"""Miscellaneous helper functionality.""" import sys import datetime import os import subprocess import shlex import re import fractions from functools import reduce import numpy as np import h5py from . import version # limit visibility __all__=[ 'srepr', 'emph','deemph','warn','strikeout', 'execute', 'natural_sort', 'show_progress', 'scale_to_coprime', 'project_stereographic', 'hybrid_IA', 'execution_stamp', 'shapeshifter', 'shapeblender', 'extend_docstring', 'extended_docstring', 'Bravais_to_Miller', 'Miller_to_Bravais', 'DREAM3D_base_group', 'DREAM3D_cell_data_group', 'dict_prune', 'dict_flatten' ] # 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(arg,glue = '\n'): r""" Join items with glue string. Parameters ---------- arg : iterable 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(arg, 'strip') and (hasattr(arg, '__getitem__') or hasattr(arg, '__iter__'))): return glue.join(str(x) for x in arg) else: return arg if isinstance(arg,str) else repr(arg) def emph(what): """ Format with emphasis. Parameters ---------- what : object with __repr__ or iterable of objects with __repr__. Message to format. Returns ------- formatted : str Formatted string representation of the joined items. """ return _colors['bold']+srepr(what)+_colors['end_color'] def deemph(what): """ Format with deemphasis. Parameters ---------- what : object with __repr__ or iterable of objects with __repr__. Message to format. Returns ------- formatted : str Formatted string representation of the joined items. """ return _colors['dim']+srepr(what)+_colors['end_color'] def warn(what): """ Format for warning. Parameters ---------- what : object with __repr__ or iterable of objects with __repr__. Message to format. Returns ------- formatted : str Formatted string representation of the joined items. """ return _colors['warning']+emph(what)+_colors['end_color'] def strikeout(what): """ Format as strikeout. Parameters ---------- what : 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(what)+_colors['end_color'] def execute(cmd,wd='./',env=None): """ Execute command. Parameters ---------- cmd : str Command to be executed. wd : str, optional Working directory of process. Defaults to ./ . env : dict, optional Environment for execution. Returns ------- stdout, stderr : str Output of the executed command. """ print(f"executing '{cmd}' in '{wd}'") process = subprocess.run(shlex.split(cmd), stdout = subprocess.PIPE, stderr = subprocess.PIPE, env = os.environ if env is None else env, cwd = wd, encoding = 'utf-8') if process.returncode != 0: print(process.stdout) print(process.stderr) raise RuntimeError(f"'{cmd}' failed with returncode {process.returncode}") return process.stdout, process.stderr def natural_sort(key): """ 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,N_iter=None,prefix='',bar_length=50): """ Decorate a loop with a progress bar. Use similar like enumerate. Parameters ---------- iterable : iterable or function with yield statement Iterable (or function with yield statement) to be decorated. N_iter : int, optional Total number of iterations. Required unless obtainable as len(iterable). prefix : str, optional Prefix string. bar_length : int, optional Length of progress bar in characters. Defaults to 50. """ if N_iter in [0,1] or (hasattr(iterable,'__len__') and len(iterable) <= 1): for item in iterable: yield item else: status = _ProgressBar(N_iter if N_iter is not None else len(iterable),prefix,bar_length) for i,item in enumerate(iterable): yield item status.update(i) def scale_to_coprime(v): """ Scale vector to co-prime (relatively prime) integers. Parameters ---------- v : numpy.ndarray of shape (:) Vector to scale. Returns ------- m : numpy.ndarray of 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) m = (np.array(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_stereographic(vector,direction='z',normalize=True,keepdims=False): """ Apply stereographic projection to vector. Parameters ---------- vector : numpy.ndarray of shape (...,3) Vector coordinates to be projected. direction : str Projection direction 'x', 'y', or 'z'. Defaults to 'z'. normalize : bool Ensure unit length of input vector. Defaults to True. keepdims : bool Maintain three-dimensional output coordinates. Default 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 of shape (...,2 | 3) Projected coordinates. Examples -------- >>> import damask >>> import numpy as np >>> project_stereographic(np.ones(3)) [0.3660254, 0.3660254] >>> project_stereographic(np.ones(3),direction='x',normalize=False,keepdims=True) [0, 0.5, 0.5] >>> project_stereographic([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+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,function_name=None): """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,N,rng_seed=None): """ 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(int) 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,to,mode='left',keep_ones=False): """ 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 : str, 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. 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: grp = 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))+',').groups() except AttributeError: raise ValueError(f'Shapes can not be shifted {fro} --> {to}') fill = () for g,d in zip(grp,fro+(None,)): fill += (1,)*g.count(',')+(d,) return fill[:-1] def shapeblender(a,b): """ 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): """ 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,extra_docstring): """ 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): """ 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(fname,'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): """ 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(fname,'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=None,hkil=None): """ Transform 4 Miller–Bravais indices to 3 Miller indices of crystal direction [uvw] or plane normal (hkl). Parameters ---------- uvtw|hkil : numpy.ndarray of shape (...,4) Miller–Bravais indices of crystallographic direction [uvtw] or plane normal (hkil). Returns ------- uvw|hkl : numpy.ndarray of 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=None,hkl=None): """ Transform 3 Miller indices to 4 Miller–Bravais indices of crystal direction [uvtw] or plane normal (hkil). Parameters ---------- uvw|hkl : numpy.ndarray of shape (...,3) Miller indices of crystallographic direction [uvw] or plane normal (hkl). Returns ------- uvtw|hkil : numpy.ndarray of shape (...,4) Miller–Bravais indices of [uvtw] direction or (hkil) plane normal. """ if (uvw is not None) ^ (hkl is None): raise KeyError('Specify either "uvw" or "hkl"') axis,basis = (np.array(uvw),np.array([[ 2,-1, 0], [-1, 2, 0], [-1,-1, 0], [ 0, 0, 3]])/3) \ if hkl is None else \ (np.array(hkl),np.array([[ 1, 0, 0], [ 0, 1, 0], [-1,-1, 0], [ 0, 0, 1]])) return np.einsum('il,...l',basis,axis) def dict_prune(d): """ Recursively remove empty dictionaries. Parameters ---------- d : dict Dictionary to prune. Returns ------- pruned : dict Pruned dictionary. """ # https://stackoverflow.com/questions/48151953 new = {} for k,v in d.items(): if isinstance(v, dict): v = dict_prune(v) if not isinstance(v,dict) or v != {}: new[k] = v return new def dict_flatten(d): """ Recursively remove keys of single-entry dictionaries. Parameters ---------- d : dict Dictionary to flatten. Returns ------- flattened : dict Flattened dictionary. """ if isinstance(d,dict) and len(d) == 1: entry = d[list(d.keys())[0]] new = dict_flatten(entry.copy()) if isinstance(entry,dict) else entry else: new = {k: (dict_flatten(v) if isinstance(v, dict) else v) for k,v in d.items()} return new #################################################################################################### # Classes #################################################################################################### class _ProgressBar: """ Report progress of an interation as a status bar. Works for 0-based loops, ETA is estimated by linear extrapolation. """ def __init__(self,total,prefix,bar_length): """ Set current time as basis for ETA estimation. Parameters ---------- total : int Total # of iterations. prefix : str Prefix string. bar_length : int Character length of bar. """ self.total = total self.prefix = prefix self.bar_length = bar_length self.time_start = self.time_last_update = datetime.datetime.now() self.fraction_last = 0.0 sys.stderr.write(f"{self.prefix} {'░'*self.bar_length} 0% ETA n/a") sys.stderr.flush() def update(self,iteration): fraction = (iteration+1) / self.total filled_length = int(self.bar_length * fraction) if filled_length > int(self.bar_length * self.fraction_last) or \ datetime.datetime.now() - self.time_last_update > datetime.timedelta(seconds=10): self.time_last_update = datetime.datetime.now() bar = '█' * filled_length + '░' * (self.bar_length - filled_length) remaining_time = (datetime.datetime.now() - self.time_start) \ * (self.total - (iteration+1)) / (iteration+1) remaining_time -= datetime.timedelta(microseconds=remaining_time.microseconds) # remove μs sys.stderr.write(f'\r{self.prefix} {bar} {fraction:>4.0%} ETA {remaining_time}') sys.stderr.flush() self.fraction_last = fraction if iteration == self.total - 1: sys.stderr.write('\n') sys.stderr.flush()