863 lines
25 KiB
Python
863 lines
25 KiB
Python
"""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 numpy as np
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import 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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# 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') -> str:
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r"""
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Join items with glue string.
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Parameters
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----------
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msg : object with __repr__ or sequence of objects 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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Returns
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-------
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joined : str
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String representation of the joined items.
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"""
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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(str(x) for x in msg)
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else:
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return msg if isinstance(msg,str) else repr(msg)
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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 : object with __repr__ or sequence of objects 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 : object with __repr__ or sequence of objects 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 : object with __repr__ or sequence of objects 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 : object with __repr__ or iterable of objects 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: 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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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:
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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 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: 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(int)
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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',
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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',
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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 execution_stamp(class_name: str,
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function_name: str = None) -> str:
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"""Timestamp the execution of a (function within a) class."""
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now = datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S%z')
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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} ({now})'
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def hybrid_IA(dist: np.ndarray,
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N: int,
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rng_seed: 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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"""
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N_opt_samples,N_inv_samples = (max(np.count_nonzero(dist),N),0) # random subsampling if too little samples requested
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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*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',
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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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Example
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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 ()
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beg = dict(left ='(^.*\\b)',
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right='(^.*?\\b)')
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sep = dict(left ='(.*\\b)',
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right='(.*?\\b)')
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end = dict(left ='(.*?$)',
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right='(.*$)')
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fro = (1,) if len(fro) == 0 else fro
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to = (1,) if len(to) == 0 else to
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try:
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match = re.match(beg[mode]
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+f',{sep[mode]}'.join(map(lambda x: f'{x}'
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if x>1 or (keep_ones and len(fro)>1) else
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'\\d+',fro))
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+f',{end[mode]}',','.join(map(str,to))+',')
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assert match
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grp = match.groups()
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except AssertionError:
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raise ValueError(f'shapes cannot be shifted {fro} --> {to}')
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fill: Any = ()
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for g,d in zip(grp,fro+(None,)):
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fill += (1,)*g.count(',')+(d,)
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return fill[:-1]
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def shapeblender(a: Tuple[int, ...],
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b: Tuple[int, ...]) -> 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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Examples
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--------
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>>> shapeblender((4,4,3),(3,2,1))
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(4,4,3,2,1)
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>>> shapeblender((1,2),(1,2,3))
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(1,2,3)
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>>> shapeblender((1,),(2,2,1))
|
||
(1,2,2,1)
|
||
>>> 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 extend_docstring(extra_docstring: str) -> Callable:
|
||
"""
|
||
Decorator: Append to function's docstring.
|
||
|
||
Parameters
|
||
----------
|
||
extra_docstring : str
|
||
Docstring to append.
|
||
|
||
"""
|
||
def _decorator(func):
|
||
func.__doc__ += extra_docstring
|
||
return func
|
||
return _decorator
|
||
|
||
|
||
def extended_docstring(f: Callable,
|
||
extra_docstring: str) -> Callable:
|
||
"""
|
||
Decorator: Combine another function's docstring with a given docstring.
|
||
|
||
Parameters
|
||
----------
|
||
f : function
|
||
Function of which the docstring is taken.
|
||
extra_docstring : str
|
||
Docstring to append.
|
||
|
||
"""
|
||
def _decorator(func):
|
||
func.__doc__ = f.__doc__ + extra_docstring
|
||
return func
|
||
return _decorator
|
||
|
||
|
||
def DREAM3D_base_group(fname: Union[str, Path]) -> 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 or pathlib.Path
|
||
Filename of the DREAM.3D (HDF5) file.
|
||
|
||
Returns
|
||
-------
|
||
path : str
|
||
Path to the base group.
|
||
|
||
"""
|
||
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:
|
||
raise ValueError(f'could not determine base group in file "{fname}"')
|
||
|
||
return base_group
|
||
|
||
def DREAM3D_cell_data_group(fname: Union[str, Path]) -> 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 or pathlib.Path
|
||
Filename of the DREAM.3D (HDF5) file.
|
||
|
||
Returns
|
||
-------
|
||
path : str
|
||
Path to the cell data group.
|
||
|
||
"""
|
||
base_group = DREAM3D_base_group(fname)
|
||
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 \
|
||
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
|
||
|
||
|
||
def Bravais_to_Miller(*,
|
||
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).
|
||
|
||
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: 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).
|
||
|
||
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
|
||
|
||
|
||
def tail_repack(extended: Union[str, Sequence[str]],
|
||
existing: List[str] = []) -> List[str]:
|
||
"""
|
||
Repack tailing characters into single string if all are new.
|
||
|
||
Parameters
|
||
----------
|
||
extended : str or list of str
|
||
Extended string list with potentially autosplitted tailing string relative to `existing`.
|
||
existing : list of str
|
||
Base string list.
|
||
|
||
Returns
|
||
-------
|
||
repacked : list of str
|
||
Repacked version of `extended`.
|
||
|
||
Examples
|
||
--------
|
||
>>> tail_repack(['a','new','e','n','t','r','y'],['a','new'])
|
||
['a','new','entry']
|
||
>>> tail_repack(['a','new','shiny','e','n','t','r','y'],['a','new'])
|
||
['a','new','shiny','e','n','t','r','y']
|
||
|
||
"""
|
||
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
|
||
list(extended[len(existing):]))
|
||
|
||
|
||
def aslist(arg: Union[IntCollection,int,None]) -> List:
|
||
"""
|
||
Transform argument to list.
|
||
|
||
Parameters
|
||
----------
|
||
arg : int or collection of int or None
|
||
Entity to transform into list.
|
||
|
||
Returns
|
||
-------
|
||
transformed : list
|
||
Entity transformed into list.
|
||
|
||
"""
|
||
return [] if arg is None else list(arg) if isinstance(arg,(np.ndarray,Collection)) else [arg]
|
||
|
||
|
||
####################################################################################################
|
||
# 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):
|
||
"""
|
||
Set current time as basis for ETA estimation.
|
||
|
||
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
|
||
|
||
sys.stderr.write(f"{self.prefix} {'░'*self.bar_length} 0% ETA n/a")
|
||
sys.stderr.flush()
|
||
|
||
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
|
||
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()
|