DAMASK_EICMD/python/damask/util.py

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"""Miscellaneous helper functionality."""
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import sys
import datetime
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import os
import subprocess
import shlex
import re
import fractions
import collections.abc as abc
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from functools import reduce
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from typing import Union, Tuple, Iterable, Callable, Dict, List, Any, Literal, Optional
from pathlib import Path
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import numpy as np
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import h5py
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from . import version
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from ._typehints import FloatSequence
# limit visibility
__all__=[
'srepr',
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'emph', 'deemph', 'warn', 'strikeout',
'run',
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'natural_sort',
'show_progress',
'scale_to_coprime',
'project_equal_angle', 'project_equal_area',
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'hybrid_IA',
'execution_stamp',
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'shapeshifter', 'shapeblender',
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'extend_docstring', 'extended_docstring',
'Bravais_to_Miller', 'Miller_to_Bravais',
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'DREAM3D_base_group', 'DREAM3D_cell_data_group',
'dict_prune', 'dict_flatten'
]
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# 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:
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r"""
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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'.
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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)
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else:
return msg if isinstance(msg,str) else repr(msg)
def emph(msg) -> str:
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"""
Format with emphasis.
Parameters
----------
msg : object with __repr__ or sequence of objects with __repr__
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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:
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"""
Format with deemphasis.
Parameters
----------
msg : object with __repr__ or sequence of objects with __repr__
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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:
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"""
Format for warning.
Parameters
----------
msg : object with __repr__ or sequence of objects with __repr__
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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:
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"""
Format as strikeout.
Parameters
----------
msg : object with __repr__ or iterable of objects with __repr__
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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]:
"""
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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.
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timeout : integer, optional
Timeout in seconds.
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Returns
-------
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stdout, stderr : (str, str)
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Output of the executed command.
"""
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print(f"running '{cmd}' in '{wd}'")
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process = subprocess.run(shlex.split(cmd),
stdout = subprocess.PIPE,
stderr = subprocess.PIPE,
env = os.environ if env is None else env,
cwd = wd,
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encoding = 'utf-8',
timeout = timeout)
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if process.returncode != 0:
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print(process.stdout)
print(process.stderr)
raise RuntimeError(f"'{cmd}' failed with returncode {process.returncode}")
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return process.stdout, process.stderr
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execute = run
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def natural_sort(key: str) -> List[Union[int, str]]:
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"""
Natural sort.
For use in python's 'sorted'.
References
----------
https://en.wikipedia.org/wiki/Natural_sort_order
"""
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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,
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N_iter: int = None,
prefix: str = '',
bar_length: int = 50) -> Any:
"""
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Decorate a loop with a progress bar.
Use similar like enumerate.
Parameters
----------
iterable : iterable
Iterable to be decorated.
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N_iter : int, optional
Total number of iterations. Required if iterable is not a sequence.
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prefix : str, optional
Prefix string.
bar_length : int, optional
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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')
else:
N = N_iter
if N <= 1:
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for item in iterable:
yield item
else:
status = ProgressBar(N,prefix,bar_length)
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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.
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Returns
-------
m : numpy.ndarray, shape (:)
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Vector scaled to co-prime numbers.
"""
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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(int)
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_}. Insufficient precision?')
return m
def project_equal_angle(vector: np.ndarray,
direction: Literal['x', 'y', 'z'] = 'z',
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.
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.
Returns
-------
coordinates : numpy.ndarray, shape (...,2 | 3)
Projected coordinates.
Examples
--------
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>>> 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]
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def project_equal_area(vector: np.ndarray,
direction: Literal['x', 'y', 'z'] = 'z',
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.
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.
Returns
-------
coordinates : numpy.ndarray, shape (...,2 | 3)
Projected coordinates.
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]
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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 = 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
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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(int)
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) \
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]]
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def shapeshifter(fro: Tuple[int, ...],
to: Tuple[int, ...],
mode: Literal['left','right'] = 'left',
keep_ones: bool = False) -> Tuple[Optional[int], ...]:
"""
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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.
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Returns
-------
new_dims : tuple
Dimensions for reshape.
Example
-------
>>> 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 not len(fro) and not len(to): return ()
beg = dict(left ='(^.*\\b)',
right='(^.*?\\b)')
sep = dict(left ='(.*\\b)',
right='(.*?\\b)')
end = dict(left ='(.*?$)',
right='(.*$)')
fro = (1,) if not len(fro) else fro
to = (1,) if not len(to) else to
try:
match = re.match(beg[mode]
+f',{sep[mode]}'.join(map(lambda x: f'{x}'
if x>1 or (keep_ones and len(fro)>1) else
'\\d+',fro))
+f',{end[mode]}',','.join(map(str,to))+',')
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assert match
grp = match.groups()
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except AssertionError:
raise ValueError(f'Shapes can not be shifted {fro} --> {to}')
fill: Tuple[Optional[int], ...] = ()
for g,d in zip(grp,fro+(None,)):
fill += (1,)*g.count(',')+(d,)
return fill[:-1]
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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:]
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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
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def extended_docstring(f: Callable, extra_docstring: str) -> Callable:
"""
Decorator: Combine another function's docstring with a given docstring.
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Parameters
----------
f : function
Function of which the docstring is taken.
extra_docstring : str
Docstring to append.
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"""
def _decorator(func):
func.__doc__ = f.__doc__ + extra_docstring
return func
return _decorator
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def DREAM3D_base_group(fname: Union[str, Path]) -> str:
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"""
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
----------
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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
-------
path : str
Path to the base group.
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"""
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with h5py.File(fname,'r') as f:
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
def DREAM3D_cell_data_group(fname: Union[str, Path]) -> str:
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"""
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
----------
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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
-------
path : str
Path to the cell data group.
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"""
base_group = DREAM3D_base_group(fname)
with h5py.File(fname,'r') as f:
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)
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: np.ndarray = None, hkil: np.ndarray = None) -> np.ndarray:
"""
Transform 4 MillerBravais indices to 3 Miller indices of crystal direction [uvw] or plane normal (hkl).
Parameters
----------
uvtw|hkil : numpy.ndarray, shape (...,4)
MillerBravais 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)
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def Miller_to_Bravais(*, uvw: np.ndarray = None, hkl: np.ndarray = None) -> np.ndarray:
"""
Transform 3 Miller indices to 4 MillerBravais 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)
MillerBravais 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:
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"""
Recursively remove empty dictionaries.
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Parameters
----------
d : dict
Dictionary to prune.
Returns
-------
pruned : dict
Pruned dictionary.
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"""
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# https://stackoverflow.com/questions/48151953
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new = {}
for k,v in d.items():
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if isinstance(v, dict):
v = dict_prune(v)
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if not isinstance(v,dict) or v != {}:
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new[k] = v
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return new
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def dict_flatten(d: Dict) -> Dict:
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"""
Recursively remove keys of single-entry dictionaries.
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Parameters
----------
d : dict
Dictionary to flatten.
Returns
-------
flattened : dict
Flattened dictionary.
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"""
if isinstance(d,dict) and len(d) == 1:
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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()}
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return new
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####################################################################################################
# Classes
####################################################################################################
class ProgressBar:
"""
Report progress of an interation as a status bar.
Works for 0-based loops, ETA is estimated by linear extrapolation.
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"""
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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
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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()
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def update(self, iteration: int) -> None:
fraction = (iteration+1) / self.total
filled_length = int(self.bar_length * fraction)
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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)
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remaining_time = (datetime.datetime.now() - self.time_start) \
* (self.total - (iteration+1)) / (iteration+1)
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remaining_time -= datetime.timedelta(microseconds=remaining_time.microseconds) # remove μs
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sys.stderr.write(f'\r{self.prefix} {bar} {fraction:>4.0%} ETA {remaining_time}')
sys.stderr.flush()
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self.fraction_last = fraction
if iteration == self.total - 1:
sys.stderr.write('\n')
sys.stderr.flush()