"""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
from functools import reduce as _reduce, partial as _partial
from pathlib import Path as _Path
import logging
from typing import Optional as _Optional, Union as _Union, Iterable as _Iterable, \
Literal as _Literal, NamedTuple as _NamedTuple, \
Any as _Any, TextIO as _TextIO, Generator as _Generator
import numpy as _np
import h5py as _h5py
from . import version as _version
from ._typehints import FloatSequence as _FloatSequence, IntSequence as _IntSequence, \
NumpyRngSeed as _NumpyRngSeed, FileHandle as _FileHandle
[docs]
class stdioTuple(_NamedTuple):
stdout: str
stderr: str
logger = logging.getLogger(__name__)
# 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
####################################################################################################
[docs]
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
[docs]
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']
[docs]
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']
[docs]
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']
[docs]
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']
[docs]
def run(cmd: str,
wd: str = './',
env: _Optional[dict[str, str]] = None,
timeout: _Optional[int] = None) -> stdioTuple:
"""
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 : int, 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]
logger.info(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:
logger.error(stdout)
logger.error(stderr)
raise RuntimeError(f"'{cmd}' failed with returncode {process.returncode}")
return stdioTuple(stdout, stderr)
[docs]
@_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
File handle for a text file.
"""
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
[docs]
def time_stamp() -> str:
"""
Provide current time as formatted string.
Returns
-------
time_stamp : str
Current time as string in %Y-%m-%d %H:%M:%S%z format.
"""
return _datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S%z')
[docs]
def execution_stamp(class_name: str,
function_name: _Optional[str] = None) -> str:
"""
Timestamp the execution of a (function within a) class.
execution_stamp : str
Fingerprint of an operation: Class, (function), version, and
current time.
"""
_function_name = '' if function_name is None else f'.{function_name}'
return f'damask.{class_name}{_function_name} v{_version} ({time_stamp()})'
[docs]
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) ]
[docs]
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)
[docs]
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 abs_lcm(a,b):
"""Absolute value of least common multiple."""
return _np.abs(_np.lcm(a,b))
max_denominator = int(10**(N_significant-1))
v_ = _np.asarray(v)
if _np.issubdtype(v_.dtype,_np.inexact):
v_ = _np.round(_np.asarray(v,_np.float64)/_np.max(_np.abs(v)),N_significant)
m = (v_ * _reduce(abs_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
[docs]
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))
array([0.3660, 0.3660])
>>> project_equal_angle(np.ones(3),direction='x',normalize=False,keepdims=True)
array([0. , 0.5, 0.5])
>>> project_equal_angle([0,1,1],direction='y',normalize=True,keepdims=False)
array([0.4142, 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]
[docs]
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))
array([0.4597, 0.4597])
>>> project_equal_area(np.ones(3),direction='x',normalize=False,keepdims=True)
array([0. , 0.7071, 0.7071])
>>> project_equal_area([0,1,1],direction='y',normalize=True,keepdims=False)
array([0.5412, 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]
[docs]
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 = _np.int_(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]]
[docs]
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)
[docs]
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)
[docs]
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)
[docs]
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 _standardize_MillerBravais(idx: _IntSequence) -> _np.ndarray:
"""
Convert Miller-Bravais indices with missing component to standard (full) form.
Parameters
----------
idx : numpy.ndarray, shape (...,4) or (...,3)
Miller–Bravais indices of crystallographic direction [uvtw] or plane normal (hkil).
The third index (t or i) can be omitted completely or given as "..." (Ellipsis).
Returns
-------
uvtw|hkil : numpy.ndarray, shape (...,4)
Miller-Bravais indices of [uvtw] direction or (hkil) plane normal.
"""
def expand(v: _np.ndarray) -> _np.ndarray:
"""Expand from 3 to 4 indices."""
return _np.block([v[...,:2], -_np.sum(v[...,:2],axis=-1,keepdims=True), v[...,2:]])
a = _np.asarray(idx)
if _np.issubdtype(a.dtype,_np.signedinteger):
if a.shape[-1] == 4:
if (_np.sum(a[...,:3],axis=-1) != 0).any(): raise ValueError(rf'u+v+t≠0 | h+k+i≠0: {a}')
return a
elif a.shape[-1] == 3:
return expand(a)
else:
if a.shape[-1] == 4:
b = (_np.block([a[...,:2],
_np.where(a[...,2:3] == ..., -_np.sum(a[...,:2],axis=-1,keepdims=True),a[...,2:3]),
a[...,3:]]))
if (_np.sum(b[...,:3].astype(int),axis=-1) != 0).any(): raise ValueError(rf'u+v+t≠0 | h+k+i≠0: {b}')
elif a.shape[-1] == 3:
b = expand(a)
if (b != (c := b.astype(int))).any():
raise ValueError(f'"uvtw" | "hkil" are not (castable to) signed integers: {a}')
return c
raise ValueError(f'invalid Miller-Bravais indices {a}')
[docs]
def Bravais_to_Miller(*,
uvtw: _Optional[_IntSequence] = None,
hkil: _Optional[_IntSequence] = None) -> _np.ndarray: # numpydoc ignore=PR01,PR02
"""
Transform 4 Miller–Bravais indices to 3 Miller indices of crystal direction [uvw] or plane normal (hkl).
Parameters
----------
uvtw|hkil : numpy.ndarray, shape (...,4) or (...,3)
Miller–Bravais indices of crystallographic direction [uvtw] or plane normal (hkil).
The third index (t or i) can be omitted completely or given as "..." (Ellipsis).
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"')
elif uvtw is not None:
axis,basis = _standardize_MillerBravais(uvtw),_np.array([[2,1,0,0],
[1,2,0,0],
[0,0,0,1]])
elif hkil is not None:
axis,basis = _standardize_MillerBravais(hkil),_np.array([[1,0,0,0],
[0,1,0,0],
[0,0,0,1]])
uvw_hkl = _np.einsum('il,...l',basis,axis)
return uvw_hkl//_np.gcd.reduce(uvw_hkl,axis=-1,keepdims=True)
MillerBravais_to_Miller = Bravais_to_Miller
[docs]
def Miller_to_Bravais(*,
uvw: _Optional[_IntSequence] = None,
hkl: _Optional[_IntSequence] = None) -> _np.ndarray: # numpydoc ignore=PR01,PR02
"""
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.asarray(uvw),_np.array([[ 2,-1, 0],
[-1, 2, 0],
[-1,-1, 0],
[ 0, 0, 3]])) \
if hkl is None else \
(_np.asarray(hkl),_np.array([[ 1, 0, 0],
[ 0, 1, 0],
[-1,-1, 0],
[ 0, 0, 1]]))
if (axis != axis.astype(int)).any():
raise ValueError(f'"uvt" | "hki" are not (castable to) signed integers: {axis}')
uvtw_hkil = _np.einsum('il,...l',basis,axis.astype(int))
return uvtw_hkil//_np.gcd.reduce(uvtw_hkil,axis=-1,keepdims=True)
Miller_to_MillerBravais = Miller_to_Bravais
[docs]
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
[docs]
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
[docs]
def to_list(a: _Any) -> list:
"""
Put into list.
Parameters
----------
a : any
Variable to put into list or convert to list.
Returns
-------
l : list
Data in list.
"""
return [a] if not hasattr(a,'__iter__') or isinstance(a,str) else list(a)
####################################################################################################
# Classes
####################################################################################################
[docs]
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')
