Merge remote-tracking branch 'origin/development' into polishing
This commit is contained in:
Martin Diehl
commit
8da04f197c
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@ -6,15 +6,18 @@ import glob
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import argparse
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import shutil
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from pathlib import Path
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import subprocess
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import shlex
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import damask
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sys.path.append(str(Path(__file__).parents[2]/'python/damask'))
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import solver
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def copy_and_patch(patch,orig,editor):
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try:
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shutil.copyfile(orig,orig.parent/patch.stem)
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except shutil.SameFileError:
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pass
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damask.util.run(f'patch {orig.parent/patch.stem} {patch} --backup --forward')
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subprocess.run(shlex.split(f'patch {orig.parent/patch.stem} {patch} --backup --forward'))
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with open(orig.parent/patch.stem) as f_in:
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content = f_in.read()
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with open(orig.parent/patch.stem,'w') as f_out:
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@ -28,15 +31,16 @@ parser = argparse.ArgumentParser(
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parser.add_argument('--editor', dest='editor', metavar='string', default='vi',
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help='Name of the editor (executable) used by Marc Mentat')
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parser.add_argument('--marc-root', dest='marc_root', metavar='string',
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default=damask.solver._marc._marc_root,
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default=solver._marc._marc_root,
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help='Marc root directory')
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parser.add_argument('--marc-version', dest='marc_version', metavar='string',
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default=damask.solver._marc._marc_version,
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default=solver._marc._marc_version,
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help='Marc version')
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parser.add_argument('--damask-root', dest='damask_root', metavar = 'string',
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default=damask.solver._marc._damask_root,
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default=solver._marc._damask_root,
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help='DAMASK root directory')
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args = parser.parse_args()
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marc_root = Path(args.marc_root).expanduser()
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damask_root = Path(args.damask_root).expanduser()
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@ -52,7 +56,7 @@ matches = {'Marc_tools': [['comp_user','comp_damask_*mp'],
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for cmd in ['patch','xvfb-run']:
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try:
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damask.util.run(f'{cmd} --help')
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subprocess.run(shlex.split(f'{cmd} --help'))
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except FileNotFoundError:
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print(f'"{cmd}" not found, please install')
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sys.exit()
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@ -71,7 +75,7 @@ print('compiling Mentat menu binaries...')
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executable = marc_root/f'mentat{marc_version}/bin/mentat'
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menu_file = marc_root/f'mentat{marc_version}/menus/linux64/main.msb'
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damask.util.run(f'xvfb-run -a {executable} -compile {menu_file}')
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subprocess.run(shlex.split(f'xvfb-run -a {executable} -compile {menu_file}'))
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print('setting file access rights...')
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@ -448,9 +448,12 @@ class Orientation(Rotation,Crystal):
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elif self.family == 'orthorhombic':
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return (np.prod(1. >= rho_abs,axis=-1)).astype(bool)
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elif self.family == 'monoclinic':
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return (1. >= rho_abs[...,1]).astype(bool)
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return np.logical_or( 1. >= rho_abs[...,1],
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np.isnan(rho_abs[...,1]))
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elif self.family == 'triclinic':
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return np.ones(rho_abs.shape[:-1]).astype(bool)
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else:
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return np.all(np.isfinite(rho_abs),axis=-1)
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raise TypeError(f'unknown symmetry "{self.family}"')
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@property
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@ -1372,7 +1372,7 @@ class Rotation:
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w[np.isclose(w[...,0],1.0+0.0j),1:] = 0.
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w[np.isclose(w[...,1],1.0+0.0j),2:] = 0.
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vr = np.swapaxes(vr,-1,-2)
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ax = np.where(np.abs(diag_delta)<1e-12,
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ax = np.where(np.abs(diag_delta)<1e-13,
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np.real(vr[np.isclose(w,1.0+0.0j)]).reshape(om.shape[:-2]+(3,)),
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np.abs(np.real(vr[np.isclose(w,1.0+0.0j)]).reshape(om.shape[:-2]+(3,))) \
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*np.sign(diag_delta))
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@ -1581,14 +1581,13 @@ class Rotation:
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@staticmethod
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def _ho2ax(ho: np.ndarray) -> np.ndarray:
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"""Homochoric vector to axis–angle pair."""
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tfit = np.array([+1.0000000000018852, -0.5000000002194847,
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-0.024999992127593126, -0.003928701544781374,
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-0.0008152701535450438, -0.0002009500426119712,
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-0.00002397986776071756, -0.00008202868926605841,
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+0.00012448715042090092, -0.0001749114214822577,
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+0.0001703481934140054, -0.00012062065004116828,
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+0.000059719705868660826, -0.00001980756723965647,
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+0.000003953714684212874, -0.00000036555001439719544])
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tfit = np.array([+0.9999999999999968, -0.49999999999986866, -0.025000000000632055,
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-0.003928571496460683, -0.0008164666077062752, -0.00019411896443261646,
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-0.00004985822229871769, -0.000014164962366386031, -1.9000248160936107e-6,
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-5.72184549898506e-6, +7.772149920658778e-6, -0.00001053483452909705,
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+9.528014229335313e-6, -5.660288876265125e-6, +1.2844901692764126e-6,
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+1.1255185726258763e-6, -1.3834391419956455e-6, +7.513691751164847e-7,
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-2.401996891720091e-7, +4.386887017466388e-8, -3.5917775353564864e-9])
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hmag_squared = np.sum(ho**2.,axis=-1,keepdims=True)
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s = np.sum(tfit*hmag_squared**np.arange(len(tfit)),axis=-1,keepdims=True)
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with np.errstate(invalid='ignore'):
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@ -1679,7 +1678,7 @@ class Rotation:
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"""
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with np.errstate(invalid='ignore',divide='ignore'):
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# get pyramide and scale by grid parameter ratio
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# get pyramid and scale by grid parameter ratio
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XYZ = np.take_along_axis(cu,Rotation._get_pyramid_order(cu,'forward'),-1) * _sc
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order = np.abs(XYZ[...,1:2]) <= np.abs(XYZ[...,0:1])
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q = np.pi/12.0 * np.where(order,XYZ[...,1:2],XYZ[...,0:1]) \
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@ -1,43 +1,25 @@
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"""Miscellaneous helper functionality."""
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import sys
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import datetime
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import os
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import subprocess
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import shlex
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import re
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import signal
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import fractions
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from collections import abc
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from functools import reduce, partial
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from typing import Callable, Union, Iterable, Sequence, Dict, List, Tuple, Literal, Any, Collection, TextIO
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from pathlib import Path
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import sys as _sys
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import datetime as _datetime
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import os as _os
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import subprocess as _subprocess
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import shlex as _shlex
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import re as _re
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import signal as _signal
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import fractions as _fractions
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from collections import abc as _abc
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from functools import reduce as _reduce, partial as _partial
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from typing import Callable as _Callable, Union as _Union, Iterable as _Iterable, Sequence as _Sequence, Dict as _Dict, \
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List as _List, Tuple as _Tuple, Literal as _Literal, Any as _Any, Collection as _Collection, TextIO as _TextIO
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from pathlib import Path as _Path
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import numpy as np
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import h5py
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import numpy as _np
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import h5py as _h5py
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from . import version
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from ._typehints import FloatSequence, NumpyRngSeed, IntCollection, FileHandle
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# limit visibility
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__all__=[
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'srepr',
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'emph', 'deemph', 'warn', 'strikeout',
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'run',
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'open_text',
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'natural_sort',
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'show_progress',
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'scale_to_coprime',
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'project_equal_angle', 'project_equal_area',
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'hybrid_IA',
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'execution_stamp',
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'shapeshifter', 'shapeblender',
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'extend_docstring', 'extended_docstring',
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'Bravais_to_Miller', 'Miller_to_Bravais',
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'DREAM3D_base_group', 'DREAM3D_cell_data_group',
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'dict_prune', 'dict_flatten',
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'tail_repack',
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]
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from . import version as _version
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from ._typehints import FloatSequence as _FloatSequence, NumpyRngSeed as _NumpyRngSeed, IntCollection as _IntCollection, \
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FileHandle as _FileHandle
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# https://svn.blender.org/svnroot/bf-blender/trunk/blender/build_files/scons/tools/bcolors.py
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# https://stackoverflow.com/questions/287871
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@ -154,8 +136,8 @@ def strikeout(msg) -> str:
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def run(cmd: str,
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wd: str = './',
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env: Dict[str, str] = None,
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timeout: int = None) -> Tuple[str, str]:
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env: _Dict[str, str] = None,
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timeout: int = None) -> _Tuple[str, str]:
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"""
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Run a command.
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@ -178,26 +160,26 @@ def run(cmd: str,
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"""
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def pass_signal(sig,_,proc,default):
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proc.send_signal(sig)
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signal.signal(sig,default)
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signal.raise_signal(sig)
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_signal.signal(sig,default)
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_signal.raise_signal(sig)
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signals = [signal.SIGINT,signal.SIGTERM]
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signals = [_signal.SIGINT,_signal.SIGTERM]
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print(f"running '{cmd}' in '{wd}'")
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process = subprocess.Popen(shlex.split(cmd),
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stdout = subprocess.PIPE,
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stderr = subprocess.PIPE,
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env = os.environ if env is None else env,
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cwd = wd,
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encoding = 'utf-8')
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process = _subprocess.Popen(_shlex.split(cmd),
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stdout = _subprocess.PIPE,
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stderr = _subprocess.PIPE,
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env = _os.environ if env is None else env,
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cwd = wd,
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encoding = 'utf-8')
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# ensure that process is terminated (https://stackoverflow.com/questions/22916783)
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sig_states = [signal.signal(sig,partial(pass_signal,proc=process,default=signal.getsignal(sig))) for sig in signals]
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sig_states = [_signal.signal(sig,_partial(pass_signal,proc=process,default=_signal.getsignal(sig))) for sig in signals]
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try:
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stdout,stderr = process.communicate(timeout=timeout)
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finally:
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for sig,state in zip(signals,sig_states):
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signal.signal(sig,state)
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_signal.signal(sig,state)
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if process.returncode != 0:
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print(stdout)
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|
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@ -207,8 +189,8 @@ def run(cmd: str,
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return stdout, stderr
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def open_text(fname: FileHandle,
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mode: Literal['r','w'] = 'r') -> TextIO:
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def open_text(fname: _FileHandle,
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mode: _Literal['r','w'] = 'r') -> _TextIO: # noqa
|
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"""
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Open a text file.
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|
|
@ -224,11 +206,11 @@ def open_text(fname: FileHandle,
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f : file handle
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"""
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return fname if not isinstance(fname, (str,Path)) else \
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open(Path(fname).expanduser(),mode,newline=('\n' if mode == 'w' else None))
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return fname if not isinstance(fname, (str,_Path)) else \
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open(_Path(fname).expanduser(),mode,newline=('\n' if mode == 'w' else None))
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|
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|
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def natural_sort(key: str) -> List[Union[int, str]]:
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def natural_sort(key: str) -> _List[_Union[int, str]]:
|
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"""
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Natural sort.
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|
|
@ -240,13 +222,13 @@ def natural_sort(key: str) -> List[Union[int, str]]:
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|
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"""
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convert = lambda text: int(text) if text.isdigit() else text
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return [ convert(c) for c in re.split('([0-9]+)', key) ]
|
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return [ convert(c) for c in _re.split('([0-9]+)', key) ]
|
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|
||||
|
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def show_progress(iterable: Iterable,
|
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def show_progress(iterable: _Iterable,
|
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N_iter: int = None,
|
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prefix: str = '',
|
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bar_length: int = 50) -> Any:
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bar_length: int = 50) -> _Any:
|
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"""
|
||||
Decorate a loop with a progress bar.
|
||||
|
||||
|
|
@ -264,7 +246,7 @@ def show_progress(iterable: Iterable,
|
|||
Length of progress bar in characters. Defaults to 50.
|
||||
|
||||
"""
|
||||
if isinstance(iterable,abc.Sequence):
|
||||
if isinstance(iterable,_abc.Sequence):
|
||||
if N_iter is None:
|
||||
N = len(iterable)
|
||||
else:
|
||||
|
|
@ -285,7 +267,7 @@ def show_progress(iterable: Iterable,
|
|||
status.update(i)
|
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|
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|
||||
def scale_to_coprime(v: FloatSequence) -> np.ndarray:
|
||||
def scale_to_coprime(v: _FloatSequence) -> _np.ndarray:
|
||||
"""
|
||||
Scale vector to co-prime (relatively prime) integers.
|
||||
|
||||
|
|
@ -304,30 +286,30 @@ def scale_to_coprime(v: FloatSequence) -> np.ndarray:
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|||
|
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def get_square_denominator(x):
|
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"""Denominator of the square of a number."""
|
||||
return fractions.Fraction(x ** 2).limit_denominator(MAX_DENOMINATOR).denominator
|
||||
return _fractions.Fraction(x ** 2).limit_denominator(MAX_DENOMINATOR).denominator
|
||||
|
||||
def lcm(a,b):
|
||||
"""Least common multiple."""
|
||||
try:
|
||||
return np.lcm(a,b) # numpy > 1.18
|
||||
return _np.lcm(a,b) # numpy > 1.18
|
||||
except AttributeError:
|
||||
return a * b // np.gcd(a, b)
|
||||
return a * b // _np.gcd(a, b)
|
||||
|
||||
v_ = np.array(v)
|
||||
m = (v_ * reduce(lcm, map(lambda x: int(get_square_denominator(x)),v_))**0.5).astype(np.int64)
|
||||
m = m//reduce(np.gcd,m)
|
||||
v_ = _np.array(v)
|
||||
m = (v_ * _reduce(lcm, map(lambda x: int(get_square_denominator(x)),v_))**0.5).astype(_np.int64)
|
||||
m = m//_reduce(_np.gcd,m)
|
||||
|
||||
with np.errstate(invalid='ignore'):
|
||||
if not np.allclose(np.ma.masked_invalid(v_/m),v_[np.argmax(abs(v_))]/m[np.argmax(abs(v_))]):
|
||||
with _np.errstate(invalid='ignore'):
|
||||
if not _np.allclose(_np.ma.masked_invalid(v_/m),v_[_np.argmax(abs(v_))]/m[_np.argmax(abs(v_))]):
|
||||
raise ValueError(f'invalid result "{m}" for input "{v_}"')
|
||||
|
||||
return m
|
||||
|
||||
|
||||
def project_equal_angle(vector: np.ndarray,
|
||||
direction: Literal['x', 'y', 'z'] = 'z',
|
||||
def project_equal_angle(vector: _np.ndarray,
|
||||
direction: _Literal['x', 'y', 'z'] = 'z', # noqa
|
||||
normalize: bool = True,
|
||||
keepdims: bool = False) -> np.ndarray:
|
||||
keepdims: bool = False) -> _np.ndarray:
|
||||
"""
|
||||
Apply equal-angle projection to vector.
|
||||
|
||||
|
|
@ -367,15 +349,15 @@ def project_equal_angle(vector: np.ndarray,
|
|||
|
||||
"""
|
||||
shift = 'zyx'.index(direction)
|
||||
v = np.roll(vector/np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||||
shift,axis=-1)
|
||||
return np.roll(np.block([v[...,:2]/(1.0+np.abs(v[...,2:3])),np.zeros_like(v[...,2:3])]),
|
||||
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
|
||||
v = _np.roll(vector/_np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||||
shift,axis=-1)
|
||||
return _np.roll(_np.block([v[...,:2]/(1.0+_np.abs(v[...,2:3])),_np.zeros_like(v[...,2:3])]),
|
||||
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
|
||||
|
||||
def project_equal_area(vector: np.ndarray,
|
||||
direction: Literal['x', 'y', 'z'] = 'z',
|
||||
def project_equal_area(vector: _np.ndarray,
|
||||
direction: _Literal['x', 'y', 'z'] = 'z', # noqa
|
||||
normalize: bool = True,
|
||||
keepdims: bool = False) -> np.ndarray:
|
||||
keepdims: bool = False) -> _np.ndarray:
|
||||
"""
|
||||
Apply equal-area projection to vector.
|
||||
|
||||
|
|
@ -416,22 +398,22 @@ def project_equal_area(vector: np.ndarray,
|
|||
|
||||
"""
|
||||
shift = 'zyx'.index(direction)
|
||||
v = np.roll(vector/np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||||
shift,axis=-1)
|
||||
return np.roll(np.block([v[...,:2]/np.sqrt(1.0+np.abs(v[...,2:3])),np.zeros_like(v[...,2:3])]),
|
||||
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
|
||||
v = _np.roll(vector/_np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||||
shift,axis=-1)
|
||||
return _np.roll(_np.block([v[...,:2]/_np.sqrt(1.0+_np.abs(v[...,2:3])),_np.zeros_like(v[...,2:3])]),
|
||||
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
|
||||
|
||||
def execution_stamp(class_name: str,
|
||||
function_name: str = None) -> str:
|
||||
"""Timestamp the execution of a (function within a) class."""
|
||||
now = datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S%z')
|
||||
now = _datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S%z')
|
||||
_function_name = '' if function_name is None else f'.{function_name}'
|
||||
return f'damask.{class_name}{_function_name} v{version} ({now})'
|
||||
return f'damask.{class_name}{_function_name} v{_version} ({now})'
|
||||
|
||||
|
||||
def hybrid_IA(dist: np.ndarray,
|
||||
def hybrid_IA(dist: _np.ndarray,
|
||||
N: int,
|
||||
rng_seed: NumpyRngSeed = None) -> np.ndarray:
|
||||
rng_seed: _NumpyRngSeed = None) -> _np.ndarray:
|
||||
"""
|
||||
Hybrid integer approximation.
|
||||
|
||||
|
|
@ -446,23 +428,23 @@ def hybrid_IA(dist: np.ndarray,
|
|||
If None, then fresh, unpredictable entropy will be pulled from the OS.
|
||||
|
||||
"""
|
||||
N_opt_samples,N_inv_samples = (max(np.count_nonzero(dist),N),0) # random subsampling if too little samples requested
|
||||
N_opt_samples,N_inv_samples = (max(_np.count_nonzero(dist),N),0) # random subsampling if too little samples requested
|
||||
|
||||
scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0)
|
||||
while (not np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
|
||||
repeats = np.rint(scale*dist).astype(np.int64)
|
||||
N_inv_samples = np.sum(repeats)
|
||||
while (not _np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
|
||||
repeats = _np.rint(scale*dist).astype(_np.int64)
|
||||
N_inv_samples = _np.sum(repeats)
|
||||
scale_,scale,inc_factor = (scale,scale+inc_factor*0.5*(scale - scale_), inc_factor*2.0) \
|
||||
if N_inv_samples < N_opt_samples else \
|
||||
(scale_,0.5*(scale_ + scale), 1.0)
|
||||
|
||||
return np.repeat(np.arange(len(dist)),repeats)[np.random.default_rng(rng_seed).permutation(N_inv_samples)[:N]]
|
||||
return _np.repeat(_np.arange(len(dist)),repeats)[_np.random.default_rng(rng_seed).permutation(N_inv_samples)[:N]]
|
||||
|
||||
|
||||
def shapeshifter(fro: Tuple[int, ...],
|
||||
to: Tuple[int, ...],
|
||||
mode: Literal['left','right'] = 'left',
|
||||
keep_ones: bool = False) -> Tuple[int, ...]:
|
||||
def shapeshifter(fro: _Tuple[int, ...],
|
||||
to: _Tuple[int, ...],
|
||||
mode: _Literal['left','right'] = 'left', # noqa
|
||||
keep_ones: bool = False) -> _Tuple[int, ...]:
|
||||
"""
|
||||
Return dimensions that reshape 'fro' to become broadcastable to 'to'.
|
||||
|
||||
|
|
@ -509,7 +491,7 @@ def shapeshifter(fro: Tuple[int, ...],
|
|||
fro = (1,) if len(fro) == 0 else fro
|
||||
to = (1,) if len(to) == 0 else to
|
||||
try:
|
||||
match = re.match(beg[mode]
|
||||
match = _re.match(beg[mode]
|
||||
+f',{sep[mode]}'.join(map(lambda x: f'{x}'
|
||||
if x>1 or (keep_ones and len(fro)>1) else
|
||||
'\\d+',fro))
|
||||
|
|
@ -518,14 +500,14 @@ def shapeshifter(fro: Tuple[int, ...],
|
|||
grp = match.groups()
|
||||
except AssertionError:
|
||||
raise ValueError(f'shapes cannot be shifted {fro} --> {to}')
|
||||
fill: Any = ()
|
||||
fill: _Any = ()
|
||||
for g,d in zip(grp,fro+(None,)):
|
||||
fill += (1,)*g.count(',')+(d,)
|
||||
return fill[:-1]
|
||||
|
||||
|
||||
def shapeblender(a: Tuple[int, ...],
|
||||
b: Tuple[int, ...]) -> Tuple[int, ...]:
|
||||
def shapeblender(a: _Tuple[int, ...],
|
||||
b: _Tuple[int, ...]) -> _Tuple[int, ...]:
|
||||
"""
|
||||
Return a shape that overlaps the rightmost entries of 'a' with the leftmost of 'b'.
|
||||
|
||||
|
|
@ -553,7 +535,7 @@ def shapeblender(a: Tuple[int, ...],
|
|||
return a + b[i:]
|
||||
|
||||
|
||||
def extend_docstring(extra_docstring: str) -> Callable:
|
||||
def extend_docstring(extra_docstring: str) -> _Callable:
|
||||
"""
|
||||
Decorator: Append to function's docstring.
|
||||
|
||||
|
|
@ -569,8 +551,8 @@ def extend_docstring(extra_docstring: str) -> Callable:
|
|||
return _decorator
|
||||
|
||||
|
||||
def extended_docstring(f: Callable,
|
||||
extra_docstring: str) -> Callable:
|
||||
def extended_docstring(f: _Callable,
|
||||
extra_docstring: str) -> _Callable:
|
||||
"""
|
||||
Decorator: Combine another function's docstring with a given docstring.
|
||||
|
||||
|
|
@ -588,7 +570,7 @@ def extended_docstring(f: Callable,
|
|||
return _decorator
|
||||
|
||||
|
||||
def DREAM3D_base_group(fname: Union[str, Path]) -> str:
|
||||
def DREAM3D_base_group(fname: _Union[str, _Path]) -> str:
|
||||
"""
|
||||
Determine the base group of a DREAM.3D file.
|
||||
|
||||
|
|
@ -606,7 +588,7 @@ def DREAM3D_base_group(fname: Union[str, Path]) -> str:
|
|||
Path to the base group.
|
||||
|
||||
"""
|
||||
with h5py.File(Path(fname).expanduser(),'r') as f:
|
||||
with _h5py.File(_Path(fname).expanduser(),'r') as f:
|
||||
base_group = f.visit(lambda path: path.rsplit('/',2)[0] if '_SIMPL_GEOMETRY/SPACING' in path else None)
|
||||
|
||||
if base_group is None:
|
||||
|
|
@ -614,7 +596,7 @@ def DREAM3D_base_group(fname: Union[str, Path]) -> str:
|
|||
|
||||
return base_group
|
||||
|
||||
def DREAM3D_cell_data_group(fname: Union[str, Path]) -> str:
|
||||
def DREAM3D_cell_data_group(fname: _Union[str, _Path]) -> str:
|
||||
"""
|
||||
Determine the cell data group of a DREAM.3D file.
|
||||
|
||||
|
|
@ -634,10 +616,10 @@ def DREAM3D_cell_data_group(fname: Union[str, Path]) -> str:
|
|||
|
||||
"""
|
||||
base_group = DREAM3D_base_group(fname)
|
||||
with h5py.File(Path(fname).expanduser(),'r') as f:
|
||||
with _h5py.File(_Path(fname).expanduser(),'r') as 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 \
|
||||
if isinstance(obj,_h5py._hl.dataset.Dataset) and _np.shape(obj)[:-1] == cells \
|
||||
else None)
|
||||
|
||||
if cell_data_group is None:
|
||||
|
|
@ -647,8 +629,8 @@ def DREAM3D_cell_data_group(fname: Union[str, Path]) -> str:
|
|||
|
||||
|
||||
def Bravais_to_Miller(*,
|
||||
uvtw: np.ndarray = None,
|
||||
hkil: np.ndarray = None) -> np.ndarray:
|
||||
uvtw: _np.ndarray = None,
|
||||
hkil: _np.ndarray = None) -> _np.ndarray:
|
||||
"""
|
||||
Transform 4 Miller–Bravais indices to 3 Miller indices of crystal direction [uvw] or plane normal (hkl).
|
||||
|
||||
|
|
@ -665,19 +647,19 @@ def Bravais_to_Miller(*,
|
|||
"""
|
||||
if (uvtw is not None) ^ (hkil is None):
|
||||
raise KeyError('specify either "uvtw" or "hkil"')
|
||||
axis,basis = (np.array(uvtw),np.array([[1,0,-1,0],
|
||||
[0,1,-1,0],
|
||||
[0,0, 0,1]])) \
|
||||
axis,basis = (_np.array(uvtw),_np.array([[1,0,-1,0],
|
||||
[0,1,-1,0],
|
||||
[0,0, 0,1]])) \
|
||||
if hkil is None else \
|
||||
(np.array(hkil),np.array([[1,0,0,0],
|
||||
[0,1,0,0],
|
||||
[0,0,0,1]]))
|
||||
return np.einsum('il,...l',basis,axis)
|
||||
(_np.array(hkil),_np.array([[1,0,0,0],
|
||||
[0,1,0,0],
|
||||
[0,0,0,1]]))
|
||||
return _np.einsum('il,...l',basis,axis)
|
||||
|
||||
|
||||
def Miller_to_Bravais(*,
|
||||
uvw: np.ndarray = None,
|
||||
hkl: np.ndarray = None) -> np.ndarray:
|
||||
uvw: _np.ndarray = None,
|
||||
hkl: _np.ndarray = None) -> _np.ndarray:
|
||||
"""
|
||||
Transform 3 Miller indices to 4 Miller–Bravais indices of crystal direction [uvtw] or plane normal (hkil).
|
||||
|
||||
|
|
@ -694,19 +676,19 @@ def Miller_to_Bravais(*,
|
|||
"""
|
||||
if (uvw is not None) ^ (hkl is None):
|
||||
raise KeyError('specify either "uvw" or "hkl"')
|
||||
axis,basis = (np.array(uvw),np.array([[ 2,-1, 0],
|
||||
[-1, 2, 0],
|
||||
[-1,-1, 0],
|
||||
[ 0, 0, 3]])/3) \
|
||||
axis,basis = (_np.array(uvw),_np.array([[ 2,-1, 0],
|
||||
[-1, 2, 0],
|
||||
[-1,-1, 0],
|
||||
[ 0, 0, 3]])/3) \
|
||||
if hkl is None else \
|
||||
(np.array(hkl),np.array([[ 1, 0, 0],
|
||||
[ 0, 1, 0],
|
||||
[-1,-1, 0],
|
||||
[ 0, 0, 1]]))
|
||||
return np.einsum('il,...l',basis,axis)
|
||||
(_np.array(hkl),_np.array([[ 1, 0, 0],
|
||||
[ 0, 1, 0],
|
||||
[-1,-1, 0],
|
||||
[ 0, 0, 1]]))
|
||||
return _np.einsum('il,...l',basis,axis)
|
||||
|
||||
|
||||
def dict_prune(d: Dict) -> Dict:
|
||||
def dict_prune(d: _Dict) -> _Dict:
|
||||
"""
|
||||
Recursively remove empty dictionaries.
|
||||
|
||||
|
|
@ -732,7 +714,7 @@ def dict_prune(d: Dict) -> Dict:
|
|||
return new
|
||||
|
||||
|
||||
def dict_flatten(d: Dict) -> Dict:
|
||||
def dict_flatten(d: _Dict) -> _Dict:
|
||||
"""
|
||||
Recursively remove keys of single-entry dictionaries.
|
||||
|
||||
|
|
@ -756,8 +738,8 @@ def dict_flatten(d: Dict) -> Dict:
|
|||
return new
|
||||
|
||||
|
||||
def tail_repack(extended: Union[str, Sequence[str]],
|
||||
existing: List[str] = []) -> List[str]:
|
||||
def tail_repack(extended: _Union[str, _Sequence[str]],
|
||||
existing: _List[str] = []) -> _List[str]:
|
||||
"""
|
||||
Repack tailing characters into single string if all are new.
|
||||
|
||||
|
|
@ -782,11 +764,11 @@ def tail_repack(extended: Union[str, Sequence[str]],
|
|||
|
||||
"""
|
||||
return [extended] if isinstance(extended,str) else existing + \
|
||||
([''.join(extended[len(existing):])] if np.prod([len(i) for i in extended[len(existing):]]) == 1 else
|
||||
([''.join(extended[len(existing):])] if _np.prod([len(i) for i in extended[len(existing):]]) == 1 else
|
||||
list(extended[len(existing):]))
|
||||
|
||||
|
||||
def aslist(arg: Union[IntCollection,int,None]) -> List:
|
||||
def aslist(arg: _Union[_IntCollection, int, None]) -> _List:
|
||||
"""
|
||||
Transform argument to list.
|
||||
|
||||
|
|
@ -801,7 +783,7 @@ def aslist(arg: Union[IntCollection,int,None]) -> List:
|
|||
Entity transformed into list.
|
||||
|
||||
"""
|
||||
return [] if arg is None else list(arg) if isinstance(arg,(np.ndarray,Collection)) else [arg]
|
||||
return [] if arg is None else list(arg) if isinstance(arg,(_np.ndarray,_Collection)) else [arg]
|
||||
|
||||
|
||||
####################################################################################################
|
||||
|
|
@ -834,11 +816,11 @@ class ProgressBar:
|
|||
self.total = total
|
||||
self.prefix = prefix
|
||||
self.bar_length = bar_length
|
||||
self.time_start = self.time_last_update = datetime.datetime.now()
|
||||
self.time_start = self.time_last_update = _datetime.datetime.now()
|
||||
self.fraction_last = 0.0
|
||||
|
||||
sys.stderr.write(f"{self.prefix} {'░'*self.bar_length} 0% ETA n/a")
|
||||
sys.stderr.flush()
|
||||
_sys.stderr.write(f"{self.prefix} {'░'*self.bar_length} 0% ETA n/a")
|
||||
_sys.stderr.flush()
|
||||
|
||||
def update(self,
|
||||
iteration: int) -> None:
|
||||
|
|
@ -846,17 +828,17 @@ class ProgressBar:
|
|||
fraction = (iteration+1) / self.total
|
||||
|
||||
if (filled_length := int(self.bar_length * fraction)) > int(self.bar_length * self.fraction_last) or \
|
||||
datetime.datetime.now() - self.time_last_update > datetime.timedelta(seconds=10):
|
||||
self.time_last_update = datetime.datetime.now()
|
||||
_datetime.datetime.now() - self.time_last_update > _datetime.timedelta(seconds=10):
|
||||
self.time_last_update = _datetime.datetime.now()
|
||||
bar = '█' * filled_length + '░' * (self.bar_length - filled_length)
|
||||
remaining_time = (datetime.datetime.now() - self.time_start) \
|
||||
remaining_time = (_datetime.datetime.now() - self.time_start) \
|
||||
* (self.total - (iteration+1)) / (iteration+1)
|
||||
remaining_time -= datetime.timedelta(microseconds=remaining_time.microseconds) # remove μs
|
||||
sys.stderr.write(f'\r{self.prefix} {bar} {fraction:>4.0%} ETA {remaining_time}')
|
||||
sys.stderr.flush()
|
||||
remaining_time -= _datetime.timedelta(microseconds=remaining_time.microseconds) # remove μs
|
||||
_sys.stderr.write(f'\r{self.prefix} {bar} {fraction:>4.0%} ETA {remaining_time}')
|
||||
_sys.stderr.flush()
|
||||
|
||||
self.fraction_last = fraction
|
||||
|
||||
if iteration == self.total - 1:
|
||||
sys.stderr.write('\n')
|
||||
sys.stderr.flush()
|
||||
_sys.stderr.write('\n')
|
||||
_sys.stderr.flush()
|
||||
|
|
|
|||
|
|
@ -224,11 +224,11 @@ class TestOrientation:
|
|||
|
||||
@pytest.mark.parametrize('family',crystal_families)
|
||||
def test_reduced_corner_cases(self,family):
|
||||
# test whether there is always a sym-eq rotation that falls into the FZ
|
||||
# test whether there is always exactly one sym-eq rotation that falls into the FZ
|
||||
N = np.random.randint(10,40)
|
||||
size = np.ones(3)*np.pi**(2./3.)
|
||||
grid = grid_filters.coordinates0_node([N+1,N+1,N+1],size,-size*.5)
|
||||
evenly_distributed = Orientation.from_cubochoric(x=grid[:-2,:-2,:-2],family=family)
|
||||
evenly_distributed = Orientation.from_cubochoric(x=grid,family=family)
|
||||
assert evenly_distributed.shape == evenly_distributed.reduced.shape
|
||||
|
||||
@pytest.mark.parametrize('family',crystal_families)
|
||||
|
|
|
|||
|
|
@ -301,14 +301,13 @@ def ro2ho(ro):
|
|||
#---------- Homochoric vector----------
|
||||
def ho2ax(ho):
|
||||
"""Homochoric vector to axis angle pair."""
|
||||
tfit = np.array([+1.0000000000018852, -0.5000000002194847,
|
||||
-0.024999992127593126, -0.003928701544781374,
|
||||
-0.0008152701535450438, -0.0002009500426119712,
|
||||
-0.00002397986776071756, -0.00008202868926605841,
|
||||
+0.00012448715042090092, -0.0001749114214822577,
|
||||
+0.0001703481934140054, -0.00012062065004116828,
|
||||
+0.000059719705868660826, -0.00001980756723965647,
|
||||
+0.000003953714684212874, -0.00000036555001439719544])
|
||||
tfit = np.array([+0.9999999999999968, -0.49999999999986866, -0.025000000000632055,
|
||||
-0.003928571496460683, -0.0008164666077062752, -0.00019411896443261646,
|
||||
-0.00004985822229871769, -0.000014164962366386031, -1.9000248160936107e-6,
|
||||
-5.72184549898506e-6, +7.772149920658778e-6, -0.00001053483452909705,
|
||||
+9.528014229335313e-6, -5.660288876265125e-6, +1.2844901692764126e-6,
|
||||
+1.1255185726258763e-6, -1.3834391419956455e-6, +7.513691751164847e-7,
|
||||
-2.401996891720091e-7, +4.386887017466388e-8, -3.5917775353564864e-9])
|
||||
# normalize h and store the magnitude
|
||||
hmag_squared = np.sum(ho**2.)
|
||||
if iszero(hmag_squared):
|
||||
|
|
|
|||
Loading…
Reference in New Issue