"""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
from collections import abc as _abc
from functools import reduce as _reduce, partial as _partial
from typing import Callable as _Callable, Union as _Union, Iterable as _Iterable, Sequence as _Sequence, Dict as _Dict, \
List as _List, Tuple as _Tuple, Literal as _Literal, Any as _Any, Collection as _Collection, TextIO as _TextIO
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, IntCollection as _IntCollection, \
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') -> str:
r"""
Join items with glue string.
Parameters
----------
msg : object with __repr__ or sequence of objects with __repr__
Items to join.
glue : str, optional
Glue used for joining operation. Defaults to '\n'.
Returns
-------
joined : str
String representation of the joined items.
"""
if (not hasattr(msg, 'strip') and
(hasattr(msg, '__getitem__') or
hasattr(msg, '__iter__'))):
return glue.join(str(x) for x in msg)
else:
return msg if isinstance(msg,str) else repr(msg)
def emph(msg) -> str:
"""
Format with emphasis.
Parameters
----------
msg : object with __repr__ or sequence of objects 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 : object with __repr__ or sequence of objects 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 : object with __repr__ or sequence of objects 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 : object with __repr__ or iterable of objects 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: _Dict[str, str] = None,
timeout: 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
def open_text(fname: _FileHandle,
mode: _Literal['r','w'] = 'r') -> _TextIO: # noqa
"""
Open a text file.
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
"""
return fname if not isinstance(fname, (str,_Path)) else \
open(_Path(fname).expanduser(),mode,newline=('\n' if mode == 'w' else None))
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: 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.
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) -> _np.ndarray:
"""
Scale vector to co-prime (relatively prime) integers.
Parameters
----------
v : sequence of float, len (:)
Vector to scale.
Returns
-------
m : numpy.ndarray, shape (:)
Vector scaled to co-prime numbers.
"""
MAX_DENOMINATOR = 1000000
def get_square_denominator(x):
"""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.lcm(a,b) # numpy > 1.18
except AttributeError:
return a * b // _np.gcd(a, b)
v_ = _np.array(v)
m = (v_ * _reduce(lcm, map(lambda x: int(get_square_denominator(x)),v_))**0.5).astype(_np.int64)
m = m//_reduce(_np.gcd,m)
with _np.errstate(invalid='ignore'):
if not _np.allclose(_np.ma.masked_invalid(v_/m),v_[_np.argmax(abs(v_))]/m[_np.argmax(abs(v_))]):
raise ValueError(f'invalid result "{m}" for input "{v_}"')
return m
def project_equal_angle(vector: _np.ndarray,
direction: _Literal['x', 'y', 'z'] = 'z', # 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 execution_stamp(class_name: str,
function_name: str = None) -> str:
"""Timestamp the execution of a (function within a) class."""
now = _datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S%z')
_function_name = '' if function_name is None else f'.{function_name}'
return f'damask.{class_name}{_function_name} v{_version} ({now})'
def hybrid_IA(dist: _np.ndarray,
N: int,
rng_seed: _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.
"""
N_opt_samples,N_inv_samples = (max(_np.count_nonzero(dist),N),0) # random subsampling if too little samples requested
scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0)
while (not _np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
repeats = _np.rint(scale*dist).astype(_np.int64)
N_inv_samples = _np.sum(repeats)
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, ...]) -> _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.
Examples
--------
>>> shapeblender((4,4,3),(3,2,1))
(4,4,3,2,1)
>>> shapeblender((1,2),(1,2,3))
(1,2,3)
>>> 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()