968 lines
33 KiB
Python
968 lines
33 KiB
Python
"""Miscellaneous helper functionality."""
|
||
|
||
import sys as _sys
|
||
import datetime as _datetime
|
||
import os as _os
|
||
import subprocess as _subprocess
|
||
import shlex as _shlex
|
||
import re as _re
|
||
import signal as _signal
|
||
import fractions as _fractions
|
||
import contextlib as _contextlib
|
||
from collections import abc as _abc, OrderedDict as _OrderedDict
|
||
from functools import reduce as _reduce, partial as _partial, wraps as _wraps
|
||
import inspect
|
||
from typing import Optional as _Optional, Callable as _Callable, Union as _Union, Iterable as _Iterable, \
|
||
Dict as _Dict, List as _List, Tuple as _Tuple, Literal as _Literal, \
|
||
Any as _Any, TextIO as _TextIO, Generator as _Generator
|
||
from pathlib import Path as _Path
|
||
|
||
import numpy as _np
|
||
import h5py as _h5py
|
||
|
||
from . import version as _version
|
||
from ._typehints import FloatSequence as _FloatSequence, NumpyRngSeed as _NumpyRngSeed, FileHandle as _FileHandle
|
||
|
||
# https://svn.blender.org/svnroot/bf-blender/trunk/blender/build_files/scons/tools/bcolors.py
|
||
# https://stackoverflow.com/questions/287871
|
||
_colors = {
|
||
'header' : '\033[95m',
|
||
'OK_blue': '\033[94m',
|
||
'OK_green': '\033[92m',
|
||
'warning': '\033[93m',
|
||
'fail': '\033[91m',
|
||
'end_color': '\033[0m',
|
||
'bold': '\033[1m',
|
||
'dim': '\033[2m',
|
||
'underline': '\033[4m',
|
||
'crossout': '\033[9m'
|
||
}
|
||
|
||
####################################################################################################
|
||
# Functions
|
||
####################################################################################################
|
||
def srepr(msg,
|
||
glue: str = '\n',
|
||
quote: bool = False) -> str:
|
||
r"""
|
||
Join (quoted) items with glue string.
|
||
|
||
Parameters
|
||
----------
|
||
msg : (sequence of) object with __repr__
|
||
Items to join.
|
||
glue : str, optional
|
||
Glue used for joining operation. Defaults to '\n'.
|
||
quote : bool, optional
|
||
Quote items. Defaults to False.
|
||
|
||
Returns
|
||
-------
|
||
joined : str
|
||
String representation of the joined and quoted items.
|
||
|
||
"""
|
||
q = '"' if quote else ''
|
||
if (not hasattr(msg, 'strip') and
|
||
(hasattr(msg, '__getitem__') or
|
||
hasattr(msg, '__iter__'))):
|
||
return glue.join(q+str(x)+q for x in msg)
|
||
else:
|
||
return q+(msg if isinstance(msg,str) else repr(msg))+q
|
||
|
||
|
||
def emph(msg) -> str:
|
||
"""
|
||
Format with emphasis.
|
||
|
||
Parameters
|
||
----------
|
||
msg : (sequence of) object with __repr__
|
||
Message to format.
|
||
|
||
Returns
|
||
-------
|
||
formatted : str
|
||
Formatted string representation of the joined items.
|
||
|
||
"""
|
||
return _colors['bold']+srepr(msg)+_colors['end_color']
|
||
|
||
def deemph(msg) -> str:
|
||
"""
|
||
Format with deemphasis.
|
||
|
||
Parameters
|
||
----------
|
||
msg : (sequence of) object with __repr__
|
||
Message to format.
|
||
|
||
Returns
|
||
-------
|
||
formatted : str
|
||
Formatted string representation of the joined items.
|
||
|
||
"""
|
||
return _colors['dim']+srepr(msg)+_colors['end_color']
|
||
|
||
def warn(msg) -> str:
|
||
"""
|
||
Format for warning.
|
||
|
||
Parameters
|
||
----------
|
||
msg : (sequence of) object with __repr__
|
||
Message to format.
|
||
|
||
Returns
|
||
-------
|
||
formatted : str
|
||
Formatted string representation of the joined items.
|
||
|
||
"""
|
||
return _colors['warning']+emph(msg)+_colors['end_color']
|
||
|
||
def strikeout(msg) -> str:
|
||
"""
|
||
Format as strikeout.
|
||
|
||
Parameters
|
||
----------
|
||
msg : (iterable of) object with __repr__
|
||
Message to format.
|
||
|
||
Returns
|
||
-------
|
||
formatted : str
|
||
Formatted string representation of the joined items.
|
||
|
||
"""
|
||
return _colors['crossout']+srepr(msg)+_colors['end_color']
|
||
|
||
|
||
def run(cmd: str,
|
||
wd: str = './',
|
||
env: _Optional[_Dict[str, str]] = None,
|
||
timeout: _Optional[int] = None) -> _Tuple[str, str]:
|
||
"""
|
||
Run a command.
|
||
|
||
Parameters
|
||
----------
|
||
cmd : str
|
||
Command to be executed.
|
||
wd : str, optional
|
||
Working directory of process. Defaults to './'.
|
||
env : dict, optional
|
||
Environment for execution.
|
||
timeout : integer, optional
|
||
Timeout in seconds.
|
||
|
||
Returns
|
||
-------
|
||
stdout, stderr : (str, str)
|
||
Output of the executed command.
|
||
|
||
"""
|
||
def pass_signal(sig,_,proc,default):
|
||
proc.send_signal(sig)
|
||
_signal.signal(sig,default)
|
||
_signal.raise_signal(sig)
|
||
|
||
signals = [_signal.SIGINT,_signal.SIGTERM]
|
||
|
||
print(f"running '{cmd}' in '{wd}'")
|
||
process = _subprocess.Popen(_shlex.split(cmd),
|
||
stdout = _subprocess.PIPE,
|
||
stderr = _subprocess.PIPE,
|
||
env = _os.environ if env is None else env,
|
||
cwd = wd,
|
||
encoding = 'utf-8')
|
||
# ensure that process is terminated (https://stackoverflow.com/questions/22916783)
|
||
sig_states = [_signal.signal(sig,_partial(pass_signal,proc=process,default=_signal.getsignal(sig))) for sig in signals]
|
||
|
||
try:
|
||
stdout,stderr = process.communicate(timeout=timeout)
|
||
finally:
|
||
for sig,state in zip(signals,sig_states):
|
||
_signal.signal(sig,state)
|
||
|
||
if process.returncode != 0:
|
||
print(stdout)
|
||
print(stderr)
|
||
raise RuntimeError(f"'{cmd}' failed with returncode {process.returncode}")
|
||
|
||
return stdout, stderr
|
||
|
||
@_contextlib.contextmanager
|
||
def open_text(fname: _FileHandle,
|
||
mode: _Literal['r','w'] = 'r') -> _Generator[_TextIO, None, None]: # noqa
|
||
"""
|
||
Open a text file with Unix line endings
|
||
|
||
If a path or string is given, a context manager ensures that
|
||
the file handle is closed.
|
||
If a file handle is given, it remains unmodified.
|
||
|
||
Parameters
|
||
----------
|
||
fname : file, str, or pathlib.Path
|
||
Name or handle of file.
|
||
mode: {'r','w'}, optional
|
||
Access mode: 'r'ead or 'w'rite, defaults to 'r'.
|
||
|
||
Returns
|
||
-------
|
||
f : file handle
|
||
|
||
"""
|
||
if isinstance(fname, (str,_Path)):
|
||
fhandle = open(_Path(fname).expanduser(),mode,newline=('\n' if mode == 'w' else None))
|
||
yield fhandle
|
||
fhandle.close()
|
||
else:
|
||
yield fname
|
||
|
||
def time_stamp() -> str:
|
||
"""Provide current time as formatted string."""
|
||
return _datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S%z')
|
||
|
||
def execution_stamp(class_name: str,
|
||
function_name: _Optional[str] = None) -> str:
|
||
"""Timestamp the execution of a (function within a) class."""
|
||
_function_name = '' if function_name is None else f'.{function_name}'
|
||
return f'damask.{class_name}{_function_name} v{_version} ({time_stamp()})'
|
||
|
||
|
||
def natural_sort(key: str) -> _List[_Union[int, str]]:
|
||
"""
|
||
Natural sort.
|
||
|
||
For use in python's 'sorted'.
|
||
|
||
References
|
||
----------
|
||
https://en.wikipedia.org/wiki/Natural_sort_order
|
||
|
||
"""
|
||
convert = lambda text: int(text) if text.isdigit() else text
|
||
return [ convert(c) for c in _re.split('([0-9]+)', key) ]
|
||
|
||
|
||
def show_progress(iterable: _Iterable,
|
||
N_iter: _Optional[int] = None,
|
||
prefix: str = '',
|
||
bar_length: int = 50) -> _Any:
|
||
"""
|
||
Decorate a loop with a progress bar.
|
||
|
||
Use similar like enumerate.
|
||
|
||
Parameters
|
||
----------
|
||
iterable : iterable
|
||
Iterable to be decorated.
|
||
N_iter : int, optional
|
||
Total number of iterations. Required if iterable is not a sequence.
|
||
prefix : str, optional
|
||
Prefix string. Defaults to ''.
|
||
bar_length : int, optional
|
||
Length of progress bar in characters. Defaults to 50.
|
||
|
||
"""
|
||
if isinstance(iterable,_abc.Sequence):
|
||
if N_iter is None:
|
||
N = len(iterable)
|
||
else:
|
||
raise ValueError('N_iter given for sequence')
|
||
else:
|
||
if N_iter is None:
|
||
raise ValueError('N_iter not given')
|
||
|
||
N = N_iter
|
||
|
||
if N <= 1:
|
||
for item in iterable:
|
||
yield item
|
||
else:
|
||
status = ProgressBar(N,prefix,bar_length)
|
||
for i,item in enumerate(iterable):
|
||
yield item
|
||
status.update(i)
|
||
|
||
|
||
def scale_to_coprime(v: _FloatSequence,
|
||
N_significant: int = 9) -> _np.ndarray:
|
||
"""
|
||
Scale vector to co-prime (relatively prime) integers.
|
||
|
||
Parameters
|
||
----------
|
||
v : sequence of float, len (:)
|
||
Vector to scale.
|
||
N_significant: int, optional
|
||
Number of significant digits to consider. Defaults to 9.
|
||
|
||
Returns
|
||
-------
|
||
m : numpy.ndarray, shape (:)
|
||
Vector scaled to co-prime numbers.
|
||
|
||
"""
|
||
|
||
def get_square_denominator(x,max_denominator):
|
||
"""Denominator of the square of a number."""
|
||
return _fractions.Fraction(x ** 2).limit_denominator(max_denominator).denominator
|
||
|
||
def lcm(a,b):
|
||
"""Least common multiple."""
|
||
try:
|
||
return _np.abs(_np.lcm(a,b)) # numpy > 1.18
|
||
except AttributeError:
|
||
return _np.abs(a * b // _np.gcd(a, b))
|
||
|
||
v_ = _np.round(_np.array(v,'float64')/_np.max(_np.abs(v)),N_significant)
|
||
max_denominator = int(10**(N_significant-1))
|
||
m = (v_ * _reduce(lcm, map(lambda x: int(get_square_denominator(x,max_denominator)),v_))**0.5).astype(_np.int64)
|
||
m = m//_reduce(_np.gcd,m)
|
||
|
||
if not _np.allclose(m/_np.max(_np.abs(m)),v/_np.max(_np.abs(v)),atol=1e-2,rtol=0):
|
||
raise ValueError(f'invalid result "{m}" for input "{v}"')
|
||
|
||
return m
|
||
|
||
|
||
def project_equal_angle(vector: _np.ndarray,
|
||
direction: _Literal['x', 'y', 'z'] = 'z', # noqa
|
||
normalize: bool = True,
|
||
keepdims: bool = False) -> _np.ndarray:
|
||
"""
|
||
Apply equal-angle projection to vector.
|
||
|
||
Parameters
|
||
----------
|
||
vector : numpy.ndarray, shape (...,3)
|
||
Vector coordinates to be projected.
|
||
direction : {'x', 'y', 'z'}
|
||
Projection direction. Defaults to 'z'.
|
||
normalize : bool
|
||
Ensure unit length of input vector. Defaults to True.
|
||
keepdims : bool
|
||
Maintain three-dimensional output coordinates.
|
||
Defaults to False.
|
||
|
||
Returns
|
||
-------
|
||
coordinates : numpy.ndarray, shape (...,2 | 3)
|
||
Projected coordinates.
|
||
|
||
Notes
|
||
-----
|
||
Two-dimensional output uses right-handed frame spanned by
|
||
the next and next-next axis relative to the projection direction,
|
||
e.g. x-y when projecting along z and z-x when projecting along y.
|
||
|
||
Examples
|
||
--------
|
||
>>> import damask
|
||
>>> import numpy as np
|
||
>>> project_equal_angle(np.ones(3))
|
||
[0.3660254, 0.3660254]
|
||
>>> project_equal_angle(np.ones(3),direction='x',normalize=False,keepdims=True)
|
||
[0, 0.5, 0.5]
|
||
>>> project_equal_angle([0,1,1],direction='y',normalize=True,keepdims=False)
|
||
[0.41421356, 0]
|
||
|
||
"""
|
||
shift = 'zyx'.index(direction)
|
||
v = _np.roll(vector/_np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||
shift,axis=-1)
|
||
return _np.roll(_np.block([v[...,:2]/(1.0+_np.abs(v[...,2:3])),_np.zeros_like(v[...,2:3])]),
|
||
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
|
||
|
||
def project_equal_area(vector: _np.ndarray,
|
||
direction: _Literal['x', 'y', 'z'] = 'z', # noqa
|
||
normalize: bool = True,
|
||
keepdims: bool = False) -> _np.ndarray:
|
||
"""
|
||
Apply equal-area projection to vector.
|
||
|
||
Parameters
|
||
----------
|
||
vector : numpy.ndarray, shape (...,3)
|
||
Vector coordinates to be projected.
|
||
direction : {'x', 'y', 'z'}
|
||
Projection direction. Defaults to 'z'.
|
||
normalize : bool
|
||
Ensure unit length of input vector. Defaults to True.
|
||
keepdims : bool
|
||
Maintain three-dimensional output coordinates.
|
||
Defaults to False.
|
||
|
||
Returns
|
||
-------
|
||
coordinates : numpy.ndarray, shape (...,2 | 3)
|
||
Projected coordinates.
|
||
|
||
Notes
|
||
-----
|
||
Two-dimensional output uses right-handed frame spanned by
|
||
the next and next-next axis relative to the projection direction,
|
||
e.g. x-y when projecting along z and z-x when projecting along y.
|
||
|
||
|
||
Examples
|
||
--------
|
||
>>> import damask
|
||
>>> import numpy as np
|
||
>>> project_equal_area(np.ones(3))
|
||
[0.45970084, 0.45970084]
|
||
>>> project_equal_area(np.ones(3),direction='x',normalize=False,keepdims=True)
|
||
[0.0, 0.70710678, 0.70710678]
|
||
>>> project_equal_area([0,1,1],direction='y',normalize=True,keepdims=False)
|
||
[0.5411961, 0.0]
|
||
|
||
"""
|
||
shift = 'zyx'.index(direction)
|
||
v = _np.roll(vector/_np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||
shift,axis=-1)
|
||
return _np.roll(_np.block([v[...,:2]/_np.sqrt(1.0+_np.abs(v[...,2:3])),_np.zeros_like(v[...,2:3])]),
|
||
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
|
||
|
||
|
||
def hybrid_IA(dist: _FloatSequence,
|
||
N: int,
|
||
rng_seed: _Optional[_NumpyRngSeed] = None) -> _np.ndarray:
|
||
"""
|
||
Hybrid integer approximation.
|
||
|
||
Parameters
|
||
----------
|
||
dist : numpy.ndarray
|
||
Distribution to be approximated.
|
||
N : int
|
||
Number of samples to draw.
|
||
rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
|
||
A seed to initialize the BitGenerator. Defaults to None.
|
||
If None, then fresh, unpredictable entropy will be pulled from the OS.
|
||
|
||
Returns
|
||
-------
|
||
hist : numpy.ndarray, shape (N)
|
||
Integer approximation of the distribution.
|
||
|
||
"""
|
||
N_opt_samples = max(_np.count_nonzero(dist),N) # random subsampling if too little samples requested
|
||
N_inv_samples = 0
|
||
|
||
scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0)
|
||
while (not _np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
|
||
repeats = _np.rint(scale*_np.array(dist)).astype(_np.int64)
|
||
N_inv_samples = _np.sum(repeats)
|
||
scale_,scale,inc_factor = (scale,scale+inc_factor*0.5*(scale - scale_), inc_factor*2.0) \
|
||
if N_inv_samples < N_opt_samples else \
|
||
(scale_,0.5*(scale_ + scale), 1.0)
|
||
|
||
return _np.repeat(_np.arange(len(dist)),repeats)[_np.random.default_rng(rng_seed).permutation(N_inv_samples)[:N]]
|
||
|
||
|
||
def shapeshifter(fro: _Tuple[int, ...],
|
||
to: _Tuple[int, ...],
|
||
mode: _Literal['left','right'] = 'left', # noqa
|
||
keep_ones: bool = False) -> _Tuple[int, ...]:
|
||
"""
|
||
Return dimensions that reshape 'fro' to become broadcastable to 'to'.
|
||
|
||
Parameters
|
||
----------
|
||
fro : tuple
|
||
Original shape of array.
|
||
to : tuple
|
||
Target shape of array after broadcasting.
|
||
len(to) cannot be less than len(fro).
|
||
mode : {'left', 'right'}, optional
|
||
Indicates whether new axes are preferably added to
|
||
either left or right of the original shape.
|
||
Defaults to 'left'.
|
||
keep_ones : bool, optional
|
||
Treat '1' in fro as literal value instead of dimensional placeholder.
|
||
Defaults to False.
|
||
|
||
Returns
|
||
-------
|
||
new_dims : tuple
|
||
Dimensions for reshape.
|
||
|
||
Examples
|
||
--------
|
||
>>> import numpy as np
|
||
>>> from damask import util
|
||
>>> a = np.ones((3,4,2))
|
||
>>> b = np.ones(4)
|
||
>>> b_extended = b.reshape(util.shapeshifter(b.shape,a.shape))
|
||
>>> (a * np.broadcast_to(b_extended,a.shape)).shape
|
||
(3,4,2)
|
||
|
||
"""
|
||
if len(fro) == 0 and len(to) == 0: return tuple()
|
||
_fro = [1] if len(fro) == 0 else list(fro)[::-1 if mode=='left' else 1]
|
||
_to = [1] if len(to) == 0 else list(to) [::-1 if mode=='left' else 1]
|
||
|
||
final_shape: _List[int] = []
|
||
index = 0
|
||
for i,item in enumerate(_to):
|
||
if item == _fro[index]:
|
||
final_shape.append(item)
|
||
index+=1
|
||
else:
|
||
final_shape.append(1)
|
||
if _fro[index] == 1 and not keep_ones:
|
||
index+=1
|
||
if index == len(_fro):
|
||
final_shape = final_shape+[1]*(len(_to)-i-1)
|
||
break
|
||
if index != len(_fro): raise ValueError(f'shapes cannot be shifted {fro} --> {to}')
|
||
return tuple(final_shape[::-1] if mode == 'left' else final_shape)
|
||
|
||
def shapeblender(a: _Tuple[int, ...],
|
||
b: _Tuple[int, ...],
|
||
keep_ones: bool = False) -> _Tuple[int, ...]:
|
||
"""
|
||
Return a shape that overlaps the rightmost entries of 'a' with the leftmost of 'b'.
|
||
|
||
Parameters
|
||
----------
|
||
a : tuple
|
||
Shape of first array.
|
||
b : tuple
|
||
Shape of second array.
|
||
keep_ones : bool, optional
|
||
Treat innermost '1's as literal value instead of dimensional placeholder.
|
||
Defaults to False.
|
||
|
||
Examples
|
||
--------
|
||
>>> shapeblender((3,2),(3,2))
|
||
(3,2)
|
||
>>> shapeblender((4,3),(3,2))
|
||
(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')
|