DAMASK_EICMD/lib/damask/util.py

486 lines
18 KiB
Python

# -*- coding: UTF-8 no BOM -*-
import sys,time,random,threading,os,subprocess,shlex
import numpy as np
from optparse import Option
class bcolors:
"""
ASCII Colors (Blender code)
https://svn.blender.org/svnroot/bf-blender/trunk/blender/build_files/scons/tools/bcolors.py
http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
"""
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
DIM = '\033[2m'
UNDERLINE = '\033[4m'
def disable(self):
self.HEADER = ''
self.OKBLUE = ''
self.OKGREEN = ''
self.WARNING = ''
self.FAIL = ''
self.ENDC = ''
self.BOLD = ''
self.UNDERLINE = ''
# -----------------------------
def srepr(arg,glue = '\n'):
"""Joins arguments as individual lines"""
if (not hasattr(arg, "strip") and
(hasattr(arg, "__getitem__") or
hasattr(arg, "__iter__"))):
return glue.join(str(x) for x in arg)
return arg if isinstance(arg,str) else repr(arg)
# -----------------------------
def croak(what, newline = True):
"""Writes formated to stderr"""
sys.stderr.write(srepr(what,glue = '\n') + ('\n' if newline else ''))
sys.stderr.flush()
# -----------------------------
def report(who = None,
what = None):
"""Reports script and file name"""
croak( (emph(who)+': ' if who is not None else '') + (what if what is not None else '') )
# -----------------------------
def report_geom(info,
what = ['grid','size','origin','homogenization','microstructures']):
"""Reports (selected) geometry information"""
output = {
'grid' : 'grid a b c: {}'.format(' x '.join(list(map(str,info['grid' ])))),
'size' : 'size x y z: {}'.format(' x '.join(list(map(str,info['size' ])))),
'origin' : 'origin x y z: {}'.format(' : '.join(list(map(str,info['origin'])))),
'homogenization' : 'homogenization: {}'.format(info['homogenization']),
'microstructures' : 'microstructures: {}'.format(info['microstructures']),
}
for item in what: croak(output[item.lower()])
# -----------------------------
def emph(what):
"""Boldens string"""
return bcolors.BOLD+srepr(what)+bcolors.ENDC
# -----------------------------
def deemph(what):
"""Dims string"""
return bcolors.DIM+srepr(what)+bcolors.ENDC
# -----------------------------
def delete(what):
"""Dims string"""
return bcolors.DIM+srepr(what)+bcolors.ENDC
# -----------------------------
def execute(cmd,
streamIn = None,
wd = './'):
"""Executes a command in given directory and returns stdout and stderr for optional stdin"""
initialPath = os.getcwd()
os.chdir(wd)
process = subprocess.Popen(shlex.split(cmd),
stdout = subprocess.PIPE,
stderr = subprocess.PIPE,
stdin = subprocess.PIPE)
out,error = [i for i in (process.communicate() if streamIn is None
else process.communicate(streamIn.read().encode('utf-8')))]
out = out.decode('utf-8').replace('\x08','')
error = error.decode('utf-8').replace('\x08','')
os.chdir(initialPath)
if process.returncode != 0: raise RuntimeError('{} failed with returncode {}'.format(cmd,process.returncode))
return out,error
def coordGridAndSize(coordinates):
"""Determines grid count and overall physical size along each dimension of an ordered array of coordinates"""
dim = coordinates.shape[1]
coords = [np.unique(coordinates[:,i]) for i in range(dim)]
mincorner = np.array(list(map(min,coords)))
maxcorner = np.array(list(map(max,coords)))
grid = np.array(list(map(len,coords)),'i')
size = grid/np.maximum(np.ones(dim,'d'), grid-1.0) * (maxcorner-mincorner) # size from edge to edge = dim * n/(n-1)
size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 equal to smallest among other ones
return grid,size
# -----------------------------
class extendableOption(Option):
"""
Used for definition of new option parser action 'extend', which enables to take multiple option arguments
taken from online tutorial http://docs.python.org/library/optparse.html
"""
ACTIONS = Option.ACTIONS + ("extend",)
STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
def take_action(self, action, dest, opt, value, values, parser):
if action == "extend":
lvalue = value.split(",")
values.ensure_value(dest, []).extend(lvalue)
else:
Option.take_action(self, action, dest, opt, value, values, parser)
# Print iterations progress
# from https://gist.github.com/aubricus/f91fb55dc6ba5557fbab06119420dd6a
def progressBar(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):
"""
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required : total iterations (Int)
prefix - Optional : prefix string (Str)
suffix - Optional : suffix string (Str)
decimals - Optional : positive number of decimals in percent complete (Int)
bar_length - Optional : character length of bar (Int)
"""
str_format = "{0:." + str(decimals) + "f}"
percents = str_format.format(100 * (iteration / float(total)))
filled_length = int(round(bar_length * iteration / float(total)))
bar = '' * filled_length + '-' * (bar_length - filled_length)
sys.stderr.write('\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),
if iteration == total: sys.stderr.write('\n\n')
sys.stderr.flush()
# -----------------------------
class backgroundMessage(threading.Thread):
"""Reporting with animation to indicate progress"""
choices = {'bounce': ['_', 'o', 'O', '°', '', '', '°', 'O', 'o', '_'],
'spin': ['', '', '', ''],
'circle': ['', '', '', ''],
'hexagon': ['', ''],
'square': ['', '', '', ''],
'triangle': ['', '', '', '', '', ''],
'amoeba': ['', '', '', '', '', '', '', ''],
'beat': ['', '', '', '', '', '', '', '', '', '', ''],
'prison': ['', '', '', '', '', '', '', ''],
'breath': ['', '', '', '', '', '', '', '', ''],
'pulse': ['·', '', '', '', ''],
'ant': ['', '', '', '', '', '', '', '', '', '', '', ''],
'juggle': ['', '', '', '', '', '', '', '', ''],
# 'wobbler': ['▁', '◣', '▏', '◤', '▔', '◥', '▕', '◢'],
'grout': ['', '', '', ''],
'partner': ['', '', '', '', '', ''],
'classic': ['-', '\\', '|', '/',],
}
def __init__(self,symbol = None,wait = 0.1):
"""Sets animation symbol"""
super(backgroundMessage, self).__init__()
self._stop = threading.Event()
self.message = ''
self.new_message = ''
self.counter = 0
self.gap = ' '
self.symbols = self.choices[symbol if symbol in self.choices else random.choice(list(self.choices.keys()))]
self.waittime = wait
def __quit__(self):
"""Cleans output"""
length = len(self.symbols[self.counter] + self.gap + self.message)
sys.stderr.write(chr(8)*length + ' '*length + chr(8)*length)
sys.stderr.write('')
sys.stderr.flush()
def stop(self):
self._stop.set()
def stopped(self):
return self._stop.is_set()
def run(self):
while not threading.enumerate()[0]._Thread__stopped:
time.sleep(self.waittime)
self.update_message()
self.__quit__()
def set_message(self, new_message):
self.new_message = new_message
self.print_message()
def print_message(self):
length = len(self.symbols[self.counter] + self.gap + self.message)
sys.stderr.write(chr(8)*length + ' '*length + chr(8)*length + \
self.symbols[self.counter] + self.gap + self.new_message) # delete former and print new message
sys.stderr.flush()
self.message = self.new_message
def update_message(self):
self.counter = (self.counter + 1)%len(self.symbols)
self.print_message()
def animation(self,which = None):
return ''.join(self.choices[which]) if which in self.choices else ''
def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
col_deriv=0, ftol=1.49012e-8, xtol=1.49012e-8,
gtol=0.0, maxfev=0, epsfcn=None, factor=100, diag=None):
from scipy.optimize import _minpack
"""
Non-linear least square fitting (Levenberg-Marquardt method) with
bounded parameters.
the codes of transformation between int <-> ext refers to the work of
Jonathan J. Helmus: https://github.com/jjhelmus/leastsqbound-scipy
other codes refers to the source code of minpack.py:
..\Lib\site-packages\scipy\optimize\minpack.py
An internal parameter list is used to enforce contraints on the fitting
parameters. The transfomation is based on that of MINUIT package.
please see: F. James and M. Winkler. MINUIT User's Guide, 2004.
bounds : list
(min, max) pairs for each parameter, use None for 'min' or 'max'
when there is no bound in that direction.
For example: if there are two parameters needed to be fitting, then
bounds is [(min1,max1), (min2,max2)]
This function is based on 'leastsq' of minpack.py, the annotation of
other parameters can be found in 'leastsq'.
..\Lib\site-packages\scipy\optimize\minpack.py
"""
def _check_func(checker, argname, thefunc, x0, args, numinputs,
output_shape=None):
from numpy import shape
"""The same as that of minpack.py"""
res = np.atleast_1d(thefunc(*((x0[:numinputs],) + args)))
if (output_shape is not None) and (shape(res) != output_shape):
if (output_shape[0] != 1):
if len(output_shape) > 1:
if output_shape[1] == 1:
return shape(res)
msg = "%s: there is a mismatch between the input and output " \
"shape of the '%s' argument" % (checker, argname)
func_name = getattr(thefunc, '__name__', None)
if func_name:
msg += " '%s'." % func_name
else:
msg += "."
raise TypeError(msg)
if np.issubdtype(res.dtype, np.inexact):
dt = res.dtype
else:
dt = dtype(float)
return shape(res), dt
def _int2extGrad(p_int, bounds):
"""Calculate the gradients of transforming the internal (unconstrained) to external (constrained) parameter."""
grad = np.empty_like(p_int)
for i, (x, bound) in enumerate(zip(p_int, bounds)):
lower, upper = bound
if lower is None and upper is None: # No constraints
grad[i] = 1.0
elif upper is None: # only lower bound
grad[i] = x/np.sqrt(x*x + 1.0)
elif lower is None: # only upper bound
grad[i] = -x/np.sqrt(x*x + 1.0)
else: # lower and upper bounds
grad[i] = (upper - lower)*np.cos(x)/2.0
return grad
def _int2extFunc(bounds):
"""Transform internal parameters into external parameters."""
local = [_int2extLocal(b) for b in bounds]
def _transform_i2e(p_int):
p_ext = np.empty_like(p_int)
p_ext[:] = [i(j) for i, j in zip(local, p_int)]
return p_ext
return _transform_i2e
def _ext2intFunc(bounds):
"""Transform external parameters into internal parameters."""
local = [_ext2intLocal(b) for b in bounds]
def _transform_e2i(p_ext):
p_int = np.empty_like(p_ext)
p_int[:] = [i(j) for i, j in zip(local, p_ext)]
return p_int
return _transform_e2i
def _int2extLocal(bound):
"""Transform a single internal parameter to an external parameter."""
lower, upper = bound
if lower is None and upper is None: # no constraints
return lambda x: x
elif upper is None: # only lower bound
return lambda x: lower - 1.0 + np.sqrt(x*x + 1.0)
elif lower is None: # only upper bound
return lambda x: upper + 1.0 - np.sqrt(x*x + 1.0)
else:
return lambda x: lower + ((upper - lower)/2.0)*(np.sin(x) + 1.0)
def _ext2intLocal(bound):
"""Transform a single external parameter to an internal parameter."""
lower, upper = bound
if lower is None and upper is None: # no constraints
return lambda x: x
elif upper is None: # only lower bound
return lambda x: np.sqrt((x - lower + 1.0)**2 - 1.0)
elif lower is None: # only upper bound
return lambda x: np.sqrt((x - upper - 1.0)**2 - 1.0)
else:
return lambda x: np.arcsin((2.0*(x - lower)/(upper - lower)) - 1.0)
i2e = _int2extFunc(bounds)
e2i = _ext2intFunc(bounds)
x0 = np.asarray(x0).flatten()
n = len(x0)
if len(bounds) != n:
raise ValueError('the length of bounds is inconsistent with the number of parameters ')
if not isinstance(args, tuple):
args = (args,)
shape, dtype = _check_func('leastsq', 'func', func, x0, args, n)
m = shape[0]
if n > m:
raise TypeError('Improper input: N=%s must not exceed M=%s' % (n, m))
if epsfcn is None:
epsfcn = np.finfo(dtype).eps
def funcWarp(x, *args):
return func(i2e(x), *args)
xi0 = e2i(x0)
if Dfun is None:
if maxfev == 0:
maxfev = 200*(n + 1)
retval = _minpack._lmdif(funcWarp, xi0, args, full_output, ftol, xtol,
gtol, maxfev, epsfcn, factor, diag)
else:
if col_deriv:
_check_func('leastsq', 'Dfun', Dfun, x0, args, n, (n, m))
else:
_check_func('leastsq', 'Dfun', Dfun, x0, args, n, (m, n))
if maxfev == 0:
maxfev = 100*(n + 1)
def DfunWarp(x, *args):
return Dfun(i2e(x), *args)
retval = _minpack._lmder(funcWarp, DfunWarp, xi0, args, full_output, col_deriv,
ftol, xtol, gtol, maxfev, factor, diag)
errors = {0: ["Improper input parameters.", TypeError],
1: ["Both actual and predicted relative reductions "
"in the sum of squares\n are at most %f" % ftol, None],
2: ["The relative error between two consecutive "
"iterates is at most %f" % xtol, None],
3: ["Both actual and predicted relative reductions in "
"the sum of squares\n are at most %f and the "
"relative error between two consecutive "
"iterates is at \n most %f" % (ftol, xtol), None],
4: ["The cosine of the angle between func(x) and any "
"column of the\n Jacobian is at most %f in "
"absolute value" % gtol, None],
5: ["Number of calls to function has reached "
"maxfev = %d." % maxfev, ValueError],
6: ["ftol=%f is too small, no further reduction "
"in the sum of squares\n is possible.""" % ftol,
ValueError],
7: ["xtol=%f is too small, no further improvement in "
"the approximate\n solution is possible." % xtol,
ValueError],
8: ["gtol=%f is too small, func(x) is orthogonal to the "
"columns of\n the Jacobian to machine "
"precision." % gtol, ValueError],
'unknown': ["Unknown error.", TypeError]}
info = retval[-1] # The FORTRAN return value
if info not in [1, 2, 3, 4] and not full_output:
if info in [5, 6, 7, 8]:
np.warnings.warn(errors[info][0], RuntimeWarning)
else:
try:
raise errors[info][1](errors[info][0])
except KeyError:
raise errors['unknown'][1](errors['unknown'][0])
mesg = errors[info][0]
x = i2e(retval[0])
if full_output:
grad = _int2extGrad(retval[0], bounds)
retval[1]['fjac'] = (retval[1]['fjac'].T / np.take(grad,
retval[1]['ipvt'] - 1)).T
cov_x = None
if info in [1, 2, 3, 4]:
from numpy.dual import inv
from numpy.linalg import LinAlgError
perm = np.take(np.eye(n), retval[1]['ipvt'] - 1, 0)
r = np.triu(np.transpose(retval[1]['fjac'])[:n, :])
R = np.dot(r, perm)
try:
cov_x = inv(np.dot(np.transpose(R), R))
except LinAlgError as inverror:
print(inverror)
pass
return (x, cov_x) + retval[1:-1] + (mesg, info)
else:
return (x, info)
def _general_function(params, ydata, xdata, function):
return function(xdata, *params) - ydata
def _weighted_general_function(params, ydata, xdata, function, weights):
return (function(xdata, *params) - ydata)*weights
def curve_fit_bound(f, xdata, ydata, p0=None, sigma=None, bounds=None, **kw):
"""Similar as 'curve_fit' in minpack.py"""
if p0 is None:
# determine number of parameters by inspecting the function
import inspect
args, varargs, varkw, defaults = inspect.getargspec(f)
if len(args) < 2:
msg = "Unable to determine number of fit parameters."
raise ValueError(msg)
if 'self' in args:
p0 = [1.0] * (len(args)-2)
else:
p0 = [1.0] * (len(args)-1)
if np.isscalar(p0):
p0 = np.array([p0])
args = (ydata, xdata, f)
if sigma is None:
func = _general_function
else:
func = _weighted_general_function
args += (1.0/np.asarray(sigma),)
return_full = kw.pop('full_output', False)
res = leastsqBound(func, p0, args=args, bounds = bounds, full_output=True, **kw)
(popt, pcov, infodict, errmsg, ier) = res
if ier not in [1, 2, 3, 4]:
msg = "Optimal parameters not found: " + errmsg
raise RuntimeError(msg)
if (len(ydata) > len(p0)) and pcov is not None:
s_sq = (func(popt, *args)**2).sum()/(len(ydata)-len(p0))
pcov = pcov * s_sq
else:
pcov = np.inf
return (popt, pcov, infodict, errmsg, ier) if return_full else (popt, pcov)