683 lines
20 KiB
Python
683 lines
20 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 fractions
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from functools import reduce
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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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# limit visibility
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__all__=[
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'srepr',
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'emph','deemph','warn','strikeout',
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'execute',
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'natural_sort',
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'show_progress',
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'scale_to_coprime',
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'project_stereographic',
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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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]
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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(arg,glue = '\n'):
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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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arg : iterable
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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(arg, 'strip') and
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(hasattr(arg, '__getitem__') or
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hasattr(arg, '__iter__'))):
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return glue.join(str(x) for x in arg)
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else:
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return arg if isinstance(arg,str) else repr(arg)
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def emph(what):
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"""
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Format with emphasis.
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Parameters
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----------
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what : 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['bold']+srepr(what)+_colors['end_color']
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def deemph(what):
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"""
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Format with deemphasis.
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Parameters
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----------
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what : 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['dim']+srepr(what)+_colors['end_color']
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def warn(what):
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"""
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Format for warning.
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Parameters
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----------
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what : 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['warning']+emph(what)+_colors['end_color']
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def strikeout(what):
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"""
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Format as strikeout.
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Parameters
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----------
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what : 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(what)+_colors['end_color']
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def execute(cmd,wd='./',env=None):
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"""
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Execute 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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Returns
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-------
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stdout, stderr : str
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Output of the executed command.
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"""
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print(f"executing '{cmd}' in '{wd}'")
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process = subprocess.run(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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if process.returncode != 0:
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print(process.stdout)
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print(process.stderr)
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raise RuntimeError(f"'{cmd}' failed with returncode {process.returncode}")
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return process.stdout, process.stderr
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def natural_sort(key):
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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,N_iter=None,prefix='',bar_length=50):
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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 or function with yield statement
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Iterable (or function with yield statement) to be decorated.
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N_iter : int, optional
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Total number of iterations. Required unless obtainable as len(iterable).
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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 N_iter in [0,1] or (hasattr(iterable,'__len__') and len(iterable) <= 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_iter if N_iter is not None else len(iterable),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):
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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 : numpy.ndarray of shape (:)
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Vector to scale.
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Returns
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-------
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m : numpy.ndarray of 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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m = (np.array(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}. Insufficient precision?')
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return m
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def project_stereographic(vector,direction='z',normalize=True,keepdims=False):
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"""
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Apply stereographic projection to vector.
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Parameters
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----------
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vector : numpy.ndarray of shape (...,3)
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Vector coordinates to be projected.
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direction : str
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Projection direction 'x', 'y', or 'z'.
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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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Default 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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Returns
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-------
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coordinates : numpy.ndarray of shape (...,2 | 3)
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Projected coordinates.
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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_stereographic(np.ones(3))
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[0.3660254, 0.3660254]
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>>> project_stereographic(np.ones(3),direction='x',normalize=False,keepdims=True)
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[0, 0.5, 0.5]
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>>> project_stereographic([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+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,function_name=None):
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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,N,rng_seed=None):
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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(int)
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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,to,mode='left',keep_ones=False):
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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 : str, 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 not len(fro) and not len(to): 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 not len(fro) else fro
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to = (1,) if not len(to) else to
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try:
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grp = 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]}',
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','.join(map(str,to))+',').groups()
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except AttributeError:
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raise ValueError(f'Shapes can not be shifted {fro} --> {to}')
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fill = ()
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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,b):
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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))
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(1,2,2,1)
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>>> shapeblender((3,2),(3,2))
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(3,2)
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"""
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i = min(len(a),len(b))
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while i > 0 and a[-i:] != b[:i]: i -= 1
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return a + b[i:]
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def extend_docstring(extra_docstring):
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"""
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Decorator: Append to function's docstring.
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Parameters
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----------
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extra_docstring : str
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Docstring to append.
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"""
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def _decorator(func):
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func.__doc__ += extra_docstring
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return func
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return _decorator
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def extended_docstring(f,extra_docstring):
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"""
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Decorator: Combine another function's docstring with a given docstring.
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Parameters
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----------
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f : function
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Function of which the docstring is taken.
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extra_docstring : str
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Docstring to append.
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"""
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def _decorator(func):
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func.__doc__ = f.__doc__ + extra_docstring
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return func
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return _decorator
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def DREAM3D_base_group(fname):
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"""
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Determine the base group of a DREAM.3D file.
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The base group is defined as the group (folder) that contains
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a 'SPACING' dataset in a '_SIMPL_GEOMETRY' group.
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Parameters
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----------
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fname : str or pathlib.Path
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Filename of the DREAM.3D (HDF5) file.
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Returns
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-------
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path : str
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Path to the base group.
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"""
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with h5py.File(fname,'r') as f:
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base_group = f.visit(lambda path: path.rsplit('/',2)[0] if '_SIMPL_GEOMETRY/SPACING' in path else None)
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if base_group is None:
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raise ValueError(f'Could not determine base group in file {fname}.')
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return base_group
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def DREAM3D_cell_data_group(fname):
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"""
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Determine the cell data group of a DREAM.3D file.
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The cell data group is defined as the group (folder) that contains
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a dataset in the base group whose length matches the total number
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of points as specified in '_SIMPL_GEOMETRY/DIMENSIONS'.
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Parameters
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----------
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fname : str or pathlib.Path
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Filename of the DREAM.3D (HDF5) file.
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Returns
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-------
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path : str
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Path to the cell data group.
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"""
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base_group = DREAM3D_base_group(fname)
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with h5py.File(fname,'r') as f:
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cells = tuple(f['/'.join([base_group,'_SIMPL_GEOMETRY','DIMENSIONS'])][()][::-1])
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cell_data_group = f[base_group].visititems(lambda path,obj: path.split('/')[0] \
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if isinstance(obj,h5py._hl.dataset.Dataset) and np.shape(obj)[:-1] == cells \
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else None)
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if cell_data_group is None:
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raise ValueError(f'Could not determine cell data group in file {fname}/{base_group}.')
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return cell_data_group
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def Bravais_to_Miller(*,uvtw=None,hkil=None):
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"""
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Transform 4 Miller–Bravais indices to 3 Miller indices of crystal direction [uvw] or plane normal (hkl).
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Parameters
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----------
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uvtw|hkil : numpy.ndarray of shape (...,4)
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Miller–Bravais indices of crystallographic direction [uvtw] or plane normal (hkil).
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Returns
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-------
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uvw|hkl : numpy.ndarray of shape (...,3)
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Miller indices of [uvw] direction or (hkl) plane normal.
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"""
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if (uvtw is not None) ^ (hkil is None):
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raise KeyError('Specify either "uvtw" or "hkil"')
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axis,basis = (np.array(uvtw),np.array([[1,0,-1,0],
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[0,1,-1,0],
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[0,0, 0,1]])) \
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if hkil is None else \
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(np.array(hkil),np.array([[1,0,0,0],
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[0,1,0,0],
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[0,0,0,1]]))
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return np.einsum('il,...l',basis,axis)
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def Miller_to_Bravais(*,uvw=None,hkl=None):
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"""
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Transform 3 Miller indices to 4 Miller–Bravais indices of crystal direction [uvtw] or plane normal (hkil).
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Parameters
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----------
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uvw|hkl : numpy.ndarray of shape (...,3)
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Miller indices of crystallographic direction [uvw] or plane normal (hkl).
|
||
|
||
Returns
|
||
-------
|
||
uvtw|hkil : numpy.ndarray of 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):
|
||
"""
|
||
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):
|
||
"""
|
||
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,prefix,bar_length):
|
||
"""
|
||
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):
|
||
|
||
fraction = (iteration+1) / self.total
|
||
filled_length = int(self.bar_length * fraction)
|
||
|
||
if filled_length > 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()
|