957 lines
32 KiB
Python
957 lines
32 KiB
Python
"""Miscellaneous helper functionality."""
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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, OrderedDict as _OrderedDict
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from functools import reduce as _reduce, partial as _partial, wraps as _wraps
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import inspect
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from typing import Optional as _Optional, Callable as _Callable, Union as _Union, Iterable as _Iterable, \
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Dict as _Dict, List as _List, Tuple as _Tuple, Literal as _Literal, \
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Any as _Any, 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 as _h5py
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from . import version as _version
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from ._typehints import FloatSequence as _FloatSequence, NumpyRngSeed as _NumpyRngSeed, 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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_colors = {
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'header' : '\033[95m',
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'OK_blue': '\033[94m',
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'OK_green': '\033[92m',
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'warning': '\033[93m',
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'fail': '\033[91m',
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'end_color': '\033[0m',
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'bold': '\033[1m',
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'dim': '\033[2m',
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'underline': '\033[4m',
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'crossout': '\033[9m'
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}
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####################################################################################################
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# Functions
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####################################################################################################
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def srepr(msg,
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glue: str = '\n',
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quote: bool = False) -> str:
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r"""
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Join (quoted) items with glue string.
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Parameters
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----------
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msg : (sequence of) object with __repr__
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Items to join.
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glue : str, optional
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Glue used for joining operation. Defaults to '\n'.
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quote : bool, optional
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Quote items. Defaults to False.
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Returns
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-------
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joined : str
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String representation of the joined and quoted items.
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"""
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q = '"' if quote else ''
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if (not hasattr(msg, 'strip') and
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(hasattr(msg, '__getitem__') or
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hasattr(msg, '__iter__'))):
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return glue.join(q+str(x)+q for x in msg)
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else:
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return q+(msg if isinstance(msg,str) else repr(msg))+q
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def emph(msg) -> str:
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"""
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Format with emphasis.
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Parameters
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----------
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msg : (sequence of) object with __repr__
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Message to format.
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Returns
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-------
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formatted : str
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Formatted string representation of the joined items.
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"""
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return _colors['bold']+srepr(msg)+_colors['end_color']
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def deemph(msg) -> str:
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"""
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Format with deemphasis.
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Parameters
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----------
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msg : (sequence of) object with __repr__
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Message to format.
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Returns
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-------
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formatted : str
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Formatted string representation of the joined items.
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"""
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return _colors['dim']+srepr(msg)+_colors['end_color']
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def warn(msg) -> str:
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"""
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Format for warning.
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Parameters
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----------
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msg : (sequence of) object with __repr__
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Message to format.
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Returns
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-------
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formatted : str
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Formatted string representation of the joined items.
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"""
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return _colors['warning']+emph(msg)+_colors['end_color']
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def strikeout(msg) -> str:
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"""
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Format as strikeout.
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Parameters
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----------
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msg : (iterable of) object with __repr__
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Message to format.
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Returns
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-------
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formatted : str
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Formatted string representation of the joined items.
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"""
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return _colors['crossout']+srepr(msg)+_colors['end_color']
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def run(cmd: str,
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wd: str = './',
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env: _Optional[_Dict[str, str]] = None,
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timeout: _Optional[int] = None) -> _Tuple[str, str]:
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"""
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Run a command.
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Parameters
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----------
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cmd : str
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Command to be executed.
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wd : str, optional
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Working directory of process. Defaults to './'.
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env : dict, optional
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Environment for execution.
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timeout : integer, optional
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Timeout in seconds.
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Returns
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-------
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stdout, stderr : (str, str)
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Output of the executed command.
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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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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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# 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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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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if process.returncode != 0:
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print(stdout)
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print(stderr)
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raise RuntimeError(f"'{cmd}' failed with returncode {process.returncode}")
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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: # noqa
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"""
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Open a text file.
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Parameters
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----------
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fname : file, str, or pathlib.Path
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Name or handle of file.
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mode: {'r','w'}, optional
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Access mode: 'r'ead or 'w'rite, defaults to 'r'.
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Returns
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-------
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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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def time_stamp() -> str:
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"""Timestamp the execution of a (function within a) class."""
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return _datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S%z')
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def execution_stamp(class_name: str,
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function_name: _Optional[str] = None) -> str:
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"""Timestamp the execution of a (function within a) class."""
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_function_name = '' if function_name is None else f'.{function_name}'
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return f'damask.{class_name}{_function_name} v{_version} ({time_stamp()})'
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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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For use in python's 'sorted'.
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References
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----------
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https://en.wikipedia.org/wiki/Natural_sort_order
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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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def show_progress(iterable: _Iterable,
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N_iter: _Optional[int] = None,
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prefix: str = '',
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bar_length: int = 50) -> _Any:
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"""
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Decorate a loop with a progress bar.
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Use similar like enumerate.
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Parameters
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----------
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iterable : iterable
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Iterable to be decorated.
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N_iter : int, optional
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Total number of iterations. Required if iterable is not a sequence.
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prefix : str, optional
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Prefix string.
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bar_length : int, optional
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Length of progress bar in characters. Defaults to 50.
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"""
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if isinstance(iterable,_abc.Sequence):
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if N_iter is None:
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N = len(iterable)
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else:
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raise ValueError('N_iter given for sequence')
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else:
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if N_iter is None:
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raise ValueError('N_iter not given')
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N = N_iter
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if N <= 1:
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for item in iterable:
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yield item
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else:
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status = ProgressBar(N,prefix,bar_length)
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for i,item in enumerate(iterable):
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yield item
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status.update(i)
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def scale_to_coprime(v: _FloatSequence) -> _np.ndarray:
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"""
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Scale vector to co-prime (relatively prime) integers.
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Parameters
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----------
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v : sequence of float, len (:)
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Vector to scale.
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Returns
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-------
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m : numpy.ndarray, shape (:)
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Vector scaled to co-prime numbers.
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"""
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MAX_DENOMINATOR = 1000000
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def get_square_denominator(x):
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"""Denominator of the square of a number."""
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return _fractions.Fraction(x ** 2).limit_denominator(MAX_DENOMINATOR).denominator
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def lcm(a,b):
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"""Least common multiple."""
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try:
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return _np.lcm(a,b) # numpy > 1.18
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except AttributeError:
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return a * b // _np.gcd(a, b)
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v_ = _np.array(v)
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m = (v_ * _reduce(lcm, map(lambda x: int(get_square_denominator(x)),v_))**0.5).astype(_np.int64)
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m = m//_reduce(_np.gcd,m)
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with _np.errstate(invalid='ignore'):
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if not _np.allclose(_np.ma.masked_invalid(v_/m),v_[_np.argmax(abs(v_))]/m[_np.argmax(abs(v_))]):
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raise ValueError(f'invalid result "{m}" for input "{v_}"')
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return m
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def project_equal_angle(vector: _np.ndarray,
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direction: _Literal['x', 'y', 'z'] = 'z', # noqa
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normalize: bool = True,
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keepdims: bool = False) -> _np.ndarray:
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"""
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Apply equal-angle projection to vector.
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Parameters
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----------
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vector : numpy.ndarray, shape (...,3)
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Vector coordinates to be projected.
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direction : {'x', 'y', 'z'}
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Projection direction. Defaults to 'z'.
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normalize : bool
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Ensure unit length of input vector. Defaults to True.
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keepdims : bool
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Maintain three-dimensional output coordinates.
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Defaults to False.
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Returns
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-------
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coordinates : numpy.ndarray, shape (...,2 | 3)
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Projected coordinates.
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Notes
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-----
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Two-dimensional output uses right-handed frame spanned by
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the next and next-next axis relative to the projection direction,
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e.g. x-y when projecting along z and z-x when projecting along y.
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Examples
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--------
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>>> import damask
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>>> import numpy as np
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>>> project_equal_angle(np.ones(3))
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[0.3660254, 0.3660254]
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>>> project_equal_angle(np.ones(3),direction='x',normalize=False,keepdims=True)
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[0, 0.5, 0.5]
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>>> project_equal_angle([0,1,1],direction='y',normalize=True,keepdims=False)
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[0.41421356, 0]
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"""
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shift = 'zyx'.index(direction)
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v = _np.roll(vector/_np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
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shift,axis=-1)
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return _np.roll(_np.block([v[...,:2]/(1.0+_np.abs(v[...,2:3])),_np.zeros_like(v[...,2:3])]),
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-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
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def project_equal_area(vector: _np.ndarray,
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direction: _Literal['x', 'y', 'z'] = 'z', # noqa
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normalize: bool = True,
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keepdims: bool = False) -> _np.ndarray:
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"""
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Apply equal-area projection to vector.
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Parameters
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----------
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vector : numpy.ndarray, shape (...,3)
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Vector coordinates to be projected.
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direction : {'x', 'y', 'z'}
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Projection direction. Defaults to 'z'.
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normalize : bool
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Ensure unit length of input vector. Defaults to True.
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keepdims : bool
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Maintain three-dimensional output coordinates.
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Defaults to False.
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Returns
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-------
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coordinates : numpy.ndarray, shape (...,2 | 3)
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Projected coordinates.
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Notes
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-----
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Two-dimensional output uses right-handed frame spanned by
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the next and next-next axis relative to the projection direction,
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e.g. x-y when projecting along z and z-x when projecting along y.
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Examples
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--------
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>>> import damask
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>>> import numpy as np
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>>> project_equal_area(np.ones(3))
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[0.45970084, 0.45970084]
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>>> project_equal_area(np.ones(3),direction='x',normalize=False,keepdims=True)
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[0.0, 0.70710678, 0.70710678]
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>>> project_equal_area([0,1,1],direction='y',normalize=True,keepdims=False)
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[0.5411961, 0.0]
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"""
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shift = 'zyx'.index(direction)
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v = _np.roll(vector/_np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
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shift,axis=-1)
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return _np.roll(_np.block([v[...,:2]/_np.sqrt(1.0+_np.abs(v[...,2:3])),_np.zeros_like(v[...,2:3])]),
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-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
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def hybrid_IA(dist: _FloatSequence,
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N: int,
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rng_seed: _Optional[_NumpyRngSeed] = None) -> _np.ndarray:
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"""
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Hybrid integer approximation.
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Parameters
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----------
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dist : numpy.ndarray
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Distribution to be approximated.
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N : int
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Number of samples to draw.
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None.
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If None, then fresh, unpredictable entropy will be pulled from the OS.
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Returns
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-------
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hist : numpy.ndarray, shape (N)
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Integer approximation of the distribution.
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"""
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N_opt_samples = max(_np.count_nonzero(dist),N) # random subsampling if too little samples requested
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N_inv_samples = 0
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scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0)
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while (not _np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
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repeats = _np.rint(scale*_np.array(dist)).astype(_np.int64)
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N_inv_samples = _np.sum(repeats)
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scale_,scale,inc_factor = (scale,scale+inc_factor*0.5*(scale - scale_), inc_factor*2.0) \
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if N_inv_samples < N_opt_samples else \
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(scale_,0.5*(scale_ + scale), 1.0)
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return _np.repeat(_np.arange(len(dist)),repeats)[_np.random.default_rng(rng_seed).permutation(N_inv_samples)[:N]]
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def shapeshifter(fro: _Tuple[int, ...],
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to: _Tuple[int, ...],
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mode: _Literal['left','right'] = 'left', # noqa
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keep_ones: bool = False) -> _Tuple[int, ...]:
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"""
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Return dimensions that reshape 'fro' to become broadcastable to 'to'.
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Parameters
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----------
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fro : tuple
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Original shape of array.
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to : tuple
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Target shape of array after broadcasting.
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len(to) cannot be less than len(fro).
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mode : {'left', 'right'}, optional
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Indicates whether new axes are preferably added to
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either left or right of the original shape.
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Defaults to 'left'.
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keep_ones : bool, optional
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Treat '1' in fro as literal value instead of dimensional placeholder.
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Defaults to False.
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Returns
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-------
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new_dims : tuple
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Dimensions for reshape.
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Examples
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--------
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>>> import numpy as np
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>>> from damask import util
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>>> a = np.ones((3,4,2))
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>>> b = np.ones(4)
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>>> b_extended = b.reshape(util.shapeshifter(b.shape,a.shape))
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>>> (a * np.broadcast_to(b_extended,a.shape)).shape
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(3,4,2)
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"""
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if len(fro) == 0 and len(to) == 0: return tuple()
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_fro = [1] if len(fro) == 0 else list(fro)[::-1 if mode=='left' else 1]
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_to = [1] if len(to) == 0 else list(to) [::-1 if mode=='left' else 1]
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final_shape: _List[int] = []
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index = 0
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for i,item in enumerate(_to):
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if item == _fro[index]:
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final_shape.append(item)
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index+=1
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else:
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final_shape.append(1)
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if _fro[index] == 1 and not keep_ones:
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index+=1
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if index == len(_fro):
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final_shape = final_shape+[1]*(len(_to)-i-1)
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break
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if index != len(_fro): raise ValueError(f'shapes cannot be shifted {fro} --> {to}')
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return tuple(final_shape[::-1] if mode == 'left' else final_shape)
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def shapeblender(a: _Tuple[int, ...],
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b: _Tuple[int, ...],
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keep_ones: bool = False) -> _Tuple[int, ...]:
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"""
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Return a shape that overlaps the rightmost entries of 'a' with the leftmost of 'b'.
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Parameters
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----------
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a : tuple
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Shape of first array.
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b : tuple
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Shape of second array.
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keep_ones : bool, optional
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Treat innermost '1's as literal value instead of dimensional placeholder.
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Defaults to False.
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Examples
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--------
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>>> shapeblender((3,2),(3,2))
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(3,2)
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>>> shapeblender((4,3),(3,2))
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||
(4,3,2)
|
||
>>> shapeblender((4,4),(3,2))
|
||
(4,4,3,2)
|
||
>>> shapeblender((1,2),(1,2,3))
|
||
(1,2,3)
|
||
>>> shapeblender((),(2,2,1))
|
||
(2,2,1)
|
||
>>> shapeblender((1,),(2,2,1))
|
||
(2,2,1)
|
||
>>> shapeblender((1,),(2,2,1),True)
|
||
(1,2,2,1)
|
||
|
||
"""
|
||
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],
|
||
adopted_parameters: _Union[None, str, _Callable] = None,
|
||
adopted_return: _Union[None, str, _Callable] = None,
|
||
adopted_notes: _Union[None, str, _Callable] = None,
|
||
adopted_examples: _Union[None, str, _Callable] = None,
|
||
adopted_references: _Union[None, str, _Callable] = None) -> str:
|
||
"""
|
||
Extend a docstring.
|
||
|
||
Parameters
|
||
----------
|
||
docstring : str or callable, optional
|
||
Docstring (of callable) to extend.
|
||
adopted_* : str or callable, optional
|
||
Additional information to insert into/append to respective section.
|
||
|
||
Notes
|
||
-----
|
||
adopted_return fetches the typehint of a passed function instead of the docstring
|
||
|
||
"""
|
||
docstring_: str = str( docstring if isinstance(docstring,str)
|
||
else docstring.__doc__ if callable(docstring) and docstring.__doc__
|
||
else '').rstrip()+'\n'
|
||
sections = _OrderedDict(
|
||
Parameters=adopted_parameters,
|
||
Returns=adopted_return,
|
||
Examples=adopted_examples,
|
||
Notes=adopted_notes,
|
||
References=adopted_references)
|
||
|
||
for i, (key, adopted) in [(i,(k,v)) for (i,(k,v)) in enumerate(sections.items()) if v is not None]:
|
||
section_regex = fr'^([ ]*){key}\s*\n\1*{"-"*len(key)}\s*\n'
|
||
if key=='Returns':
|
||
if callable(adopted):
|
||
return_class = adopted.__annotations__.get('return','')
|
||
return_type_ = (_sys.modules[adopted.__module__].__name__.split('.')[0]
|
||
+'.'
|
||
+(return_class.__name__ if not isinstance(return_class,str) else return_class))
|
||
else:
|
||
return_type_ = adopted
|
||
docstring_ = _re.sub(fr'(^[ ]*{key}\s*\n\s*{"-"*len(key)}\s*\n[ ]*[A-Za-z0-9_ ]*: )(.*)\n',
|
||
fr'\1{return_type_}\n',
|
||
docstring_,flags=_re.MULTILINE)
|
||
else:
|
||
section_content_regex = fr'{section_regex}(?P<content>.*?)\n *(\n|\Z)'
|
||
adopted_: str = adopted.__doc__ if callable(adopted) else adopted #type: ignore
|
||
try:
|
||
if _re.search(fr'{section_regex}', adopted_, flags=_re.MULTILINE):
|
||
adopted_ = _re.search(section_content_regex, #type: ignore
|
||
adopted_,
|
||
flags=_re.MULTILINE|_re.DOTALL).group('content')
|
||
except AttributeError:
|
||
raise RuntimeError(f"Function docstring passed for docstring section '{key}' is invalid:\n{docstring}")
|
||
|
||
docstring_indent, adopted_indent = (min([len(line)-len(line.lstrip()) for line in section.split('\n') if line.strip()])
|
||
for section in [docstring_, adopted_])
|
||
shift = adopted_indent - docstring_indent
|
||
adopted_content = '\n'.join([(line[shift:] if shift > 0 else
|
||
f'{" "*-shift}{line}') for line in adopted_.split('\n') if line.strip()])
|
||
|
||
if _re.search(section_regex, docstring_, flags=_re.MULTILINE):
|
||
docstring_section_content = _re.search(section_content_regex, # type: ignore
|
||
docstring_,
|
||
flags=_re.MULTILINE|_re.DOTALL).group('content')
|
||
a_items, d_items = (_re.findall('^[ ]*([A-Za-z0-9_ ]*?)[ ]*:',content,flags=_re.MULTILINE)
|
||
for content in [adopted_content,docstring_section_content])
|
||
for item in a_items:
|
||
if item in d_items:
|
||
adopted_content = _re.sub(fr'^([ ]*){item}.*?(?:(\n)\1([A-Za-z0-9_])|([ ]*\Z))',
|
||
r'\1\3',
|
||
adopted_content,
|
||
flags=_re.MULTILINE|_re.DOTALL).rstrip(' \n')
|
||
docstring_ = _re.sub(fr'(^[ ]*{key}\s*\n\s*{"-"*len(key)}\s*\n.*?)\n *(\Z|\n)',
|
||
fr'\1\n{adopted_content}\n\2',
|
||
docstring_,
|
||
flags=_re.MULTILINE|_re.DOTALL)
|
||
else:
|
||
section_title = f'{" "*(shift+docstring_indent)}{key}\n{" "*(shift+docstring_indent)}{"-"*len(key)}\n'
|
||
section_matches = [_re.search(
|
||
fr'[ ]*{list(sections.keys())[index]}\s*\n\s*{"-"*len(list(sections.keys())[index])}\s*', docstring_)
|
||
for index in range(i,len(sections))]
|
||
subsequent_section = '\\Z' if not any(section_matches) else \
|
||
'\n'+next(item for item in section_matches if item is not None).group(0)
|
||
docstring_ = _re.sub(fr'({subsequent_section})',
|
||
fr'\n{section_title}{adopted_content}\n\1',
|
||
docstring_)
|
||
return docstring_
|
||
|
||
|
||
def extend_docstring(docstring: _Union[None, str, _Callable] = None,
|
||
**kwargs) -> _Callable:
|
||
"""
|
||
Decorator: Extend the function's docstring.
|
||
|
||
Parameters
|
||
----------
|
||
docstring : str or callable, optional
|
||
Docstring to extend. Defaults to that of decorated function.
|
||
adopted_* : str or callable, optional
|
||
Additional information to insert into/append to respective section.
|
||
|
||
Notes
|
||
-----
|
||
Return type will become own type if docstring is callable.
|
||
|
||
"""
|
||
def _decorator(func):
|
||
if 'adopted_return' not in kwargs: kwargs['adopted_return'] = func
|
||
func.__doc__ = _docstringer(func.__doc__ if docstring is None else docstring,
|
||
**kwargs)
|
||
return func
|
||
return _decorator
|
||
|
||
def pass_on(keyword: str,
|
||
target: _Callable,
|
||
wrapped: _Callable = None) -> _Callable: # type: ignore
|
||
"""
|
||
Decorator: Combine signatures of 'wrapped' and 'target' functions and pass on output of 'target' as 'keyword' argument.
|
||
|
||
Parameters
|
||
----------
|
||
keyword : str
|
||
Keyword added to **kwargs of the decorated function
|
||
passing on the result of 'target'.
|
||
target : callable
|
||
The output of this function is passed to the
|
||
decorated function as 'keyword' argument.
|
||
wrapped: callable, optional
|
||
Signature of 'wrapped' function combined with
|
||
that of 'target' yields the overall signature of decorated function.
|
||
|
||
Notes
|
||
-----
|
||
The keywords used by 'target' will be prioritized
|
||
if they overlap with those of the decorated function.
|
||
Functions 'target' and 'wrapped' are assumed to only have keyword arguments.
|
||
|
||
"""
|
||
|
||
def decorator(func):
|
||
@_wraps(func)
|
||
def wrapper(*args, **kwargs):
|
||
kw_wrapped = set(kwargs.keys()) - set(inspect.getfullargspec(target).args)
|
||
kwargs_wrapped = {kw: kwargs.pop(kw) for kw in kw_wrapped}
|
||
kwargs_wrapped[keyword] = target(**kwargs)
|
||
return func(*args, **kwargs_wrapped)
|
||
args_ = [] if wrapped is None or 'self' not in inspect.signature(wrapped).parameters \
|
||
else [inspect.signature(wrapped).parameters['self']]
|
||
for f in [target] if wrapped is None else [target,wrapped]:
|
||
for param in inspect.signature(f).parameters.values():
|
||
if param.name != keyword \
|
||
and param.name not in [p.name for p in args_]+['self','cls', 'args', 'kwargs']:
|
||
args_.append(param.replace(kind=inspect._ParameterKind.KEYWORD_ONLY))
|
||
wrapper.__signature__ = inspect.Signature(parameters=args_,return_annotation=inspect.signature(func).return_annotation)
|
||
return wrapper
|
||
return decorator
|
||
|
||
def DREAM3D_base_group(fname: _Union[str, _Path, _h5py.File]) -> str:
|
||
"""
|
||
Determine the base group of a DREAM.3D file.
|
||
|
||
The base group is defined as the group (folder) that contains
|
||
a 'SPACING' dataset in a '_SIMPL_GEOMETRY' group.
|
||
|
||
Parameters
|
||
----------
|
||
fname : str, pathlib.Path, or _h5py.File
|
||
Filename of the DREAM.3D (HDF5) file.
|
||
|
||
Returns
|
||
-------
|
||
path : str
|
||
Path to the base group.
|
||
|
||
"""
|
||
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
|
||
|
||
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.
|
||
|
||
The cell data group is defined as the group (folder) that contains
|
||
a dataset in the base group whose length matches the total number
|
||
of points as specified in '_SIMPL_GEOMETRY/DIMENSIONS'.
|
||
|
||
Parameters
|
||
----------
|
||
fname : str, pathlib.Path, or h5py.File
|
||
Filename of the DREAM.3D (HDF5) file.
|
||
|
||
Returns
|
||
-------
|
||
path : str
|
||
Path to the cell data group.
|
||
|
||
"""
|
||
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,
|
||
hkil: _Optional[_np.ndarray] = None) -> _np.ndarray:
|
||
"""
|
||
Transform 4 Miller–Bravais indices to 3 Miller indices of crystal direction [uvw] or plane normal (hkl).
|
||
|
||
Parameters
|
||
----------
|
||
uvtw|hkil : numpy.ndarray, shape (...,4)
|
||
Miller–Bravais indices of crystallographic direction [uvtw] or plane normal (hkil).
|
||
|
||
Returns
|
||
-------
|
||
uvw|hkl : numpy.ndarray, shape (...,3)
|
||
Miller indices of [uvw] direction or (hkl) plane normal.
|
||
|
||
"""
|
||
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]])) \
|
||
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)
|
||
|
||
def Miller_to_Bravais(*,
|
||
uvw: _Optional[_np.ndarray] = None,
|
||
hkl: _Optional[_np.ndarray] = None) -> _np.ndarray:
|
||
"""
|
||
Transform 3 Miller indices to 4 Miller–Bravais indices of crystal direction [uvtw] or plane normal (hkil).
|
||
|
||
Parameters
|
||
----------
|
||
uvw|hkl : numpy.ndarray, shape (...,3)
|
||
Miller indices of crystallographic direction [uvw] or plane normal (hkl).
|
||
|
||
Returns
|
||
-------
|
||
uvtw|hkil : numpy.ndarray, shape (...,4)
|
||
Miller–Bravais indices of [uvtw] direction or (hkil) plane normal.
|
||
|
||
"""
|
||
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) \
|
||
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)
|
||
|
||
|
||
def dict_prune(d: _Dict) -> _Dict:
|
||
"""
|
||
Recursively remove empty dictionaries.
|
||
|
||
Parameters
|
||
----------
|
||
d : dict
|
||
Dictionary to prune.
|
||
|
||
Returns
|
||
-------
|
||
pruned : dict
|
||
Pruned dictionary.
|
||
|
||
"""
|
||
# https://stackoverflow.com/questions/48151953
|
||
new = {}
|
||
for k,v in d.items():
|
||
if isinstance(v, dict):
|
||
v = dict_prune(v)
|
||
if not isinstance(v,dict) or v != {}:
|
||
new[k] = v
|
||
|
||
return new
|
||
|
||
def dict_flatten(d: _Dict) -> _Dict:
|
||
"""
|
||
Recursively remove keys of single-entry dictionaries.
|
||
|
||
Parameters
|
||
----------
|
||
d : dict
|
||
Dictionary to flatten.
|
||
|
||
Returns
|
||
-------
|
||
flattened : dict
|
||
Flattened dictionary.
|
||
|
||
"""
|
||
if isinstance(d,dict) and len(d) == 1:
|
||
entry = d[list(d.keys())[0]]
|
||
new = dict_flatten(entry.copy()) if isinstance(entry,dict) else entry
|
||
else:
|
||
new = {k: (dict_flatten(v) if isinstance(v, dict) else v) for k,v in d.items()}
|
||
|
||
return new
|
||
|
||
|
||
####################################################################################################
|
||
# Classes
|
||
####################################################################################################
|
||
class ProgressBar:
|
||
"""
|
||
Report progress of an interation as a status bar.
|
||
|
||
Works for 0-based loops, ETA is estimated by linear extrapolation.
|
||
"""
|
||
|
||
def __init__(self,
|
||
total: int,
|
||
prefix: str,
|
||
bar_length: int):
|
||
"""
|
||
New progress bar.
|
||
|
||
Parameters
|
||
----------
|
||
total : int
|
||
Total # of iterations.
|
||
prefix : str
|
||
Prefix string.
|
||
bar_length : int
|
||
Character length of bar.
|
||
|
||
"""
|
||
self.total = total
|
||
self.prefix = prefix
|
||
self.bar_length = bar_length
|
||
self.time_start = self.time_last_update = _datetime.datetime.now()
|
||
self.fraction_last = 0.0
|
||
|
||
if _sys.stdout.isatty():
|
||
_sys.stdout.write(f"{self.prefix} {'░'*self.bar_length} 0% ETA n/a")
|
||
|
||
def update(self,
|
||
iteration: int) -> None:
|
||
|
||
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()
|
||
bar = '█' * filled_length + '░' * (self.bar_length - filled_length)
|
||
remaining_time = (_datetime.datetime.now() - self.time_start) \
|
||
* (self.total - (iteration+1)) / (iteration+1)
|
||
remaining_time -= _datetime.timedelta(microseconds=remaining_time.microseconds) # remove μs
|
||
if _sys.stdout.isatty():
|
||
_sys.stdout.write(f'\r{self.prefix} {bar} {fraction:>4.0%} ETA {remaining_time}')
|
||
|
||
self.fraction_last = fraction
|
||
|
||
if iteration == self.total - 1 and _sys.stdout.isatty():
|
||
_sys.stdout.write('\n')
|