else str(fname)+'.vti') cells = np.array(v.vtk_data.GetDimensions())-1 bbox = np.array(v.vtk_data.GetBounds()).reshape(3,2).T ic = {label:v.get(label).reshape(cells,order='F') for label in set(v.labels['Cell Data']) - {'material'}} return GeomGrid(material = v.get('material').reshape(cells,order='F'), size = bbox[1] - bbox[0], origin = bbox[0], initial_conditions = ic, comments = v.comments, ) @typing.no_type_check @staticmethod def load_ASCII(fname)-> 'GeomGrid': """ Load from geom file. Storing geometry files in ASCII format is deprecated. This function will be removed in a future version of DAMASK. Parameters ---------- fname : str, pathlib.Path, or file handle Geometry file to read. Returns ------- loaded : damask.GeomGrid GeomGrid-based geometry from file. """ warnings.warn('Support for ASCII-based geom format will be removed in DAMASK 3.0.0', DeprecationWarning,2) if isinstance(fname, (str, Path)): f = open(fname) elif isinstance(fname, TextIO): f = fname else: raise TypeError f.seek(0) try: header_length_,keyword = f.readline().split()[:2] header_length = int(header_length_) except ValueError: header_length,keyword = (-1, 'invalid') if not keyword.startswith('head') or header_length < 3: raise TypeError('invalid or missing header length information') comments = [] content = f.readlines() for i,line in enumerate(content[:header_length]): items = line.split('#')[0].lower().strip().split() if (key := items[0] if items else '') == 'grid': cells = np.array([ int(dict(zip(items[1::2],items[2::2]))[i]) for i in ['a','b','c']]) elif key == 'size': size = np.array([float(dict(zip(items[1::2],items[2::2]))[i]) for i in ['x','y','z']]) elif key == 'origin': origin = np.array([float(dict(zip(items[1::2],items[2::2]))[i]) for i in ['x','y','z']]) else: comments.append(line.strip()) material = np.empty(cells.prod()) # initialize as flat array i = 0 for line in content[header_length:]: if len(items := line.split('#')[0].split()) == 3: if items[1].lower() == 'of': material_entry = np.ones(int(items[0]))*float(items[2]) elif items[1].lower() == 'to': material_entry = np.linspace(int(items[0]),int(items[2]), abs(int(items[2])-int(items[0]))+1,dtype=float) else: material_entry = list(map(float, items)) else: material_entry = list(map(float, items)) material[i:i+len(material_entry)] = material_entry i += len(items) if i != cells.prod(): raise TypeError(f'mismatch between {cells.prod()} expected entries and {i} found') if not np.any(np.mod(material,1) != 0.0): # no float present material = material.astype(np.int64) - (1 if material.min() > 0 else 0) return GeomGrid(material = material.reshape(cells,order='F'), size = size, origin = origin, comments = comments, ) @staticmethod def load_Neper(fname: Union[str, Path]) -> 'GeomGrid': """ Load from Neper VTK file. Parameters ---------- fname : str or pathlib.Path Geometry file to read. Returns ------- loaded : damask.GeomGrid GeomGrid-based geometry from file. Notes ----- Material indices in Neper usually start at 1 unless a buffer material with index 0 is added. Examples -------- Read a periodic polycrystal generated with Neper. >>> import damask >>> N_grains = 20 >>> cells = (32,32,32) >>> damask.util.run(f'neper -T -n {N_grains} -tesrsize {cells[0]}:{cells[1]}:{cells[2]} -periodicity all -format vtk') >>> damask.GeomGrid.load_Neper(f'n{N_grains}-id1.vtk').renumber() cells: 32 × 32 × 32 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m # materials: 20 """ v = VTK.load(fname,'ImageData') cells = np.array(v.vtk_data.GetDimensions())-1 bbox = np.array(v.vtk_data.GetBounds()).reshape(3,2).T return GeomGrid(material = v.get('MaterialId').reshape(cells,order='F').astype('int32',casting='unsafe'), size = bbox[1] - bbox[0], origin = bbox[0], comments = util.execution_stamp('GeomGrid','load_Neper'), ) @staticmethod def load_DREAM3D(fname: Union[str, Path], feature_IDs: Optional[str] = None, cell_data: Optional[str] = None, phases: str = 'Phases', Euler_angles: str = 'EulerAngles', base_group: Optional[str] = None) -> 'GeomGrid': """ Load DREAM.3D (HDF5) file. Data in DREAM.3D files can be stored per cell ('CellData') and/or per grain ('Grain Data'). Per default, i.e. if 'feature_IDs' is None, cell-wise data is assumed. Parameters ---------- fname : str or pathlib.Path Filename of the DREAM.3D (HDF5) file. feature_IDs : str, optional Name of the dataset containing the mapping between cells and grain-wise data. Defaults to 'None', in which case cell-wise data is used. cell_data : str, optional Name of the group (folder) containing cell-wise data. Defaults to None in wich case it is automatically detected. phases : str, optional Name of the dataset containing the phase ID. It is not used for grain-wise data, i.e. when feature_IDs is not None. Defaults to 'Phases'. Euler_angles : str, optional Name of the dataset containing the crystallographic orientation as Euler angles in radians It is not used for grain-wise data, i.e. when feature_IDs is not None. Defaults to 'EulerAngles'. base_group : str, optional Path to the group (folder) that contains geometry (_SIMPL_GEOMETRY), and grain- or cell-wise data. Defaults to None, in which case it is set as the path that contains _SIMPL_GEOMETRY/SPACING. Returns ------- loaded : damask.GeomGrid GeomGrid-based geometry from file. Notes ----- damask.ConfigMaterial.load_DREAM3D gives the corresponding material definition. For cell-wise data, only unique combinations of orientation and phase are considered. """ with h5py.File(fname, 'r') as f: b = util.DREAM3D_base_group(f) if base_group is None else base_group c = util.DREAM3D_cell_data_group(f) if cell_data is None else cell_data cells = f['/'.join([b,'_SIMPL_GEOMETRY','DIMENSIONS'])][()] size = f['/'.join([b,'_SIMPL_GEOMETRY','SPACING'])] * cells origin = f['/'.join([b,'_SIMPL_GEOMETRY','ORIGIN'])][()] if feature_IDs is None: phase = f['/'.join([b,c,phases])][()].reshape(-1,1) O = Rotation.from_Euler_angles(f['/'.join([b,c,Euler_angles])]).as_quaternion().reshape(-1,4) # noqa unique,unique_inverse = np.unique(np.hstack([O,phase]),return_inverse=True,axis=0) ma = np.arange(cells.prod()) if len(unique) == cells.prod() else \ np.arange(unique.size)[np.argsort(pd.unique(unique_inverse))][unique_inverse] else: ma = f['/'.join([b,c,feature_IDs])][()].flatten() return GeomGrid(material = ma.reshape(cells,order='F'), size = size, origin = origin, comments = util.execution_stamp('GeomGrid','load_DREAM3D'), ) @staticmethod def from_table(table: Table, coordinates: str, labels: Union[str, Sequence[str]]) -> 'GeomGrid': """ Create grid from ASCII table. Parameters ---------- table : damask.Table Table that contains material information. coordinates : str Label of the vector column containing the spatial coordinates. Need to be ordered (1./x fast, 3./z slow). labels : (sequence of) str Label(s) of the columns containing the material definition. Each unique combination of values results in one material ID. Returns ------- new : damask.GeomGrid GeomGrid-based geometry from values in table. """ cells,size,origin = grid_filters.cellsSizeOrigin_coordinates0_point(table.get(coordinates)) labels_ = [labels] if isinstance(labels,str) else labels unique,unique_inverse = np.unique(np.hstack([table.get(l) for l in labels_]),return_inverse=True,axis=0) ma = np.arange(cells.prod()) if len(unique) == cells.prod() else \ np.arange(unique.size)[np.argsort(pd.unique(unique_inverse))][unique_inverse] return GeomGrid(material = ma.reshape(cells,order='F'), size = size, origin = origin, comments = util.execution_stamp('GeomGrid','from_table'), ) @staticmethod def _find_closest_seed(seeds: np.ndarray, weights: np.ndarray, point: np.ndarray) -> np.integer: return np.argmin(np.sum((np.broadcast_to(point,(len(seeds),3))-seeds)**2,axis=1) - weights) @staticmethod def from_Laguerre_tessellation(cells: IntSequence, size: FloatSequence, seeds: np.ndarray, weights: FloatSequence, material: Optional[IntSequence] = None, periodic: bool = True): """ Create grid from Laguerre tessellation. Parameters ---------- cells : sequence of int, len (3) Cell counts along x,y,z direction. size : sequence of float, len (3) Edge lengths of the grid in meter. seeds : numpy.ndarray of float, shape (:,3) Position of the seed points in meter. All points need to lay within the box [(0,0,0),size]. weights : sequence of float, len (seeds.shape[0]) Weights of the seeds. Setting all weights to 1.0 gives a standard Voronoi tessellation. material : sequence of int, len (seeds.shape[0]), optional Material ID of the seeds. Defaults to None, in which case materials are consecutively numbered. periodic : bool, optional Assume grid to be periodic. Defaults to True. Returns ------- new : damask.GeomGrid GeomGrid-based geometry from tessellation. """ weights_p: FloatSequence if periodic: weights_p = np.tile(weights,27) # Laguerre weights (1,2,3,1,2,3,...,1,2,3) seeds_p = np.vstack((seeds -np.array([size[0],0.,0.]),seeds, seeds +np.array([size[0],0.,0.]))) seeds_p = np.vstack((seeds_p-np.array([0.,size[1],0.]),seeds_p,seeds_p+np.array([0.,size[1],0.]))) seeds_p = np.vstack((seeds_p-np.array([0.,0.,size[2]]),seeds_p,seeds_p+np.array([0.,0.,size[2]]))) else: weights_p = np.array(weights,float) seeds_p = seeds coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3) pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',4))) result = pool.map_async(partial(GeomGrid._find_closest_seed,seeds_p,weights_p), coords) pool.close() pool.join() material_ = np.array(result.get()).reshape(cells) if periodic: material_ %= len(weights) return GeomGrid(material = material_ if material is None else np.array(material)[material_], size = size, comments = util.execution_stamp('GeomGrid','from_Laguerre_tessellation'), ) @staticmethod def from_Voronoi_tessellation(cells: IntSequence, size: FloatSequence, seeds: np.ndarray, material: Optional[IntSequence] = None, periodic: bool = True) -> 'GeomGrid': """ Create grid from Voronoi tessellation. Parameters ---------- cells : sequence of int, len (3) Cell counts along x,y,z direction. size : sequence of float, len (3) Edge lengths of the grid in meter. seeds : numpy.ndarray of float, shape (:,3) Position of the seed points in meter. All points need to lay within the box [(0,0,0),size]. material : sequence of int, len (seeds.shape[0]), optional Material ID of the seeds. Defaults to None, in which case materials are consecutively numbered. periodic : bool, optional Assume grid to be periodic. Defaults to True. Returns ------- new : damask.GeomGrid GeomGrid-based geometry from tessellation. """ coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3) tree = spatial.cKDTree(seeds,boxsize=size) if periodic else \ spatial.cKDTree(seeds) try: material_ = tree.query(coords, workers = int(os.environ.get('OMP_NUM_THREADS',4)))[1] except TypeError: material_ = tree.query(coords, n_jobs = int(os.environ.get('OMP_NUM_THREADS',4)))[1] # scipy <1.6 return GeomGrid(material = (material_ if material is None else np.array(material)[material_]).reshape(cells), size = size, comments = util.execution_stamp('GeomGrid','from_Voronoi_tessellation'), ) _minimal_surface = \ {'Schwarz P': lambda x,y,z: np.cos(x) + np.cos(y) + np.cos(z), 'Double Primitive': lambda x,y,z: ( 0.5 * (np.cos(x)*np.cos(y) + np.cos(y)*np.cos(z) + np.cos(z)*np.cos(x)) + 0.2 * (np.cos(2*x) + np.cos(2*y) + np.cos(2*z)) ), 'Schwarz D': lambda x,y,z: ( np.sin(x)*np.sin(y)*np.sin(z) + np.sin(x)*np.cos(y)*np.cos(z) + np.cos(x)*np.cos(y)*np.sin(z) + np.cos(x)*np.sin(y)*np.cos(z) ), 'Complementary D': lambda x,y,z: ( np.cos(3*x+y)*np.cos(z) - np.sin(3*x-y)*np.sin(z) + np.cos(x+3*y)*np.cos(z) + np.sin(x-3*y)*np.sin(z) + np.cos(x-y)*np.cos(3*z) - np.sin(x+y)*np.sin(3*z) ), 'Double Diamond': lambda x,y,z: 0.5 * (np.sin(x)*np.sin(y) + np.sin(y)*np.sin(z) + np.sin(z)*np.sin(x) + np.cos(x) * np.cos(y) * np.cos(z) ), 'Dprime': lambda x,y,z: 0.5 * ( np.cos(x)*np.cos(y)*np.cos(z) + np.cos(x)*np.sin(y)*np.sin(z) + np.sin(x)*np.cos(y)*np.sin(z) + np.sin(x)*np.sin(y)*np.cos(z) - np.sin(2*x)*np.sin(2*y) - np.sin(2*y)*np.sin(2*z) - np.sin(2*z)*np.sin(2*x) ) - 0.2, 'Gyroid': lambda x,y,z: np.cos(x)*np.sin(y) + np.cos(y)*np.sin(z) + np.cos(z)*np.sin(x), 'Gprime': lambda x,y,z : ( np.sin(2*x)*np.cos(y)*np.sin(z) + np.sin(2*y)*np.cos(z)*np.sin(x) + np.sin(2*z)*np.cos(x)*np.sin(y) ) + 0.32, 'Karcher K': lambda x,y,z: ( 0.3 * ( np.cos(x) + np.cos(y) + np.cos(z) + np.cos(x)*np.cos(y) + np.cos(y)*np.cos(z) + np.cos(z)*np.cos(x) ) - 0.4 * ( np.cos(2*x) + np.cos(2*y) + np.cos(2*z) ) ) + 0.2, 'Lidinoid': lambda x,y,z: 0.5 * ( np.sin(2*x)*np.cos(y)*np.sin(z) + np.sin(2*y)*np.cos(z)*np.sin(x) + np.sin(2*z)*np.cos(x)*np.sin(y) - np.cos(2*x)*np.cos(2*y) - np.cos(2*y)*np.cos(2*z) - np.cos(2*z)*np.cos(2*x) ) + 0.15, 'Neovius': lambda x,y,z: ( 3 * (np.cos(x)+np.cos(y)+np.cos(z)) + 4 * np.cos(x)*np.cos(y)*np.cos(z) ), 'Fisher-Koch S': lambda x,y,z: ( np.cos(2*x)*np.sin( y)*np.cos( z) + np.cos( x)*np.cos(2*y)*np.sin( z) + np.sin( x)*np.cos( y)*np.cos(2*z) ), } @staticmethod def from_minimal_surface(cells: IntSequence, size: FloatSequence, surface: str, threshold: float = 0.0, periods: int = 1, materials: IntSequence = (0,1)) -> 'GeomGrid': """ Create grid from definition of triply-periodic minimal surface. Parameters ---------- cells : sequence of int, len (3) Cell counts along x,y,z direction. size : sequence of float, len (3) Edge lengths of the grid in meter. surface : str Type of the minimal surface. See notes for details. threshold : float, optional. Threshold of the minimal surface. Defaults to 0.0. periods : integer, optional. Number of periods per unit cell. Defaults to 1. materials : sequence of int, len (2) Material IDs. Defaults to (0,1). Returns ------- new : damask.GeomGrid GeomGrid-based geometry from definition of minimal surface. Notes ----- The following triply-periodic minimal surfaces are implemented: - Schwarz P - Double Primitive - Schwarz D - Complementary D - Double Diamond - Dprime - Gyroid - Gprime - Karcher K - Lidinoid - Neovius - Fisher-Koch S References ---------- S.B.G. Blanquer et al., Biofabrication 9(2):025001, 2017 https://doi.org/10.1088/1758-5090/aa6553 M. Wohlgemuth et al., Macromolecules 34(17):6083-6089, 2001 https://doi.org/10.1021/ma0019499 M.-T. Hsieh and L. Valdevit, Software Impacts 6:100026, 2020 https://doi.org/10.1016/j.simpa.2020.100026 Examples -------- Minimal surface of 'Gyroid' type. >>> import numpy as np >>> import damask >>> damask.GeomGrid.from_minimal_surface([64]*3,np.ones(3)*1.e-4,'Gyroid') cells : 64 × 64 × 64 size : 0.0001 × 0.0001 × 0.0001 m³ origin: 0.0 0.0 0.0 m # materials: 2 Minimal surface of 'Neovius' type with specific material IDs. >>> import numpy as np >>> import damask >>> damask.GeomGrid.from_minimal_surface([80]*3,np.ones(3)*5.e-4, ... 'Neovius',materials=(1,5)) cells : 80 × 80 × 80 size : 0.0005 × 0.0005 × 0.0005 m³ origin: 0.0 0.0 0.0 m # materials: 2 (min: 1, max: 5) """ x,y,z = np.meshgrid(periods*2.0*np.pi*(np.arange(cells[0])+0.5)/cells[0], periods*2.0*np.pi*(np.arange(cells[1])+0.5)/cells[1], periods*2.0*np.pi*(np.arange(cells[2])+0.5)/cells[2], indexing='ij',sparse=True) return GeomGrid(material = np.where(threshold < GeomGrid._minimal_surface[surface](x,y,z),materials[1],materials[0]), size = size, comments = util.execution_stamp('GeomGrid','from_minimal_surface'), ) def save(self, fname: Union[str, Path], compress: bool = True): """ Save as VTK ImageData file. Parameters ---------- fname : str or pathlib.Path Filename to write. Valid extension is .vti, which will be appended if not given. compress : bool, optional Compress with zlib algorithm. Defaults to True. """ v = VTK.from_image_data(self.cells,self.size,self.origin)\ .set('material',self.material.flatten(order='F')) for label,data in self.initial_conditions.items(): v = v.set(label,data.flatten(order='F')) v.comments = self.comments v.save(fname,parallel=False,compress=compress) def save_ASCII(self, fname: Union[str, TextIO]): """ Save as geom file. Storing geometry files in ASCII format is deprecated. This function will be removed in a future version of DAMASK. Parameters ---------- fname : str or file handle Geometry file to write with extension '.geom'. compress : bool, optional Compress geometry using 'x of y' and 'a to b'. """ warnings.warn('Support for ASCII-based geom format will be removed in DAMASK 3.0.0', DeprecationWarning,2) header = [f'{len(self.comments)+4} header'] + self.comments \ + ['grid a {} b {} c {}'.format(*self.cells), 'size x {} y {} z {}'.format(*self.size), 'origin x {} y {} z {}'.format(*self.origin), 'homogenization 1', ] format_string = '%g' if self.material.dtype in np.sctypes['float'] else \ '%{}i'.format(1+int(np.floor(np.log10(np.nanmax(self.material))))) np.savetxt(fname, self.material.reshape([self.cells[0],np.prod(self.cells[1:])],order='F').T, header='\n'.join(header), fmt=format_string, comments='') def show(self, colormap: Union[Colormap, str] = 'cividis') -> None: """ Show on screen. Parameters ---------- colormap : damask.Colormap or str, optional Colormap for visualization of material IDs. Defaults to 'cividis'. """ VTK.from_image_data(self.cells,self.size,self.origin) \ .set('material',self.material.flatten('F'),) \ .show('material',colormap) def canvas(self, cells: Optional[IntSequence] = None, offset: Optional[IntSequence] = None, fill: Optional[int] = None) -> 'GeomGrid': """ Crop or enlarge/pad grid. Parameters ---------- cells : sequence of int, len (3), optional Cell counts along x,y,z direction. offset : sequence of int, len (3), optional Offset (measured in cells) from old to new grid. Defaults to [0,0,0]. fill : int, optional Material ID to fill the background. Defaults to material.max()+1. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. Examples -------- Remove lower 1/2 of the microstructure in z-direction. >>> import numpy as np >>> import damask >>> g = damask.GeomGrid(np.zeros([32]*3,int),np.ones(3)*1e-3) >>> g.canvas([32,32,16],[0,0,16]) cells: 32 × 32 × 16 size: 0.001 × 0.001 × 0.0005 m³ origin: 0.0 0.0 0.0005 m # materials: 1 """ offset_ = np.array(offset,np.int64) if offset is not None else np.zeros(3,np.int64) cells_ = np.array(cells,np.int64) if cells is not None else self.cells canvas = np.full(cells_,np.nanmax(self.material) + 1 if fill is None else fill,self.material.dtype) LL = np.clip( offset_, 0,np.minimum(self.cells, cells_+offset_)) UR = np.clip( offset_+cells_, 0,np.minimum(self.cells, cells_+offset_)) ll = np.clip(-offset_, 0,np.minimum( cells_,self.cells-offset_)) ur = np.clip(-offset_+self.cells,0,np.minimum( cells_,self.cells-offset_)) canvas[ll[0]:ur[0],ll[1]:ur[1],ll[2]:ur[2]] = self.material[LL[0]:UR[0],LL[1]:UR[1],LL[2]:UR[2]] return GeomGrid(material = canvas, size = self.size/self.cells*np.asarray(canvas.shape), origin = self.origin+offset_*self.size/self.cells, comments = self.comments+[util.execution_stamp('GeomGrid','canvas')], ) def mirror(self, directions: Sequence[str], reflect: bool = False) -> 'GeomGrid': """ Mirror grid along given directions. Parameters ---------- directions : (sequence of) {'x', 'y', 'z'} Direction(s) along which the grid is mirrored. reflect : bool, optional Reflect (include) outermost layers. Defaults to False. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. Examples -------- Mirror along y-direction. >>> import numpy as np >>> import damask >>> (g := damask.GeomGrid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) cells: 4 × 5 × 6 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m # materials: 120 >>> g.mirror('y') cells: 4 × 8 × 6 size: 1.0 × 1.6 × 1.0 m³ origin: 0.0 0.0 0.0 m # materials: 120 Reflect along x- and y-direction. >>> g.mirror('xy',reflect=True) cells: 8 × 10 × 6 size: 2.0 × 2.0 × 1.0 m³ origin: 0.0 0.0 0.0 m # materials: 120 Independence of mirroring order. >>> g.mirror('xy') == g.mirror(['y','x']) True """ if not set(directions).issubset(valid := ['x', 'y', 'z']): raise ValueError(f'invalid direction "{set(directions).difference(valid)}" specified') limits: Sequence[Optional[int]] = [None,None] if reflect else [-2,0] mat = self.material.copy() if 'x' in directions: mat = np.concatenate([mat,mat[limits[0]:limits[1]:-1,:,:]],0) if 'y' in directions: mat = np.concatenate([mat,mat[:,limits[0]:limits[1]:-1,:]],1) if 'z' in directions: mat = np.concatenate([mat,mat[:,:,limits[0]:limits[1]:-1]],2) return GeomGrid(material = mat, size = self.size/self.cells*np.asarray(mat.shape), origin = self.origin, comments = self.comments+[util.execution_stamp('GeomGrid','mirror')], ) def flip(self, directions: Sequence[str]) -> 'GeomGrid': """ Flip grid along given directions. Parameters ---------- directions : (sequence of) {'x', 'y', 'z'} Direction(s) along which the grid is flipped. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. Examples -------- Invariance of flipping order. >>> import numpy as np >>> import damask >>> (g := damask.GeomGrid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) cells: 4 × 5 × 6 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m # materials: 120 >>> g.flip('xyz') == g.flip(['x','z','y']) True Invariance of flipping a (fully) mirrored grid. >>> g.mirror('x',True) == g.mirror('x',True).flip('x') True """ if not set(directions).issubset(valid := ['x', 'y', 'z']): raise ValueError(f'invalid direction "{set(directions).difference(valid)}" specified') mat = np.flip(self.material, [valid.index(d) for d in directions if d in valid]) return GeomGrid(material = mat, size = self.size, origin = self.origin, comments = self.comments+[util.execution_stamp('GeomGrid','flip')], ) def rotate(self, R: Rotation, fill: Optional[int] = None) -> 'GeomGrid': """ Rotate grid (possibly extending its bounding box). Parameters ---------- R : damask.Rotation Rotation to apply to the grid. fill : int, optional Material ID to fill enlarged bounding box. Defaults to material.max()+1. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. Examples -------- Rotation by 180° (π) is equivalent to twice flipping. >>> import numpy as np >>> import damask >>> (g := damask.GeomGrid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) cells: 4 × 5 × 6 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m # materials: 120 >>> g.rotate(damask.Rotation.from_axis_angle([0,0,1,180],degrees=True)) == g.flip('xy') True """ material = self.material # These rotations are always applied in the reference coordinate system, i.e. (z,x,z) not (z,x',z'') # see https://www.cs.utexas.edu/~theshark/courses/cs354/lectures/cs354-14.pdf for angle,axes in zip(R.as_Euler_angles(degrees=True)[::-1], [(0,1),(1,2),(0,1)]): material_temp = ndimage.rotate(material,angle,axes,order=0,prefilter=False, output=self.material.dtype, cval=np.nanmax(self.material) + 1 if fill is None else fill) # avoid scipy interpolation errors for rotations close to multiples of 90° material = material_temp if np.prod(material_temp.shape) != np.prod(material.shape) else \ np.rot90(material,k=np.rint(angle/90.).astype(np.int64),axes=axes) origin = self.origin-(np.asarray(material.shape)-self.cells)*.5 * self.size/self.cells return GeomGrid(material = material, size = self.size/self.cells*np.asarray(material.shape), origin = origin, comments = self.comments+[util.execution_stamp('GeomGrid','rotate')], ) def scale(self, cells: IntSequence) -> 'GeomGrid': """ Scale grid to new cell counts. Parameters ---------- cells : sequence of int, len (3) Cell counts along x,y,z direction. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. Examples -------- Double grid resolution. >>> import numpy as np >>> import damask >>> (g := damask.GeomGrid(np.zeros([32]*3,int),np.ones(3)*1e-4)) cells: 32 × 32 × 32 size: 0.0001 × 0.0001 × 0.0001 m³ origin: 0.0 0.0 0.0 m # materials: 1 >>> g.scale(g.cells*2) cells : 64 × 64 × 64 size : 0.0001 × 0.0001 × 0.0001 m³ origin: 0.0 0.0 0.0 m # materials: 1 """ orig = tuple(map(np.linspace,self.origin + self.size/self.cells*.5, self.origin + self.size - self.size/self.cells*.5,self.cells)) interpolator = partial(interpolate.RegularGridInterpolator, points=orig,method='nearest',bounds_error=False,fill_value=None) new = grid_filters.coordinates0_point(cells,self.size,self.origin) return GeomGrid(material = interpolator(values=self.material)(new).astype(int), size = self.size, origin = self.origin, initial_conditions = {k: interpolator(values=v)(new) for k,v in self.initial_conditions.items()}, comments = self.comments+[util.execution_stamp('GeomGrid','scale')], ) def assemble(self, idx: np.ndarray) -> 'GeomGrid': """ Assemble new grid from index map. Parameters ---------- idx : numpy.ndarray of int, shape (:,:,:) or (:,:,:,3) GeomGrid of flat indices or coordinate indices. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. Cell count of resulting grid matches shape of index map. """ cells = idx.shape[:3] flat = (idx if len(idx.shape)==3 else grid_filters.ravel_index(idx)).flatten(order='F') ic = {k: v.flatten(order='F')[flat].reshape(cells,order='F') for k,v in self.initial_conditions.items()} return GeomGrid(material = self.material.flatten(order='F')[flat].reshape(cells,order='F'), size = self.size, origin = self.origin, initial_conditions = ic, comments = self.comments+[util.execution_stamp('GeomGrid','assemble')], ) def renumber(self) -> 'GeomGrid': """ Renumber sorted material indices as 0,...,N-1. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. """ _,renumbered = np.unique(self.material,return_inverse=True) return GeomGrid(material = renumbered.reshape(self.cells), size = self.size, origin = self.origin, initial_conditions = self.initial_conditions, comments = self.comments+[util.execution_stamp('GeomGrid','renumber')], ) def substitute(self, from_material: Union[int,IntSequence], to_material: Union[int,IntSequence]) -> 'GeomGrid': """ Substitute material indices. Parameters ---------- from_material : (sequence of) int Material indices to be substituted. to_material : (sequence of) int New material indices. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. """ material = self.material.copy() for f,t in zip(from_material if isinstance(from_material,(Sequence,np.ndarray)) else [from_material], to_material if isinstance(to_material,(Sequence,np.ndarray)) else [to_material]): # ToDo Python 3.10 has strict mode for zip material[self.material==f] = t return GeomGrid(material = material, size = self.size, origin = self.origin, initial_conditions = self.initial_conditions, comments = self.comments+[util.execution_stamp('GeomGrid','substitute')], ) def sort(self) -> 'GeomGrid': """ Sort material indices such that min(material ID) is located at (0,0,0). Returns ------- updated : damask.GeomGrid Updated grid-based geometry. """ a = self.material.flatten(order='F') from_ma = pd.unique(a) sort_idx = np.argsort(from_ma) ma = np.unique(a)[sort_idx][np.searchsorted(from_ma,a,sorter = sort_idx)] return GeomGrid(material = ma.reshape(self.cells,order='F'), size = self.size, origin = self.origin, initial_conditions = self.initial_conditions, comments = self.comments+[util.execution_stamp('GeomGrid','sort')], ) def clean(self, distance: float = np.sqrt(3), selection: Optional[IntSequence] = None, invert_selection: bool = False, periodic: bool = True, rng_seed: Optional[NumpyRngSeed] = None) -> 'GeomGrid': """ Smooth grid by selecting most frequent material ID within given stencil at each location. Parameters ---------- distance : float, optional Voxel distance checked for presence of other materials. Defaults to sqrt(3). selection : (sequence of) int, optional Material IDs to consider. Defaults to all. invert_selection : bool, optional Consider all material IDs except those in selection. Defaults to False. periodic : bool, optional Assume grid to be periodic. Defaults to True. rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional A seed to initialize the BitGenerator. Defaults to None. If None, then fresh, unpredictable entropy will be pulled from the OS. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. Notes ----- If multiple material IDs are most frequent within a stencil, a random choice is taken. """ def most_frequent(stencil: np.ndarray, selection: Union[None,np.ndarray], rng: np.random.Generator): me = stencil[stencil.size//2] if selection is None or me in selection: unique, counts = np.unique(stencil,return_counts=True) return rng.choice(unique[counts==np.max(counts)]) else: return me rng = np.random.default_rng(rng_seed) d = np.floor(distance).astype(np.int64) ext = np.linspace(-d,d,1+2*d,dtype=float), xx,yy,zz = np.meshgrid(ext,ext,ext) footprint = xx**2+yy**2+zz**2 <= distance**2+distance*1e-8 selection_ = None if selection is None else \ np.setdiff1d(self.material,selection) if invert_selection else \ np.intersect1d(self.material,selection) material = ndimage.generic_filter( self.material, most_frequent, footprint=footprint, mode='wrap' if periodic else 'nearest', extra_keywords=dict(selection=selection_,rng=rng), ).astype(self.material.dtype) return GeomGrid(material = material, size = self.size, origin = self.origin, initial_conditions = self.initial_conditions, comments = self.comments+[util.execution_stamp('GeomGrid','clean')], ) def add_primitive(self, dimension: Union[FloatSequence, IntSequence], center: Union[FloatSequence, IntSequence], exponent: Union[FloatSequence, float], fill: Optional[int] = None, R: Rotation = Rotation(), inverse: bool = False, periodic: bool = True) -> 'GeomGrid': """ Insert a primitive geometric object at a given position. Parameters ---------- dimension : sequence of int or float, len (3) Dimension (diameter/side length) of the primitive. If given as integers, cell centers are addressed. If given as floats, physical coordinates are addressed. center : sequence of int or float, len (3) Center of the primitive. If given as integers, cell centers are addressed. If given as floats, physical coordinates are addressed. exponent : (sequence of) float, len (3) Exponents for the three axes. 0 gives octahedron (ǀxǀ^(2^0) + ǀyǀ^(2^0) + ǀzǀ^(2^0) < 1) 1 gives sphere (ǀxǀ^(2^1) + ǀyǀ^(2^1) + ǀzǀ^(2^1) < 1) fill : int, optional Fill value for primitive. Defaults to material.max()+1. R : damask.Rotation, optional Rotation of the primitive. Defaults to no rotation. inverse : bool, optional Retain original materials within primitive and fill outside. Defaults to False. periodic : bool, optional Assume grid to be periodic. Defaults to True. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. Examples -------- Add a sphere at the center. >>> import numpy as np >>> import damask >>> g = damask.GeomGrid(np.zeros([64]*3,int), np.ones(3)*1e-4) >>> g.add_primitive(np.ones(3)*5e-5,np.ones(3)*5e-5,1) cells : 64 × 64 × 64 size : 0.0001 × 0.0001 × 0.0001 m³ origin: 0.0 0.0 0.0 m # materials: 2 Add a cube at the origin. >>> import numpy as np >>> import damask >>> g = damask.GeomGrid(np.zeros([64]*3,int), np.ones(3)*1e-4) >>> g.add_primitive(np.ones(3,int)*32,np.zeros(3),np.inf) cells : 64 × 64 × 64 size : 0.0001 × 0.0001 × 0.0001 m³ origin: 0.0 0.0 0.0 m # materials: 2 """ # radius and center r = np.array(dimension)/2.0*self.size/self.cells if np.array(dimension).dtype in np.sctypes['int'] else \ np.array(dimension)/2.0 c = (np.array(center) + .5)*self.size/self.cells if np.array(center).dtype in np.sctypes['int'] else \ (np.array(center) - self.origin) coords = grid_filters.coordinates0_point(self.cells,self.size, -(0.5*(self.size + (self.size/self.cells if np.array(center).dtype in np.sctypes['int'] else 0)) if periodic else c)) coords_rot = R.broadcast_to(tuple(self.cells))@coords with np.errstate(all='ignore'): mask = np.sum(np.power(np.abs(coords_rot)/r,2.0**np.array(exponent)),axis=-1) > 1.0 if periodic: # translate back to center mask = np.roll(mask,((c/self.size-0.5)*self.cells).round().astype(np.int64),(0,1,2)) return GeomGrid(material = np.where(np.logical_not(mask) if inverse else mask, self.material, np.nanmax(self.material)+1 if fill is None else fill), size = self.size, origin = self.origin, initial_conditions = self.initial_conditions, comments = self.comments+[util.execution_stamp('GeomGrid','add_primitive')], ) def vicinity_offset(self, distance: float = np.sqrt(3), offset: Optional[int] = None, selection: Optional[IntSequence] = None, invert_selection: bool = False, periodic: bool = True) -> 'GeomGrid': """ Offset material ID of points in the vicinity of selected (or just other) material IDs. Trigger points are variations in material ID, i.e. grain/phase boundaries or explicitly given material IDs. Parameters ---------- distance : float, optional Voxel distance checked for presence of other materials. Defaults to sqrt(3). offset : int, optional Offset (positive or negative) to tag material IDs. Defaults to material.max()+1. selection : (sequence of) int, optional Material IDs that trigger an offset. Defaults to any other than own material ID. invert_selection : bool, optional Consider all material IDs except those in selection. Defaults to False. periodic : bool, optional Assume grid to be periodic. Defaults to True. Returns ------- updated : damask.GeomGrid Updated grid-based geometry. """ @numba_njit_wrapper() def tainted_neighborhood(stencil: np.ndarray, selection: Optional[np.ndarray] = None): me = stencil[stencil.size//2] if selection is None: return np.any(stencil != me) elif not len(selection)==0: for stencil_item in stencil: for selection_item in selection: if stencil_item==selection_item and selection_item!=me: return True return False d = np.floor(distance).astype(np.int64) ext = np.linspace(-d,d,1+2*d,dtype=float), xx,yy,zz = np.meshgrid(ext,ext,ext) footprint = xx**2+yy**2+zz**2 <= distance**2+distance*1e-8 offset_ = np.nanmax(self.material)+1 if offset is None else offset selection_ = None if selection is None else \ np.setdiff1d(self.material,selection) if invert_selection else \ np.intersect1d(self.material,selection) mask = ndimage.generic_filter(self.material, tainted_neighborhood, footprint=footprint, mode='wrap' if periodic else 'nearest', extra_keywords=dict(selection=selection_), ) return GeomGrid(material = np.where(mask, self.material + offset_,self.material), size = self.size, origin = self.origin, initial_conditions = self.initial_conditions, comments = self.comments+[util.execution_stamp('GeomGrid','vicinity_offset')], ) def get_grain_boundaries(self, periodic: bool = True, directions: Sequence[str] = 'xyz') -> VTK: """ Create VTK unstructured grid containing grain boundaries. Parameters ---------- periodic : bool, optional Assume grid to be periodic. Defaults to True. directions : (sequence of) {'x', 'y', 'z'}, optional Direction(s) along which the boundaries are determined. Defaults to 'xyz'. Returns ------- grain_boundaries : damask.VTK VTK-based geometry of grain boundary network. """ if not set(directions).issubset(valid := ['x', 'y', 'z']): raise ValueError(f'invalid direction "{set(directions).difference(valid)}" specified') o = [[0, self.cells[0]+1, np.prod(self.cells[:2]+1)+self.cells[0]+1, np.prod(self.cells[:2]+1)], [0, np.prod(self.cells[:2]+1), np.prod(self.cells[:2]+1)+1, 1], [0, 1, self.cells[0]+1+1, self.cells[0]+1]] # offset for connectivity connectivity = [] for i,d in enumerate(['x','y','z']): if d not in directions: continue mask = self.material != np.roll(self.material,1,i) for j in [0,1,2]: mask = np.concatenate((mask,np.take(mask,[0],j)*(i==j)),j) if i == 0 and not periodic: mask[0,:,:] = mask[-1,:,:] = False if i == 1 and not periodic: mask[:,0,:] = mask[:,-1,:] = False if i == 2 and not periodic: mask[:,:,0] = mask[:,:,-1] = False base_nodes = np.argwhere(mask.flatten(order='F')).reshape(-1,1) connectivity.append(np.block([base_nodes + o[i][k] for k in range(4)])) coords = grid_filters.coordinates0_node(self.cells,self.size,self.origin).reshape(-1,3,order='F') return VTK.from_unstructured_grid(coords,np.vstack(connectivity),'QUAD')