Merge branch 'development' into nonSchmid-adaptation
This commit is contained in:
Philip Eisenlohr
commit
f7e06660b1
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@ -106,15 +106,12 @@ class ConfigMaterial(Config):
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Load DREAM.3D (HDF5) file.
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Data in DREAM.3D files can be stored per cell ('CellData')
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and/or per grain ('Grain Data'). Per default, cell-wise data
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is assumed.
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damask.Grid.load_DREAM3D allows to get the corresponding geometry
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for the grid solver.
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and/or per grain ('Grain Data'). Per default, i.e. if
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'grain_data' is None, cell-wise data is assumed.
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Parameters
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----------
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fname : str
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fname : str or pathlib.Path
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Filename of the DREAM.3D (HDF5) file.
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grain_data : str
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Name of the group (folder) containing grain-wise data. Defaults
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@ -140,19 +137,26 @@ class ConfigMaterial(Config):
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and grain- or cell-wise data. Defaults to None, in which case
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it is set as the path that contains _SIMPL_GEOMETRY/SPACING.
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Notes
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-----
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Homogenization and phase entries are emtpy and need to be defined separately.
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Returns
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-------
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loaded : damask.ConfigMaterial
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Material configuration from file.
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Notes
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-----
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damask.Grid.load_DREAM3D gives the corresponding geometry for
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the grid solver.
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For cell-wise data, only unique combinations of
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orientation and phase are considered.
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Homogenization and phase entries are emtpy and need to be
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defined separately.
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"""
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b = util.DREAM3D_base_group(fname) if base_group is None else base_group
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c = util.DREAM3D_cell_data_group(fname) if cell_data is None else cell_data
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f = h5py.File(fname,'r')
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with h5py.File(fname, 'r') as f:
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b = util.DREAM3D_base_group(f) if base_group is None else base_group
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c = util.DREAM3D_cell_data_group(f) if cell_data is None else cell_data
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if grain_data is None:
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phase = f['/'.join([b,c,phases])][()].flatten()
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@ -358,14 +358,14 @@ class Grid:
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"""
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Load DREAM.3D (HDF5) file.
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Data in DREAM.3D files can be stored per cell ('CellData') and/or
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per grain ('Grain Data'). Per default, cell-wise data is assumed.
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Data in DREAM.3D files can be stored per cell ('CellData')
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and/or per grain ('Grain Data'). Per default, i.e. if
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'feature_IDs' is None, cell-wise data is assumed.
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damask.ConfigMaterial.load_DREAM3D gives the corresponding material definition.
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Parameters
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----------
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fname : str or or pathlib.Path
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fname : str or pathlib.Path
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Filename of the DREAM.3D (HDF5) file.
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feature_IDs : str, optional
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Name of the dataset containing the mapping between cells and
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@ -392,10 +392,18 @@ class Grid:
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loaded : damask.Grid
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Grid-based geometry from file.
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Notes
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-----
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damask.ConfigMaterial.load_DREAM3D gives the corresponding
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material definition.
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For cell-wise data, only unique combinations of
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orientation and phase are considered.
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"""
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b = util.DREAM3D_base_group(fname) if base_group is None else base_group
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c = util.DREAM3D_cell_data_group(fname) if cell_data is None else cell_data
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f = h5py.File(fname, 'r')
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with h5py.File(fname, 'r') as f:
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b = util.DREAM3D_base_group(f) if base_group is None else base_group
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c = util.DREAM3D_cell_data_group(f) if cell_data is None else cell_data
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cells = f['/'.join([b,'_SIMPL_GEOMETRY','DIMENSIONS'])][()]
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size = f['/'.join([b,'_SIMPL_GEOMETRY','SPACING'])] * cells
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@ -804,7 +804,7 @@ class Orientation(Rotation,Crystal):
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blend += sym_ops.shape
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v = sym_ops.broadcast_to(shape) \
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@ np.broadcast_to(v.reshape(util.shapeshifter(v.shape,shape+(3,))),shape+(3,))
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return ~(self.broadcast_to(blend))@ np.broadcast_to(v,blend+(3,))
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return ~(self.broadcast_to(blend))@np.broadcast_to(v,blend+(3,))
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def Schmid(self, *,
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@ -833,7 +833,7 @@ class Orientation(Rotation,Crystal):
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>>> import damask
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>>> np.set_printoptions(3,suppress=True,floatmode='fixed')
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>>> O = damask.Orientation.from_Euler_angles(phi=[0,45,0],degrees=True,lattice='cF')
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>>> O.Schmid(N_slip=[1])
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>>> O.Schmid(N_slip=[12])[0]
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array([[ 0.000, 0.000, 0.000],
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[ 0.577, -0.000, 0.816],
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[ 0.000, 0.000, 0.000]])
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@ -1,5 +1,3 @@
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import multiprocessing as mp
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from multiprocessing.synchronize import Lock
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import re
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import fnmatch
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import os
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@ -7,8 +5,8 @@ import copy
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import datetime
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import xml.etree.ElementTree as ET # noqa
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import xml.dom.minidom
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import functools
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from pathlib import Path
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from functools import partial
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from collections import defaultdict
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from collections.abc import Iterable
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from typing import Optional, Union, Callable, Any, Sequence, Literal, Dict, List, Tuple
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@ -601,17 +599,6 @@ class Result:
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f['/geometry/T_c'].attrs['VTK_TYPE'].decode())
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@staticmethod
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def _add_absolute(x: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': np.abs(x['data']),
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'label': f'|{x["label"]}|',
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'meta': {
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'unit': x['meta']['unit'],
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'description': f"absolute value of {x['label']} ({x['meta']['description']})",
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'creator': 'add_absolute'
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}
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}
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def add_absolute(self, x: str):
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"""
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Add absolute value.
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@ -622,28 +609,20 @@ class Result:
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Name of scalar, vector, or tensor dataset to take absolute value of.
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"""
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self._add_generic_pointwise(self._add_absolute,{'x':x})
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@staticmethod
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def _add_calculation(**kwargs) -> Dict[str, Any]:
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formula = kwargs['formula']
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for d in re.findall(r'#(.*?)#',formula):
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formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
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data = eval(formula)
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if not hasattr(data,'shape') or data.shape[0] != kwargs[d]['data'].shape[0]:
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raise ValueError('"{}" results in invalid shape'.format(kwargs['formula']))
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def absolute(x: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': data,
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'label': kwargs['label'],
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'data': np.abs(x['data']),
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'label': f'|{x["label"]}|',
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'meta': {
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'unit': kwargs['unit'],
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'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
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'creator': 'add_calculation'
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'unit': x['meta']['unit'],
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'description': f"absolute value of {x['label']} ({x['meta']['description']})",
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'creator': 'add_absolute'
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}
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}
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self._add_generic_pointwise(absolute,{'x':x})
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def add_calculation(self,
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formula: str,
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name: str,
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@ -692,24 +671,30 @@ class Result:
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... 'Mises equivalent of the Cauchy stress')
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"""
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def calculation(**kwargs) -> Dict[str, Any]:
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formula = kwargs['formula']
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for d in re.findall(r'#(.*?)#',formula):
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formula = formula.replace(f'#{d}#',f"kwargs['{d}']['data']")
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data = eval(formula)
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if not hasattr(data,'shape') or data.shape[0] != kwargs[d]['data'].shape[0]:
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raise ValueError('"{}" results in invalid shape'.format(kwargs['formula']))
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return {
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'data': data,
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'label': kwargs['label'],
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'meta': {
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'unit': kwargs['unit'],
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'description': f"{kwargs['description']} (formula: {kwargs['formula']})",
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'creator': 'add_calculation'
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}
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}
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dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
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args = {'formula':formula,'label':name,'unit':unit,'description':description}
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self._add_generic_pointwise(self._add_calculation,dataset_mapping,args)
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self._add_generic_pointwise(calculation,dataset_mapping,args)
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@staticmethod
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def _add_stress_Cauchy(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': mechanics.stress_Cauchy(P['data'],F['data']),
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'label': 'sigma',
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'meta': {
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'unit': P['meta']['unit'],
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'description': "Cauchy stress calculated "
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f"from {P['label']} ({P['meta']['description']})"
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f" and {F['label']} ({F['meta']['description']})",
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'creator': 'add_stress_Cauchy'
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}
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}
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def add_stress_Cauchy(self,
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P: str = 'P',
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F: str = 'F'):
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@ -726,20 +711,23 @@ class Result:
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Defaults to 'F'.
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"""
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self._add_generic_pointwise(self._add_stress_Cauchy,{'P':P,'F':F})
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@staticmethod
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def _add_determinant(T: Dict[str, Any]) -> Dict[str, Any]:
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def stress_Cauchy(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': np.linalg.det(T['data']),
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'label': f"det({T['label']})",
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'data': mechanics.stress_Cauchy(P['data'],F['data']),
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'label': 'sigma',
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'meta': {
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'unit': T['meta']['unit'],
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'description': f"determinant of tensor {T['label']} ({T['meta']['description']})",
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'creator': 'add_determinant'
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'unit': P['meta']['unit'],
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'description': "Cauchy stress calculated "
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f"from {P['label']} ({P['meta']['description']})"
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f" and {F['label']} ({F['meta']['description']})",
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'creator': 'add_stress_Cauchy'
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}
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}
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self._add_generic_pointwise(stress_Cauchy,{'P':P,'F':F})
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def add_determinant(self, T: str):
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"""
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Add the determinant of a tensor.
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@ -758,20 +746,21 @@ class Result:
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>>> r.add_determinant('F_p')
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"""
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self._add_generic_pointwise(self._add_determinant,{'T':T})
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@staticmethod
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def _add_deviator(T: Dict[str, Any]) -> Dict[str, Any]:
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def determinant(T: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': tensor.deviatoric(T['data']),
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'label': f"s_{T['label']}",
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'data': np.linalg.det(T['data']),
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'label': f"det({T['label']})",
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'meta': {
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'unit': T['meta']['unit'],
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'description': f"deviator of tensor {T['label']} ({T['meta']['description']})",
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'creator': 'add_deviator'
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'description': f"determinant of tensor {T['label']} ({T['meta']['description']})",
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'creator': 'add_determinant'
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}
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}
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self._add_generic_pointwise(determinant,{'T':T})
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def add_deviator(self, T: str):
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"""
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Add the deviatoric part of a tensor.
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@ -790,29 +779,21 @@ class Result:
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>>> r.add_deviator('sigma')
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"""
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self._add_generic_pointwise(self._add_deviator,{'T':T})
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@staticmethod
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def _add_eigenvalue(T_sym: Dict[str, Any], eigenvalue: Literal['max, mid, min']) -> Dict[str, Any]:
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if eigenvalue == 'max':
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label,p = 'maximum',2
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elif eigenvalue == 'mid':
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label,p = 'intermediate',1
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elif eigenvalue == 'min':
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label,p = 'minimum',0
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else:
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raise ValueError(f'invalid eigenvalue: {eigenvalue}')
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|
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def deviator(T: Dict[str, Any]) -> Dict[str, Any]:
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return {
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'data': tensor.eigenvalues(T_sym['data'])[:,p],
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'label': f"lambda_{eigenvalue}({T_sym['label']})",
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'meta' : {
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'unit': T_sym['meta']['unit'],
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'description': f"{label} eigenvalue of {T_sym['label']} ({T_sym['meta']['description']})",
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'creator': 'add_eigenvalue'
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'data': tensor.deviatoric(T['data']),
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'label': f"s_{T['label']}",
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'meta': {
|
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'unit': T['meta']['unit'],
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'description': f"deviator of tensor {T['label']} ({T['meta']['description']})",
|
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'creator': 'add_deviator'
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}
|
||||
}
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self._add_generic_pointwise(deviator,{'T':T})
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|
||||
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def add_eigenvalue(self,
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T_sym: str,
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eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
|
||||
|
|
@ -835,11 +816,47 @@ class Result:
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>>> r.add_eigenvalue('sigma','min')
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||||
|
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"""
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self._add_generic_pointwise(self._add_eigenvalue,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
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|
||||
def eigenval(T_sym: Dict[str, Any], eigenvalue: Literal['max, mid, min']) -> Dict[str, Any]:
|
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if eigenvalue == 'max':
|
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label,p = 'maximum',2
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elif eigenvalue == 'mid':
|
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label,p = 'intermediate',1
|
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elif eigenvalue == 'min':
|
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label,p = 'minimum',0
|
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else:
|
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raise ValueError(f'invalid eigenvalue: {eigenvalue}')
|
||||
|
||||
return {
|
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'data': tensor.eigenvalues(T_sym['data'])[:,p],
|
||||
'label': f"lambda_{eigenvalue}({T_sym['label']})",
|
||||
'meta' : {
|
||||
'unit': T_sym['meta']['unit'],
|
||||
'description': f"{label} eigenvalue of {T_sym['label']} ({T_sym['meta']['description']})",
|
||||
'creator': 'add_eigenvalue'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(eigenval,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_eigenvector(T_sym: Dict[str, Any], eigenvalue: Literal['max', 'mid', 'min']) -> Dict[str, Any]:
|
||||
def add_eigenvector(self,
|
||||
T_sym: str,
|
||||
eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
|
||||
"""
|
||||
Add eigenvector of symmetric tensor.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
T_sym : str
|
||||
Name of symmetric tensor dataset.
|
||||
eigenvalue : {'max', 'mid', 'min'}, optional
|
||||
Eigenvalue to which the eigenvector corresponds.
|
||||
Defaults to 'max'.
|
||||
|
||||
"""
|
||||
|
||||
def eigenvector(T_sym: Dict[str, Any], eigenvalue: Literal['max', 'mid', 'min']) -> Dict[str, Any]:
|
||||
if eigenvalue == 'max':
|
||||
label,p = 'maximum',2
|
||||
elif eigenvalue == 'mid':
|
||||
|
|
@ -859,40 +876,10 @@ class Result:
|
|||
'creator': 'add_eigenvector'
|
||||
}
|
||||
}
|
||||
def add_eigenvector(self,
|
||||
T_sym: str,
|
||||
eigenvalue: Literal['max', 'mid', 'min'] = 'max'):
|
||||
"""
|
||||
Add eigenvector of symmetric tensor.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
T_sym : str
|
||||
Name of symmetric tensor dataset.
|
||||
eigenvalue : {'max', 'mid', 'min'}, optional
|
||||
Eigenvalue to which the eigenvector corresponds.
|
||||
Defaults to 'max'.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
||||
self._add_generic_pointwise(eigenvector,{'T_sym':T_sym},{'eigenvalue':eigenvalue})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_IPF_color(l: FloatSequence, q: Dict[str, Any]) -> Dict[str, Any]:
|
||||
m = util.scale_to_coprime(np.array(l))
|
||||
lattice = q['meta']['lattice']
|
||||
o = Orientation(rotation = q['data'],lattice=lattice)
|
||||
|
||||
return {
|
||||
'data': np.uint8(o.IPF_color(l)*255),
|
||||
'label': 'IPFcolor_({} {} {})'.format(*m),
|
||||
'meta' : {
|
||||
'unit': '8-bit RGB',
|
||||
'lattice': q['meta']['lattice'],
|
||||
'description': 'Inverse Pole Figure (IPF) colors along sample direction ({} {} {})'.format(*m),
|
||||
'creator': 'add_IPF_color'
|
||||
}
|
||||
}
|
||||
def add_IPF_color(self,
|
||||
l: FloatSequence,
|
||||
q: str = 'O'):
|
||||
|
|
@ -916,20 +903,26 @@ class Result:
|
|||
>>> r.add_IPF_color(np.array([0,1,1]))
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_IPF_color,{'q':q},{'l':l})
|
||||
|
||||
def IPF_color(l: FloatSequence, q: Dict[str, Any]) -> Dict[str, Any]:
|
||||
m = util.scale_to_coprime(np.array(l))
|
||||
lattice = q['meta']['lattice']
|
||||
o = Orientation(rotation = q['data'],lattice=lattice)
|
||||
|
||||
@staticmethod
|
||||
def _add_maximum_shear(T_sym: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': mechanics.maximum_shear(T_sym['data']),
|
||||
'label': f"max_shear({T_sym['label']})",
|
||||
'meta': {
|
||||
'unit': T_sym['meta']['unit'],
|
||||
'description': f"maximum shear component of {T_sym['label']} ({T_sym['meta']['description']})",
|
||||
'creator': 'add_maximum_shear'
|
||||
'data': np.uint8(o.IPF_color(l)*255),
|
||||
'label': 'IPFcolor_({} {} {})'.format(*m),
|
||||
'meta' : {
|
||||
'unit': '8-bit RGB',
|
||||
'lattice': q['meta']['lattice'],
|
||||
'description': 'Inverse Pole Figure (IPF) colors along sample direction ({} {} {})'.format(*m),
|
||||
'creator': 'add_IPF_color'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(IPF_color,{'q':q},{'l':l})
|
||||
|
||||
|
||||
def add_maximum_shear(self, T_sym: str):
|
||||
"""
|
||||
Add maximum shear components of symmetric tensor.
|
||||
|
|
@ -940,30 +933,20 @@ class Result:
|
|||
Name of symmetric tensor dataset.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_maximum_shear,{'T_sym':T_sym})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_equivalent_Mises(T_sym: Dict[str, Any], kind: str) -> Dict[str, Any]:
|
||||
k = kind
|
||||
if k is None:
|
||||
if T_sym['meta']['unit'] == '1':
|
||||
k = 'strain'
|
||||
elif T_sym['meta']['unit'] == 'Pa':
|
||||
k = 'stress'
|
||||
if k not in ['stress', 'strain']:
|
||||
raise ValueError(f'invalid von Mises kind "{kind}"')
|
||||
|
||||
def maximum_shear(T_sym: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': (mechanics.equivalent_strain_Mises if k=='strain' else \
|
||||
mechanics.equivalent_stress_Mises)(T_sym['data']),
|
||||
'label': f"{T_sym['label']}_vM",
|
||||
'data': mechanics.maximum_shear(T_sym['data']),
|
||||
'label': f"max_shear({T_sym['label']})",
|
||||
'meta': {
|
||||
'unit': T_sym['meta']['unit'],
|
||||
'description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['description']})",
|
||||
'creator': 'add_Mises'
|
||||
'description': f"maximum shear component of {T_sym['label']} ({T_sym['meta']['description']})",
|
||||
'creator': 'add_maximum_shear'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(maximum_shear,{'T_sym':T_sym})
|
||||
|
||||
|
||||
def add_equivalent_Mises(self,
|
||||
T_sym: str,
|
||||
kind: Optional[str] = None):
|
||||
|
|
@ -993,11 +976,45 @@ class Result:
|
|||
>>> r.add_equivalent_Mises('epsilon_V^0.0(F)')
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
|
||||
def equivalent_Mises(T_sym: Dict[str, Any], kind: str) -> Dict[str, Any]:
|
||||
k = kind
|
||||
if k is None:
|
||||
if T_sym['meta']['unit'] == '1':
|
||||
k = 'strain'
|
||||
elif T_sym['meta']['unit'] == 'Pa':
|
||||
k = 'stress'
|
||||
if k not in ['stress', 'strain']:
|
||||
raise ValueError(f'invalid von Mises kind "{kind}"')
|
||||
|
||||
return {
|
||||
'data': (mechanics.equivalent_strain_Mises if k=='strain' else \
|
||||
mechanics.equivalent_stress_Mises)(T_sym['data']),
|
||||
'label': f"{T_sym['label']}_vM",
|
||||
'meta': {
|
||||
'unit': T_sym['meta']['unit'],
|
||||
'description': f"Mises equivalent {k} of {T_sym['label']} ({T_sym['meta']['description']})",
|
||||
'creator': 'add_Mises'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(equivalent_Mises,{'T_sym':T_sym},{'kind':kind})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_norm(x: Dict[str, Any], ord: Union[int, float, Literal['fro', 'nuc']]) -> Dict[str, Any]:
|
||||
def add_norm(self,
|
||||
x: str,
|
||||
ord: Union[None, int, float, Literal['fro', 'nuc']] = None):
|
||||
"""
|
||||
Add the norm of a vector or tensor.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : str
|
||||
Name of vector or tensor dataset.
|
||||
ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
|
||||
Order of the norm. inf means NumPy's inf object. For details refer to numpy.linalg.norm.
|
||||
|
||||
"""
|
||||
def norm(x: Dict[str, Any], ord: Union[int, float, Literal['fro', 'nuc']]) -> Dict[str, Any]:
|
||||
o = ord
|
||||
if len(x['data'].shape) == 2:
|
||||
axis: Union[int, Tuple[int, int]] = 1
|
||||
|
|
@ -1019,36 +1036,10 @@ class Result:
|
|||
'creator': 'add_norm'
|
||||
}
|
||||
}
|
||||
def add_norm(self,
|
||||
x: str,
|
||||
ord: Union[None, int, float, Literal['fro', 'nuc']] = None):
|
||||
"""
|
||||
Add the norm of a vector or tensor.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : str
|
||||
Name of vector or tensor dataset.
|
||||
ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
|
||||
Order of the norm. inf means NumPy's inf object. For details refer to numpy.linalg.norm.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_norm,{'x':x},{'ord':ord})
|
||||
self._add_generic_pointwise(norm,{'x':x},{'ord':ord})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_stress_second_Piola_Kirchhoff(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
|
||||
'label': 'S',
|
||||
'meta': {
|
||||
'unit': P['meta']['unit'],
|
||||
'description': "second Piola-Kirchhoff stress calculated "
|
||||
f"from {P['label']} ({P['meta']['description']})"
|
||||
f" and {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_stress_second_Piola_Kirchhoff'
|
||||
}
|
||||
}
|
||||
def add_stress_second_Piola_Kirchhoff(self,
|
||||
P: str = 'P',
|
||||
F: str = 'F'):
|
||||
|
|
@ -1071,34 +1062,23 @@ class Result:
|
|||
is taken into account.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_stress_second_Piola_Kirchhoff,{'P':P,'F':F})
|
||||
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_pole(q: Dict[str, Any],
|
||||
uvw: FloatSequence,
|
||||
hkl: FloatSequence,
|
||||
with_symmetry: bool,
|
||||
normalize: bool) -> Dict[str, Any]:
|
||||
c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1
|
||||
brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2]
|
||||
label = 'p^' + '{}{} {} {}{}'.format(brackets[0],
|
||||
*(uvw if uvw else hkl),
|
||||
brackets[-1],)
|
||||
ori = Orientation(q['data'],lattice=q['meta']['lattice'],a=1,c=c)
|
||||
|
||||
def stress_second_Piola_Kirchhoff(P: Dict[str, Any], F: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': ori.to_pole(uvw=uvw,hkl=hkl,with_symmetry=with_symmetry,normalize=normalize),
|
||||
'label': label,
|
||||
'meta' : {
|
||||
'unit': '1',
|
||||
'description': f'{"normalized " if normalize else ""}lab frame vector along lattice ' \
|
||||
+ ('direction' if uvw is not None else 'plane') \
|
||||
+ ('s' if with_symmetry else ''),
|
||||
'creator': 'add_pole'
|
||||
'data': mechanics.stress_second_Piola_Kirchhoff(P['data'],F['data']),
|
||||
'label': 'S',
|
||||
'meta': {
|
||||
'unit': P['meta']['unit'],
|
||||
'description': "second Piola-Kirchhoff stress calculated "
|
||||
f"from {P['label']} ({P['meta']['description']})"
|
||||
f" and {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_stress_second_Piola_Kirchhoff'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(stress_second_Piola_Kirchhoff,{'P':P,'F':F})
|
||||
|
||||
|
||||
|
||||
def add_pole(self,
|
||||
q: str = 'O',
|
||||
*,
|
||||
|
|
@ -1124,22 +1104,33 @@ class Result:
|
|||
Defaults to True.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_pole,
|
||||
{'q':q},
|
||||
{'uvw':uvw,'hkl':hkl,'with_symmetry':with_symmetry,'normalize':normalize})
|
||||
def pole(q: Dict[str, Any],
|
||||
uvw: FloatSequence,
|
||||
hkl: FloatSequence,
|
||||
with_symmetry: bool,
|
||||
normalize: bool) -> Dict[str, Any]:
|
||||
c = q['meta']['c/a'] if 'c/a' in q['meta'] else 1
|
||||
brackets = ['[]','()','⟨⟩','{}'][(uvw is None)*1+with_symmetry*2]
|
||||
label = 'p^' + '{}{} {} {}{}'.format(brackets[0],
|
||||
*(uvw if uvw else hkl),
|
||||
brackets[-1],)
|
||||
ori = Orientation(q['data'],lattice=q['meta']['lattice'],a=1,c=c)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_rotation(F: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': mechanics.rotation(F['data']).as_matrix(),
|
||||
'label': f"R({F['label']})",
|
||||
'meta': {
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f"rotational part of {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_rotation'
|
||||
'data': ori.to_pole(uvw=uvw,hkl=hkl,with_symmetry=with_symmetry,normalize=normalize),
|
||||
'label': label,
|
||||
'meta' : {
|
||||
'unit': '1',
|
||||
'description': f'{"normalized " if normalize else ""}lab frame vector along lattice ' \
|
||||
+ ('direction' if uvw is not None else 'plane') \
|
||||
+ ('s' if with_symmetry else ''),
|
||||
'creator': 'add_pole'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(pole,{'q':q},{'uvw':uvw,'hkl':hkl,'with_symmetry':with_symmetry,'normalize':normalize})
|
||||
|
||||
|
||||
def add_rotation(self, F: str):
|
||||
"""
|
||||
Add rotational part of a deformation gradient.
|
||||
|
|
@ -1158,20 +1149,20 @@ class Result:
|
|||
>>> r.add_rotation('F')
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_rotation,{'F':F})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_spherical(T: Dict[str, Any]) -> Dict[str, Any]:
|
||||
def rotation(F: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': tensor.spherical(T['data'],False),
|
||||
'label': f"p_{T['label']}",
|
||||
'data': mechanics.rotation(F['data']).as_matrix(),
|
||||
'label': f"R({F['label']})",
|
||||
'meta': {
|
||||
'unit': T['meta']['unit'],
|
||||
'description': f"spherical component of tensor {T['label']} ({T['meta']['description']})",
|
||||
'creator': 'add_spherical'
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f"rotational part of {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_rotation'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(rotation,{'F':F})
|
||||
|
||||
|
||||
def add_spherical(self, T: str):
|
||||
"""
|
||||
Add the spherical (hydrostatic) part of a tensor.
|
||||
|
|
@ -1190,22 +1181,20 @@ class Result:
|
|||
>>> r.add_spherical('sigma')
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_spherical,{'T':T})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_strain(F: Dict[str, Any], t: Literal['V', 'U'], m: float) -> Dict[str, Any]:
|
||||
side = 'left' if t == 'V' else 'right'
|
||||
def spherical(T: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': mechanics.strain(F['data'],t,m),
|
||||
'label': f"epsilon_{t}^{m}({F['label']})",
|
||||
'data': tensor.spherical(T['data'],False),
|
||||
'label': f"p_{T['label']}",
|
||||
'meta': {
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f'Seth-Hill strain tensor of order {m} based on {side} stretch tensor '+\
|
||||
f"of {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_strain'
|
||||
'unit': T['meta']['unit'],
|
||||
'description': f"spherical component of tensor {T['label']} ({T['meta']['description']})",
|
||||
'creator': 'add_spherical'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(spherical,{'T':T})
|
||||
|
||||
|
||||
def add_strain(self,
|
||||
F: str = 'F',
|
||||
t: Literal['V', 'U'] = 'V',
|
||||
|
|
@ -1266,21 +1255,22 @@ class Result:
|
|||
| https://de.wikipedia.org/wiki/Verzerrungstensor
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_strain,{'F':F},{'t':t,'m':m})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_stretch_tensor(F: Dict[str, Any], t: str) -> Dict[str, Any]:
|
||||
def strain(F: Dict[str, Any], t: Literal['V', 'U'], m: float) -> Dict[str, Any]:
|
||||
side = 'left' if t == 'V' else 'right'
|
||||
return {
|
||||
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
|
||||
'label': f"{t}({F['label']})",
|
||||
'data': mechanics.strain(F['data'],t,m),
|
||||
'label': f"epsilon_{t}^{m}({F['label']})",
|
||||
'meta': {
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
|
||||
+f"of {F['label']} ({F['meta']['description']})", # noqa
|
||||
'creator': 'add_stretch_tensor'
|
||||
'description': f'Seth-Hill strain tensor of order {m} based on {side} stretch tensor '+\
|
||||
f"of {F['label']} ({F['meta']['description']})",
|
||||
'creator': 'add_strain'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(strain,{'F':F},{'t':t,'m':m})
|
||||
|
||||
|
||||
def add_stretch_tensor(self,
|
||||
F: str = 'F',
|
||||
t: Literal['V', 'U'] = 'V'):
|
||||
|
|
@ -1296,20 +1286,21 @@ class Result:
|
|||
Defaults to 'V'.
|
||||
|
||||
"""
|
||||
self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_curl(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
def stretch_tensor(F: Dict[str, Any], t: str) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': grid_filters.curl(size,f['data']),
|
||||
'label': f"curl({f['label']})",
|
||||
'data': (mechanics.stretch_left if t.upper() == 'V' else mechanics.stretch_right)(F['data']),
|
||||
'label': f"{t}({F['label']})",
|
||||
'meta': {
|
||||
'unit': f['meta']['unit']+'/m',
|
||||
'description': f"curl of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_curl'
|
||||
'unit': F['meta']['unit'],
|
||||
'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
|
||||
+f"of {F['label']} ({F['meta']['description']})", # noqa
|
||||
'creator': 'add_stretch_tensor'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_pointwise(stretch_tensor,{'F':F},{'t':t})
|
||||
|
||||
|
||||
def add_curl(self, f: str):
|
||||
"""
|
||||
Add curl of a field.
|
||||
|
|
@ -1325,20 +1316,20 @@ class Result:
|
|||
i.e. fields resulting from the grid solver.
|
||||
|
||||
"""
|
||||
self._add_generic_grid(self._add_curl,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_divergence(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
def curl(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': grid_filters.divergence(size,f['data']),
|
||||
'label': f"divergence({f['label']})",
|
||||
'data': grid_filters.curl(size,f['data']),
|
||||
'label': f"curl({f['label']})",
|
||||
'meta': {
|
||||
'unit': f['meta']['unit']+'/m',
|
||||
'description': f"divergence of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_divergence'
|
||||
'description': f"curl of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_curl'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_grid(curl,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
def add_divergence(self, f: str):
|
||||
"""
|
||||
Add divergence of a field.
|
||||
|
|
@ -1354,21 +1345,20 @@ class Result:
|
|||
i.e. fields resulting from the grid solver.
|
||||
|
||||
"""
|
||||
self._add_generic_grid(self._add_divergence,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_gradient(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
def divergence(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
|
||||
f['data'].reshape(f['data'].shape+(1,))),
|
||||
'label': f"gradient({f['label']})",
|
||||
'data': grid_filters.divergence(size,f['data']),
|
||||
'label': f"divergence({f['label']})",
|
||||
'meta': {
|
||||
'unit': f['meta']['unit']+'/m',
|
||||
'description': f"gradient of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_gradient'
|
||||
'description': f"divergence of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_divergence'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_grid(divergence,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
def add_gradient(self, f: str):
|
||||
"""
|
||||
Add gradient of a field.
|
||||
|
|
@ -1384,7 +1374,19 @@ class Result:
|
|||
i.e. fields resulting from the grid solver.
|
||||
|
||||
"""
|
||||
self._add_generic_grid(self._add_gradient,{'f':f},{'size':self.size})
|
||||
def gradient(f: Dict[str, Any], size: np.ndarray) -> Dict[str, Any]:
|
||||
return {
|
||||
'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
|
||||
f['data'].reshape(f['data'].shape+(1,))),
|
||||
'label': f"gradient({f['label']})",
|
||||
'meta': {
|
||||
'unit': f['meta']['unit']+'/m',
|
||||
'description': f"gradient of {f['label']} ({f['meta']['description']})",
|
||||
'creator': 'add_gradient'
|
||||
}
|
||||
}
|
||||
|
||||
self._add_generic_grid(gradient,{'f':f},{'size':self.size})
|
||||
|
||||
|
||||
def _add_generic_grid(self,
|
||||
|
|
@ -1446,29 +1448,6 @@ class Result:
|
|||
f'damask.Result.{creator} v{damask.version}'.encode()
|
||||
|
||||
|
||||
def _job_pointwise(self,
|
||||
group: str,
|
||||
callback: Callable,
|
||||
datasets: Dict[str, str],
|
||||
args: Dict[str, str],
|
||||
lock: Lock) -> List[Union[None, Any]]:
|
||||
"""Execute job for _add_generic_pointwise."""
|
||||
try:
|
||||
datasets_in = {}
|
||||
lock.acquire()
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for arg,label in datasets.items():
|
||||
loc = f[group+'/'+label]
|
||||
datasets_in[arg]={'data' :loc[()],
|
||||
'label':label,
|
||||
'meta': {k:(v.decode() if not h5py3 and type(v) is bytes else v) \
|
||||
for k,v in loc.attrs.items()}}
|
||||
lock.release()
|
||||
r = callback(**datasets_in,**args)
|
||||
return [group,r]
|
||||
except Exception as err:
|
||||
print(f'Error during calculation: {err}.')
|
||||
return [None,None]
|
||||
|
||||
|
||||
def _add_generic_pointwise(self,
|
||||
|
|
@ -1490,8 +1469,24 @@ class Result:
|
|||
Arguments parsed to func.
|
||||
|
||||
"""
|
||||
pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',4)))
|
||||
lock = mp.Manager().Lock()
|
||||
|
||||
def job_pointwise(group: str,
|
||||
callback: Callable,
|
||||
datasets: Dict[str, str],
|
||||
args: Dict[str, str]) -> Union[None, Any]:
|
||||
try:
|
||||
datasets_in = {}
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for arg,label in datasets.items():
|
||||
loc = f[group+'/'+label]
|
||||
datasets_in[arg]={'data' :loc[()],
|
||||
'label':label,
|
||||
'meta': {k:(v.decode() if not h5py3 and type(v) is bytes else v) \
|
||||
for k,v in loc.attrs.items()}}
|
||||
return callback(**datasets_in,**args)
|
||||
except Exception as err:
|
||||
print(f'Error during calculation: {err}.')
|
||||
return None
|
||||
|
||||
groups = []
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
|
|
@ -1506,12 +1501,10 @@ class Result:
|
|||
print('No matching dataset found, no data was added.')
|
||||
return
|
||||
|
||||
default_arg = partial(self._job_pointwise,callback=func,datasets=datasets,args=args,lock=lock)
|
||||
|
||||
for group,result in util.show_progress(pool.imap_unordered(default_arg,groups),len(groups)):# type: ignore
|
||||
if not result:
|
||||
for group in util.show_progress(groups):
|
||||
if not (result := job_pointwise(group, callback=func, datasets=datasets, args=args)): # type: ignore
|
||||
continue
|
||||
lock.acquire()
|
||||
with h5py.File(self.fname, 'a') as f:
|
||||
try:
|
||||
if not self._protected and '/'.join([group,result['label']]) in f:
|
||||
|
|
@ -1543,10 +1536,6 @@ class Result:
|
|||
|
||||
except (OSError,RuntimeError) as err:
|
||||
print(f'Could not add dataset: {err}.')
|
||||
lock.release()
|
||||
|
||||
pool.close()
|
||||
pool.join()
|
||||
|
||||
|
||||
def _mappings(self):
|
||||
|
|
@ -2064,7 +2053,7 @@ class Result:
|
|||
|
||||
cfg_dir = (Path.cwd() if target_dir is None else Path(target_dir))
|
||||
with h5py.File(self.fname,'r') as f_in:
|
||||
f_in['setup'].visititems(partial(export,
|
||||
f_in['setup'].visititems(functools.partial(export,
|
||||
output=output,
|
||||
cfg_dir=cfg_dir,
|
||||
overwrite=overwrite))
|
||||
|
|
|
|||
|
|
@ -375,6 +375,11 @@ class Rotation:
|
|||
Return self@other.
|
||||
|
||||
Rotate vector, second-order tensor, or fourth-order tensor.
|
||||
`other` is interpreted as an array of tensor quantities with the highest-possible order
|
||||
considering the shape of `self`. Compatible innermost dimensions will blend.
|
||||
For instance, shapes of (2,) and (3,3) for `self` and `other` prompt interpretation of
|
||||
`other` as a second-rank tensor and result in (2,) rotated tensors, whereas
|
||||
shapes of (2,1) and (3,3) for `self` and `other` result in (2,3) rotated vectors.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -386,29 +391,73 @@ class Rotation:
|
|||
rotated : numpy.ndarray, shape (...,3), (...,3,3), or (...,3,3,3,3)
|
||||
Rotated vector or tensor, i.e. transformed to frame defined by rotation.
|
||||
|
||||
Examples
|
||||
--------
|
||||
All below examples rely on imported modules:
|
||||
>>> import numpy as np
|
||||
>>> import damask
|
||||
|
||||
Application of twelve (random) rotations to a set of five vectors.
|
||||
|
||||
>>> r = damask.Rotation.from_random(shape=(12))
|
||||
>>> o = np.ones((5,3))
|
||||
>>> (r@o).shape # (12) @ (5, 3)
|
||||
(12,5, 3)
|
||||
|
||||
Application of a (random) rotation to all twelve second-rank tensors.
|
||||
|
||||
>>> r = damask.Rotation.from_random()
|
||||
>>> o = np.ones((12,3,3))
|
||||
>>> (r@o).shape # (1) @ (12, 3,3)
|
||||
(12,3,3)
|
||||
|
||||
Application of twelve (random) rotations to the corresponding twelve second-rank tensors.
|
||||
|
||||
>>> r = damask.Rotation.from_random(shape=(12))
|
||||
>>> o = np.ones((12,3,3))
|
||||
>>> (r@o).shape # (12) @ (3,3)
|
||||
(12,3,3)
|
||||
|
||||
Application of each of three (random) rotations to all three vectors.
|
||||
|
||||
>>> r = damask.Rotation.from_random(shape=(3))
|
||||
>>> o = np.ones((3,3))
|
||||
>>> (r[...,np.newaxis]@o[np.newaxis,...]).shape # (3,1) @ (1,3, 3)
|
||||
(3,3,3)
|
||||
|
||||
Application of twelve (random) rotations to all twelve second-rank tensors.
|
||||
|
||||
>>> r = damask.Rotation.from_random(shape=(12))
|
||||
>>> o = np.ones((12,3,3))
|
||||
>>> (r@o[np.newaxis,...]).shape # (12) @ (1,12, 3,3)
|
||||
(12,3,3,3)
|
||||
|
||||
"""
|
||||
if isinstance(other, np.ndarray):
|
||||
if self.shape + (3,) == other.shape:
|
||||
q_m = self.quaternion[...,0]
|
||||
p_m = self.quaternion[...,1:]
|
||||
obs = util.shapeblender(self.shape,other.shape,keep_ones=False)[len(self.shape):]
|
||||
for l in [4,2,1]:
|
||||
if obs[-l:] == l*(3,):
|
||||
bs = util.shapeblender(self.shape,other.shape[:-l],False)
|
||||
self_ = self.broadcast_to(bs) if self.shape != bs else self
|
||||
if l==1:
|
||||
q_m = self_.quaternion[...,0]
|
||||
p_m = self_.quaternion[...,1:]
|
||||
A = q_m**2 - np.einsum('...i,...i',p_m,p_m)
|
||||
B = 2. * np.einsum('...i,...i',p_m,other)
|
||||
C = 2. * _P * q_m
|
||||
return np.block([(A * other[...,i]).reshape(self.shape+(1,)) +
|
||||
(B * p_m[...,i]).reshape(self.shape+(1,)) +
|
||||
(C * ( p_m[...,(i+1)%3]*other[...,(i+2)%3]\
|
||||
- p_m[...,(i+2)%3]*other[...,(i+1)%3])).reshape(self.shape+(1,))
|
||||
for i in [0,1,2]])
|
||||
if self.shape + (3,3) == other.shape:
|
||||
R = self.as_matrix()
|
||||
return np.einsum('...im,...jn,...mn',R,R,other)
|
||||
if self.shape + (3,3,3,3) == other.shape:
|
||||
R = self.as_matrix()
|
||||
return np.einsum('...im,...jn,...ko,...lp,...mnop',R,R,R,R,other)
|
||||
return np.block([(A * other[...,i]) +
|
||||
(B * p_m[...,i]) +
|
||||
(C * ( p_m[...,(i+1)%3]*other[...,(i+2)%3]
|
||||
- p_m[...,(i+2)%3]*other[...,(i+1)%3]))
|
||||
for i in [0,1,2]]).reshape(bs+(3,),order='F')
|
||||
else:
|
||||
return np.einsum({2: '...im,...jn,...mn',
|
||||
4: '...im,...jn,...ko,...lp,...mnop'}[l],
|
||||
*l*[self_.as_matrix()],
|
||||
other)
|
||||
raise ValueError('can only rotate vectors, second-order tensors, and fourth-order tensors')
|
||||
elif isinstance(other, Rotation):
|
||||
raise TypeError('use "R1*R2", i.e. multiplication, to compose rotations "R1" and "R2"')
|
||||
raise TypeError('use "R2*R1", i.e. multiplication, to compose rotations "R1" and "R2"')
|
||||
else:
|
||||
raise TypeError(f'cannot rotate "{type(other)}"')
|
||||
|
||||
|
|
|
|||
|
|
@ -512,7 +512,8 @@ def shapeshifter(fro: _Tuple[int, ...],
|
|||
return tuple(final_shape[::-1] if mode == 'left' else final_shape)
|
||||
|
||||
def shapeblender(a: _Tuple[int, ...],
|
||||
b: _Tuple[int, ...]) -> _Tuple[int, ...]:
|
||||
b: _Tuple[int, ...],
|
||||
keep_ones: bool = True) -> _Tuple[int, ...]:
|
||||
"""
|
||||
Return a shape that overlaps the rightmost entries of 'a' with the leftmost of 'b'.
|
||||
|
||||
|
|
@ -522,6 +523,9 @@ def shapeblender(a: _Tuple[int, ...],
|
|||
Shape of first array.
|
||||
b : tuple
|
||||
Shape of second array.
|
||||
keep_ones : bool, optional
|
||||
Treat innermost '1's as literal value instead of dimensional placeholder.
|
||||
Defaults to True.
|
||||
|
||||
Examples
|
||||
--------
|
||||
|
|
@ -531,13 +535,30 @@ def shapeblender(a: _Tuple[int, ...],
|
|||
(1,2,3)
|
||||
>>> shapeblender((1,),(2,2,1))
|
||||
(1,2,2,1)
|
||||
>>> shapeblender((1,),(2,2,1),False)
|
||||
(2,2,1)
|
||||
>>> shapeblender((3,2),(3,2))
|
||||
(3,2)
|
||||
|
||||
"""
|
||||
i = min(len(a),len(b))
|
||||
while i > 0 and a[-i:] != b[:i]: i -= 1
|
||||
return a + b[i:]
|
||||
def is_broadcastable(a,b):
|
||||
try:
|
||||
_np.broadcast_shapes(a,b)
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
|
||||
a_,_b = a,b
|
||||
if keep_ones:
|
||||
i = min(len(a_),len(_b))
|
||||
while i > 0 and a_[-i:] != _b[:i]: i -= 1
|
||||
return a_ + _b[i:]
|
||||
else:
|
||||
a_ += max(0,len(_b)-len(a_))*(1,)
|
||||
while not is_broadcastable(a_,_b):
|
||||
a_ = a_ + ((1,) if len(a_)<=len(_b) else ())
|
||||
_b = ((1,) if len(_b)<len(a_) else ()) + _b
|
||||
return _np.broadcast_shapes(a_,_b)
|
||||
|
||||
|
||||
def _docstringer(docstring: _Union[str, _Callable],
|
||||
|
|
@ -698,7 +719,7 @@ def pass_on(keyword: str,
|
|||
return wrapper
|
||||
return decorator
|
||||
|
||||
def DREAM3D_base_group(fname: _Union[str, _Path]) -> str:
|
||||
def DREAM3D_base_group(fname: _Union[str, _Path, _h5py.File]) -> str:
|
||||
"""
|
||||
Determine the base group of a DREAM.3D file.
|
||||
|
||||
|
|
@ -707,7 +728,7 @@ def DREAM3D_base_group(fname: _Union[str, _Path]) -> str:
|
|||
|
||||
Parameters
|
||||
----------
|
||||
fname : str or pathlib.Path
|
||||
fname : str, pathlib.Path, or _h5py.File
|
||||
Filename of the DREAM.3D (HDF5) file.
|
||||
|
||||
Returns
|
||||
|
|
@ -716,15 +737,19 @@ def DREAM3D_base_group(fname: _Union[str, _Path]) -> str:
|
|||
Path to the base group.
|
||||
|
||||
"""
|
||||
with _h5py.File(_Path(fname).expanduser(),'r') as f:
|
||||
def get_base_group(f: _h5py.File) -> str:
|
||||
base_group = f.visit(lambda path: path.rsplit('/',2)[0] if '_SIMPL_GEOMETRY/SPACING' in path else None)
|
||||
|
||||
if base_group is None:
|
||||
raise ValueError(f'could not determine base group in file "{fname}"')
|
||||
|
||||
return base_group
|
||||
|
||||
def DREAM3D_cell_data_group(fname: _Union[str, _Path]) -> str:
|
||||
if isinstance(fname,_h5py.File):
|
||||
return get_base_group(fname)
|
||||
|
||||
with _h5py.File(_Path(fname).expanduser(),'r') as f:
|
||||
return get_base_group(f)
|
||||
|
||||
def DREAM3D_cell_data_group(fname: _Union[str, _Path, _h5py.File]) -> str:
|
||||
"""
|
||||
Determine the cell data group of a DREAM.3D file.
|
||||
|
||||
|
|
@ -734,7 +759,7 @@ def DREAM3D_cell_data_group(fname: _Union[str, _Path]) -> str:
|
|||
|
||||
Parameters
|
||||
----------
|
||||
fname : str or pathlib.Path
|
||||
fname : str, pathlib.Path, or h5py.File
|
||||
Filename of the DREAM.3D (HDF5) file.
|
||||
|
||||
Returns
|
||||
|
|
@ -743,18 +768,22 @@ def DREAM3D_cell_data_group(fname: _Union[str, _Path]) -> str:
|
|||
Path to the cell data group.
|
||||
|
||||
"""
|
||||
base_group = DREAM3D_base_group(fname)
|
||||
with _h5py.File(_Path(fname).expanduser(),'r') as f:
|
||||
def get_cell_data_group(f: _h5py.File) -> str:
|
||||
base_group = DREAM3D_base_group(f)
|
||||
cells = tuple(f['/'.join([base_group,'_SIMPL_GEOMETRY','DIMENSIONS'])][()][::-1])
|
||||
cell_data_group = f[base_group].visititems(lambda path,obj: path.split('/')[0] \
|
||||
if isinstance(obj,_h5py._hl.dataset.Dataset) and _np.shape(obj)[:-1] == cells \
|
||||
else None)
|
||||
|
||||
if cell_data_group is None:
|
||||
raise ValueError(f'could not determine cell-data group in file "{fname}/{base_group}"')
|
||||
|
||||
return cell_data_group
|
||||
|
||||
if isinstance(fname,_h5py.File):
|
||||
return get_cell_data_group(fname)
|
||||
|
||||
with _h5py.File(_Path(fname).expanduser(),'r') as f:
|
||||
return get_cell_data_group(f)
|
||||
|
||||
|
||||
def Bravais_to_Miller(*,
|
||||
uvtw: _Optional[_np.ndarray] = None,
|
||||
|
|
|
|||
|
|
@ -162,7 +162,7 @@ class TestOrientation:
|
|||
([np.arccos(3**(-.5)),np.pi/4,0],[0,0],[0,0,1],[0,0,1])])
|
||||
def test_fiber_IPF(self,crystal,sample,direction,color):
|
||||
fiber = Orientation.from_fiber_component(crystal=crystal,sample=sample,family='cubic',shape=200)
|
||||
print(np.allclose(fiber.IPF_color(direction),color))
|
||||
assert np.allclose(fiber.IPF_color(direction),color)
|
||||
|
||||
|
||||
@pytest.mark.parametrize('kwargs',[
|
||||
|
|
@ -455,11 +455,9 @@ class TestOrientation:
|
|||
p = Orientation.from_random(family=family,shape=right)
|
||||
blend = util.shapeblender(o.shape,p.shape)
|
||||
for loc in np.random.randint(0,blend,(10,len(blend))):
|
||||
# print(f'{a}/{b} @ {loc}')
|
||||
# print(o[tuple(loc[:len(o.shape)])].disorientation(p[tuple(loc[-len(p.shape):])]))
|
||||
# print(o.disorientation(p)[tuple(loc)])
|
||||
assert o[tuple(loc[:len(o.shape)])].disorientation(p[tuple(loc[-len(p.shape):])]) \
|
||||
.isclose(o.disorientation(p)[tuple(loc)])
|
||||
l = () if left is None else tuple(np.minimum(np.array(left )-1,loc[:len(left)]))
|
||||
r = () if right is None else tuple(np.minimum(np.array(right)-1,loc[-len(right):]))
|
||||
assert o[l].disorientation(p[r]).isclose(o.disorientation(p)[tuple(loc)])
|
||||
|
||||
@pytest.mark.parametrize('family',crystal_families)
|
||||
@pytest.mark.parametrize('left,right',[
|
||||
|
|
@ -467,13 +465,16 @@ class TestOrientation:
|
|||
((2,2),(4,4)),
|
||||
((3,1),(1,3)),
|
||||
(None,(3,)),
|
||||
(None,()),
|
||||
])
|
||||
def test_IPF_color_blending(self,family,left,right):
|
||||
o = Orientation.from_random(family=family,shape=left)
|
||||
v = np.random.random(right+(3,))
|
||||
blend = util.shapeblender(o.shape,v.shape[:-1])
|
||||
for loc in np.random.randint(0,blend,(10,len(blend))):
|
||||
assert np.allclose(o[tuple(loc[:len(o.shape)])].IPF_color(v[tuple(loc[-len(v.shape[:-1]):])]),
|
||||
l = () if left is None else tuple(np.minimum(np.array(left )-1,loc[:len(left)]))
|
||||
r = () if right is None else tuple(np.minimum(np.array(right)-1,loc[-len(right):]))
|
||||
assert np.allclose(o[l].IPF_color(v[r]),
|
||||
o.IPF_color(v)[tuple(loc)])
|
||||
|
||||
@pytest.mark.parametrize('family',crystal_families)
|
||||
|
|
@ -488,7 +489,9 @@ class TestOrientation:
|
|||
v = np.random.random(right+(3,))
|
||||
blend = util.shapeblender(o.shape,v.shape[:-1])
|
||||
for loc in np.random.randint(0,blend,(10,len(blend))):
|
||||
assert np.allclose(o[tuple(loc[:len(o.shape)])].to_SST(v[tuple(loc[-len(v.shape[:-1]):])]),
|
||||
l = () if left is None else tuple(np.minimum(np.array(left )-1,loc[:len(left)]))
|
||||
r = () if right is None else tuple(np.minimum(np.array(right)-1,loc[-len(right):]))
|
||||
assert np.allclose(o[l].to_SST(v[r]),
|
||||
o.to_SST(v)[tuple(loc)])
|
||||
|
||||
@pytest.mark.parametrize('lattice,a,b,c,alpha,beta,gamma',
|
||||
|
|
@ -514,7 +517,9 @@ class TestOrientation:
|
|||
v = np.random.random(right+(3,))
|
||||
blend = util.shapeblender(o.shape,v.shape[:-1])
|
||||
for loc in np.random.randint(0,blend,(10,len(blend))):
|
||||
assert np.allclose(o[tuple(loc[:len(o.shape)])].to_pole(uvw=v[tuple(loc[-len(v.shape[:-1]):])]),
|
||||
l = () if left is None else tuple(np.minimum(np.array(left )-1,loc[:len(left)]))
|
||||
r = () if right is None else tuple(np.minimum(np.array(right)-1,loc[-len(right):]))
|
||||
assert np.allclose(o[l].to_pole(uvw=v[r]),
|
||||
o.to_pole(uvw=v)[tuple(loc)])
|
||||
|
||||
def test_mul_invalid(self):
|
||||
|
|
|
|||
|
|
@ -326,7 +326,7 @@ class TestResult:
|
|||
if shape == 'pseudo_scalar': default.add_calculation('#F#[:,0,0:1]','x','1','a pseudo scalar')
|
||||
if shape == 'scalar': default.add_calculation('#F#[:,0,0]','x','1','just a scalar')
|
||||
if shape == 'vector': default.add_calculation('#F#[:,:,1]','x','1','just a vector')
|
||||
x = default.place('x').reshape((np.product(default.cells),-1))
|
||||
x = default.place('x').reshape((np.prod(default.cells),-1))
|
||||
default.add_gradient('x')
|
||||
in_file = default.place('gradient(x)')
|
||||
in_memory = grid_filters.gradient(default.size,x.reshape(tuple(default.cells)+x.shape[1:])).reshape(in_file.shape)
|
||||
|
|
|
|||
|
|
@ -1065,7 +1065,7 @@ class TestRotation:
|
|||
|
||||
@pytest.mark.parametrize('data',[np.random.rand(4),
|
||||
np.random.rand(3,2),
|
||||
np.random.rand(3,2,3,3)])
|
||||
np.random.rand(3,3,3,1)])
|
||||
def test_rotate_invalid_shape(self,data):
|
||||
R = Rotation.from_random()
|
||||
with pytest.raises(ValueError):
|
||||
|
|
|
|||
|
|
@ -398,7 +398,7 @@ class TestGridFilters:
|
|||
np.arange(cells[1]),
|
||||
np.arange(cells[2]),indexing='ij')).reshape(tuple(cells)+(3,),order='F')
|
||||
x,y,z = map(np.random.randint,cells)
|
||||
assert grid_filters.ravel_index(indices)[x,y,z] == np.arange(0,np.product(cells)).reshape(cells,order='F')[x,y,z]
|
||||
assert grid_filters.ravel_index(indices)[x,y,z] == np.arange(0,np.prod(cells)).reshape(cells,order='F')[x,y,z]
|
||||
|
||||
def test_unravel_index(self):
|
||||
cells = np.random.randint(8,32,(3))
|
||||
|
|
|
|||
|
|
@ -128,39 +128,47 @@ class TestUtil:
|
|||
with pytest.raises(ValueError):
|
||||
util.shapeshifter(fro,to,mode)
|
||||
|
||||
@pytest.mark.parametrize('a,b,answer',
|
||||
@pytest.mark.parametrize('a,b,ones,answer',
|
||||
[
|
||||
((),(1,),(1,)),
|
||||
((1,),(),(1,)),
|
||||
((1,),(7,),(1,7)),
|
||||
((2,),(2,2),(2,2)),
|
||||
((1,2),(2,2),(1,2,2)),
|
||||
((1,2,3),(2,3,4),(1,2,3,4)),
|
||||
((1,2,3),(1,2,3),(1,2,3)),
|
||||
((),(1,),True,(1,)),
|
||||
((1,),(),False,(1,)),
|
||||
((1,1),(7,),False,(1,7)),
|
||||
((1,),(7,),False,(7,)),
|
||||
((1,),(7,),True,(1,7)),
|
||||
((2,),(2,2),False,(2,2)),
|
||||
((1,2),(2,2),False,(2,2)),
|
||||
((1,1,2),(2,2),False,(1,2,2)),
|
||||
((1,1,2),(2,2),True,(1,1,2,2)),
|
||||
((1,2,3),(2,3,4),False,(1,2,3,4)),
|
||||
((1,2,3),(1,2,3),False,(1,2,3)),
|
||||
])
|
||||
def test_shapeblender(self,a,b,answer):
|
||||
assert util.shapeblender(a,b) == answer
|
||||
def test_shapeblender(self,a,b,ones,answer):
|
||||
assert util.shapeblender(a,b,ones) == answer
|
||||
|
||||
@pytest.mark.parametrize('style',[util.emph,util.deemph,util.warn,util.strikeout])
|
||||
def test_decorate(self,style):
|
||||
assert 'DAMASK' in style('DAMASK')
|
||||
|
||||
@pytest.mark.parametrize('complete',[True,False])
|
||||
def test_D3D_base_group(self,tmp_path,complete):
|
||||
@pytest.mark.parametrize('fhandle',[True,False])
|
||||
def test_D3D_base_group(self,tmp_path,complete,fhandle):
|
||||
base_group = ''.join(random.choices('DAMASK', k=10))
|
||||
with h5py.File(tmp_path/'base_group.dream3d','w') as f:
|
||||
f.create_group('/'.join((base_group,'_SIMPL_GEOMETRY')))
|
||||
if complete:
|
||||
f['/'.join((base_group,'_SIMPL_GEOMETRY'))].create_dataset('SPACING',data=np.ones(3))
|
||||
|
||||
fname = tmp_path/'base_group.dream3d'
|
||||
if fhandle: fname = h5py.File(fname)
|
||||
if complete:
|
||||
assert base_group == util.DREAM3D_base_group(tmp_path/'base_group.dream3d')
|
||||
assert base_group == util.DREAM3D_base_group(fname)
|
||||
else:
|
||||
with pytest.raises(ValueError):
|
||||
util.DREAM3D_base_group(tmp_path/'base_group.dream3d')
|
||||
util.DREAM3D_base_group(fname)
|
||||
|
||||
@pytest.mark.parametrize('complete',[True,False])
|
||||
def test_D3D_cell_data_group(self,tmp_path,complete):
|
||||
@pytest.mark.parametrize('fhandle',[True,False])
|
||||
def test_D3D_cell_data_group(self,tmp_path,complete,fhandle):
|
||||
base_group = ''.join(random.choices('DAMASK', k=10))
|
||||
cell_data_group = ''.join(random.choices('KULeuven', k=10))
|
||||
cells = np.random.randint(1,50,3)
|
||||
|
|
@ -172,11 +180,13 @@ class TestUtil:
|
|||
if complete:
|
||||
f['/'.join((base_group,cell_data_group))].create_dataset('data',shape=np.append(cells,1))
|
||||
|
||||
fname = tmp_path/'cell_data_group.dream3d'
|
||||
if fhandle: fname = h5py.File(fname)
|
||||
if complete:
|
||||
assert cell_data_group == util.DREAM3D_cell_data_group(tmp_path/'cell_data_group.dream3d')
|
||||
assert cell_data_group == util.DREAM3D_cell_data_group(fname)
|
||||
else:
|
||||
with pytest.raises(ValueError):
|
||||
util.DREAM3D_cell_data_group(tmp_path/'cell_data_group.dream3d')
|
||||
util.DREAM3D_cell_data_group(fname)
|
||||
|
||||
|
||||
@pytest.mark.parametrize('full,reduced',[({}, {}),
|
||||
|
|
|
|||
Loading…
Reference in New Issue