modified background threading because using it in postResults led to an dead lock.
rest modifications are just style
This commit is contained in:
Martin Diehl
parent
48233d2767
commit
75480bc677
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@ -6,11 +6,13 @@ import numpy as np
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from optparse import Option
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class bcolors:
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'''
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"""
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ASCII Colors (Blender code)
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https://svn.blender.org/svnroot/bf-blender/trunk/blender/build_files/scons/tools/bcolors.py
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http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
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'''
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"""
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HEADER = '\033[95m'
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OKBLUE = '\033[94m'
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OKGREEN = '\033[92m'
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@ -32,9 +34,8 @@ class bcolors:
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# -----------------------------
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def srepr(arg,
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glue = '\n'):
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# -----------------------------
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def srepr(arg,glue = '\n'):
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"""joins arguments as individual lines"""
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if (not hasattr(arg, "strip") and
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hasattr(arg, "__getitem__") or
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hasattr(arg, "__iter__")):
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@ -42,20 +43,19 @@ def srepr(arg,
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return arg if isinstance(arg,basestring) else repr(arg)
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# -----------------------------
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def croak(what,
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newline = True):
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# -----------------------------
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def croak(what, newline = True):
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"""writes formated to stderr"""
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sys.stderr.write(srepr(what,glue = '\n') + ('\n' if newline else ''))
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sys.stderr.flush()
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# -----------------------------
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def report(who,what):
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# -----------------------------
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"""reports script and file name"""
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croak( (emph(who) if who else '') + (': '+what if what else '') )
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# -----------------------------
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def emph(what):
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# -----------------------------
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"""emphasizes string on screen"""
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return bcolors.BOLD+srepr(what)+bcolors.ENDC
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# -----------------------------
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@ -68,7 +68,6 @@ for f in ['cos', 'sin', 'tan']:
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# -----------------------------
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def gridLocation(idx,res):
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# -----------------------------
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return ( idx % res[0], \
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( idx // res[0]) % res[1], \
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( idx // res[0] // res[1]) % res[2] )
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@ -76,7 +75,6 @@ def gridLocation(idx,res):
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# -----------------------------
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def gridIndex(location,res):
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# -----------------------------
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return ( location[0] % res[0] + \
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( location[1] % res[1]) * res[0] + \
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( location[2] % res[2]) * res[1] * res[0] )
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@ -84,9 +82,11 @@ def gridIndex(location,res):
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# -----------------------------
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class extendableOption(Option):
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# -----------------------------
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# used for definition of new option parser action 'extend', which enables to take multiple option arguments
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# taken from online tutorial http://docs.python.org/library/optparse.html
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"""
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used for definition of new option parser action 'extend', which enables to take multiple option arguments
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taken from online tutorial http://docs.python.org/library/optparse.html
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"""
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ACTIONS = Option.ACTIONS + ("extend",)
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STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
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@ -102,28 +102,34 @@ class extendableOption(Option):
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# -----------------------------
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class backgroundMessage(threading.Thread):
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# -----------------------------
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"""reporting with animation to indicate progress"""
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choices = {'bounce': ['_','o','O','°','¯','¯','°','O','o',],
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'circle': [u'\u25f4',u'\u25f5',u'\u25f6',u'\u25f7'],
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'hexagon': [u'\u2b22',u'\u2b23'],
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'square': [u'\u2596',u'\u2598',u'\u259d',u'\u2597'],
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'triangle': [u'\u140a',u'\u140a',u'\u1403',u'\u1405',u'\u1405',u'\u1403'],
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'amoeba': [u'\u2596',u'\u258f',u'\u2598',u'\u2594',u'\u259d',u'\u2595',u'\u2597',u'\u2582'],
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'beat': [u'\u2581',u'\u2582',u'\u2583',u'\u2585',u'\u2586',u'\u2587',u'\u2587',u'\u2586',u'\u2585',u'\u2583',u'\u2582',],
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'prison': [u'\u168b',u'\u168c',u'\u168d',u'\u168f',u'\u168e',u'\u168d',u'\u168c',u'\u168b',],
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'breath': [u'\u1690',u'\u1691',u'\u1692',u'\u1693',u'\u1694',u'\u1693',u'\u1692',u'\u1691',u'\u1690',],
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'amoeba': [u'\u2596',u'\u258f',u'\u2598',u'\u2594',u'\u259d',u'\u2595',
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u'\u2597',u'\u2582'],
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'beat': [u'\u2581',u'\u2582',u'\u2583',u'\u2585',u'\u2586',u'\u2587',
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u'\u2587',u'\u2586',u'\u2585',u'\u2583',u'\u2582',],
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'prison': [u'\u168b',u'\u168c',u'\u168d',u'\u168f',u'\u168e',u'\u168d',
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u'\u168c',u'\u168b',],
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'breath': [u'\u1690',u'\u1691',u'\u1692',u'\u1693',u'\u1694',u'\u1693',
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u'\u1692',u'\u1691',u'\u1690',],
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'pulse': [u'·',u'•',u'\u25cf',u'\u25cf',u'•',],
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'ant': [u'\u2801',u'\u2802',u'\u2810',u'\u2820',u'\u2804',u'\u2840',u'\u2880',u'\u2820',u'\u2804',u'\u2802',u'\u2810',u'\u2808'],
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'juggle': [u'\ua708',u'\ua709',u'\ua70a',u'\ua70b',u'\ua70c',u'\ua711',u'\ua710',u'\ua70f',u'\ua70d',],
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'ant': [u'\u2801',u'\u2802',u'\u2810',u'\u2820',u'\u2804',u'\u2840',
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u'\u2880',u'\u2820',u'\u2804',u'\u2802',u'\u2810',u'\u2808'],
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'juggle': [u'\ua708',u'\ua709',u'\ua70a',u'\ua70b',u'\ua70c',u'\ua711',
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u'\ua710',u'\ua70f',u'\ua70d',],
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# 'wobbler': [u'\u2581',u'\u25e3',u'\u258f',u'\u25e4',u'\u2594',u'\u25e5',u'\u2595',u'\u25e2',],
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'grout': [u'\u2581',u'\u258f',u'\u2594',u'\u2595',],
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'partner': [u'\u26ac',u'\u26ad',u'\u26ae',u'\u26af',u'\u26ae',u'\u26ad',],
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'classic': ['-', '\\', '|', '/',],
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}
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def __init__(self,
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symbol = None,
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wait = 0.1):
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def __init__(self,symbol = None,wait = 0.1):
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"""sets animation symbol"""
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super(backgroundMessage, self).__init__()
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self._stop = threading.Event()
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self.message = ''
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@ -134,6 +140,7 @@ class backgroundMessage(threading.Thread):
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self.waittime = wait
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def __quit__(self):
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"""cleans output"""
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length = len(self.symbols[self.counter] + self.gap + self.message)
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sys.stderr.write(chr(8)*length + ' '*length + chr(8)*length)
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sys.stderr.write('')
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@ -146,8 +153,7 @@ class backgroundMessage(threading.Thread):
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return self._stop.is_set()
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def run(self):
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# while not threading.enumerate()[0]._Thread__stopped:
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while not self.stopped():
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while not threading.enumerate()[0]._Thread__stopped:
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time.sleep(self.waittime)
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self.update_message()
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self.__quit__()
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@ -170,116 +176,19 @@ class backgroundMessage(threading.Thread):
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def animation(self,which = None):
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return ''.join(self.choices[which]) if which in self.choices else ''
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'''
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def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
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col_deriv=0, ftol=1.49012e-8, xtol=1.49012e-8,
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gtol=0.0, maxfev=0, epsfcn=None, factor=100, diag=None):
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from scipy.optimize import _minpack
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"""
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Non-linear least square fitting (Levenberg-Marquardt method) with
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bounded parameters.
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the codes of transformation between int <-> ext refers to the work of
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Jonathan J. Helmus: https://github.com/jjhelmus/leastsqbound-scipy
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other codes refers to the source code of minpack.py:
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..\Lib\site-packages\scipy\optimize\minpack.py
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'''
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from numpy import (array, arcsin, asarray, cos, dot, eye, empty_like,
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isscalar,finfo, take, triu, transpose, sqrt, sin)
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def _check_func(checker, argname, thefunc, x0, args, numinputs,
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output_shape=None):
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from numpy import atleast_1d, shape, issubdtype, dtype, inexact
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'''
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The same as that of minpack.py,
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'''
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res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
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if (output_shape is not None) and (shape(res) != output_shape):
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if (output_shape[0] != 1):
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if len(output_shape) > 1:
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if output_shape[1] == 1:
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return shape(res)
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msg = "%s: there is a mismatch between the input and output " \
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"shape of the '%s' argument" % (checker, argname)
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func_name = getattr(thefunc, '__name__', None)
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if func_name:
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msg += " '%s'." % func_name
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else:
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msg += "."
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raise TypeError(msg)
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if issubdtype(res.dtype, inexact):
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dt = res.dtype
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else:
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dt = dtype(float)
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return shape(res), dt
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def _int2extGrad(p_int, bounds):
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"""
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Calculate the gradients of transforming the internal (unconstrained)
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to external (constained) parameter.
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"""
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grad = empty_like(p_int)
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for i, (x, bound) in enumerate(zip(p_int, bounds)):
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lower, upper = bound
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if lower is None and upper is None: # No constraints
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grad[i] = 1.0
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elif upper is None: # only lower bound
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grad[i] = x/sqrt(x*x + 1.0)
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elif lower is None: # only upper bound
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grad[i] = -x/sqrt(x*x + 1.0)
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else: # lower and upper bounds
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grad[i] = (upper - lower)*cos(x)/2.0
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return grad
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def _int2extFunc(bounds):
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'''
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transform internal parameters into external parameters.
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'''
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local = [_int2extLocal(b) for b in bounds]
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def _transform_i2e(p_int):
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p_ext = empty_like(p_int)
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p_ext[:] = [i(j) for i, j in zip(local, p_int)]
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return p_ext
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return _transform_i2e
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def _ext2intFunc(bounds):
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'''
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transform external parameters into internal parameters.
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'''
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local = [_ext2intLocal(b) for b in bounds]
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def _transform_e2i(p_ext):
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p_int = empty_like(p_ext)
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p_int[:] = [i(j) for i, j in zip(local, p_ext)]
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return p_int
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return _transform_e2i
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def _int2extLocal(bound):
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'''
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transform a single internal parameter to an external parameter.
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'''
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lower, upper = bound
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if lower is None and upper is None: # no constraints
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return lambda x: x
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elif upper is None: # only lower bound
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return lambda x: lower - 1.0 + sqrt(x*x + 1.0)
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elif lower is None: # only upper bound
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return lambda x: upper + 1.0 - sqrt(x*x + 1.0)
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else:
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return lambda x: lower + ((upper - lower)/2.0)*(sin(x) + 1.0)
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def _ext2intLocal(bound):
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'''
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transform a single external parameter to an internal parameter.
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'''
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lower, upper = bound
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if lower is None and upper is None: # no constraints
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return lambda x: x
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elif upper is None: # only lower bound
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return lambda x: sqrt((x - lower + 1.0)**2 - 1.0)
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elif lower is None: # only upper bound
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return lambda x: sqrt((x - upper - 1.0)**2 - 1.0)
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else:
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return lambda x: arcsin((2.0*(x - lower)/(upper - lower)) - 1.0)
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def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
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col_deriv=0, ftol=1.49012e-8, xtol=1.49012e-8,
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gtol=0.0, maxfev=0, epsfcn=None, factor=100, diag=None):
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from scipy.optimize import _minpack
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'''
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An internal parameter list is used to enforce contraints on the fitting
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parameters. The transfomation is based on that of MINUIT package.
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please see: F. James and M. Winkler. MINUIT User's Guide, 2004.
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@ -293,11 +202,92 @@ def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
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This function is based on 'leastsq' of minpack.py, the annotation of
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other parameters can be found in 'leastsq'.
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..\Lib\site-packages\scipy\optimize\minpack.py
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'''
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"""
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def _check_func(checker, argname, thefunc, x0, args, numinputs,
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output_shape=None):
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"""The same as that of minpack.py"""
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res = np.atleast_1d(thefunc(*((x0[:numinputs],) + args)))
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if (output_shape is not None) and (shape(res) != output_shape):
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if (output_shape[0] != 1):
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if len(output_shape) > 1:
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if output_shape[1] == 1:
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return shape(res)
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msg = "%s: there is a mismatch between the input and output " \
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"shape of the '%s' argument" % (checker, argname)
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func_name = getattr(thefunc, '__name__', None)
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if func_name:
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msg += " '%s'." % func_name
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else:
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msg += "."
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raise TypeError(msg)
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if np.issubdtype(res.dtype, np.inexact):
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dt = res.dtype
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else:
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dt = dtype(float)
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return shape(res), dt
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def _int2extGrad(p_int, bounds):
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"""Calculate the gradients of transforming the internal (unconstrained) to external (constained) parameter."""
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grad = np.empty_like(p_int)
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for i, (x, bound) in enumerate(zip(p_int, bounds)):
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lower, upper = bound
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if lower is None and upper is None: # No constraints
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grad[i] = 1.0
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elif upper is None: # only lower bound
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grad[i] = x/np.sqrt(x*x + 1.0)
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elif lower is None: # only upper bound
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grad[i] = -x/np.sqrt(x*x + 1.0)
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else: # lower and upper bounds
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grad[i] = (upper - lower)*np.cos(x)/2.0
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return grad
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def _int2extFunc(bounds):
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"""transform internal parameters into external parameters."""
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local = [_int2extLocal(b) for b in bounds]
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def _transform_i2e(p_int):
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p_ext = np.empty_like(p_int)
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p_ext[:] = [i(j) for i, j in zip(local, p_int)]
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return p_ext
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return _transform_i2e
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|
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def _ext2intFunc(bounds):
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"""transform external parameters into internal parameters."""
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local = [_ext2intLocal(b) for b in bounds]
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def _transform_e2i(p_ext):
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p_int = np.empty_like(p_ext)
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p_int[:] = [i(j) for i, j in zip(local, p_ext)]
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return p_int
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return _transform_e2i
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def _int2extLocal(bound):
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"""transform a single internal parameter to an external parameter."""
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lower, upper = bound
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if lower is None and upper is None: # no constraints
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return lambda x: x
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elif upper is None: # only lower bound
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return lambda x: lower - 1.0 + np.sqrt(x*x + 1.0)
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elif lower is None: # only upper bound
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return lambda x: upper + 1.0 - np.sqrt(x*x + 1.0)
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else:
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return lambda x: lower + ((upper - lower)/2.0)*(np.sin(x) + 1.0)
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|
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def _ext2intLocal(bound):
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"""transform a single external parameter to an internal parameter."""
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lower, upper = bound
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if lower is None and upper is None: # no constraints
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return lambda x: x
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elif upper is None: # only lower bound
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return lambda x: np.sqrt((x - lower + 1.0)**2 - 1.0)
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elif lower is None: # only upper bound
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return lambda x: np.sqrt((x - upper - 1.0)**2 - 1.0)
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else:
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return lambda x: np.arcsin((2.0*(x - lower)/(upper - lower)) - 1.0)
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i2e = _int2extFunc(bounds)
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e2i = _ext2intFunc(bounds)
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x0 = asarray(x0).flatten()
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x0 = np.asarray(x0).flatten()
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n = len(x0)
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if len(bounds) != n:
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|
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@ -312,9 +302,8 @@ def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
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if n > m:
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raise TypeError('Improper input: N=%s must not exceed M=%s' % (n, m))
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if epsfcn is None:
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epsfcn = finfo(dtype).eps
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epsfcn = np.finfo(dtype).eps
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# wrapped func
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def funcWarp(x, *args):
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return func(i2e(x), *args)
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|
|
@ -333,7 +322,6 @@ def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
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if maxfev == 0:
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maxfev = 100*(n + 1)
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# wrapped Dfun
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def DfunWarp(x, *args):
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return Dfun(i2e(x), *args)
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|
|
@ -369,7 +357,7 @@ def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
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if info not in [1, 2, 3, 4] and not full_output:
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if info in [5, 6, 7, 8]:
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warnings.warn(errors[info][0], RuntimeWarning)
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np.warnings.warn(errors[info][0], RuntimeWarning)
|
||||
else:
|
||||
try:
|
||||
raise errors[info][1](errors[info][0])
|
||||
|
|
@ -381,17 +369,17 @@ def leastsqBound(func, x0, args=(), bounds=None, Dfun=None, full_output=0,
|
|||
|
||||
if full_output:
|
||||
grad = _int2extGrad(retval[0], bounds)
|
||||
retval[1]['fjac'] = (retval[1]['fjac'].T / take(grad,
|
||||
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 = take(eye(n), retval[1]['ipvt'] - 1, 0)
|
||||
r = triu(transpose(retval[1]['fjac'])[:n, :])
|
||||
R = dot(r, perm)
|
||||
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(dot(transpose(R), R))
|
||||
cov_x = inv(np.dot(np.transpose(R), R))
|
||||
except LinAlgError as inverror:
|
||||
print inverror
|
||||
pass
|
||||
|
|
@ -405,7 +393,7 @@ 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'''
|
||||
"""Similar as 'curve_fit' in minpack.py"""
|
||||
if p0 is None:
|
||||
# determine number of parameters by inspecting the function
|
||||
import inspect
|
||||
|
|
@ -418,15 +406,15 @@ def curve_fit_bound(f, xdata, ydata, p0=None, sigma=None, bounds=None, **kw):
|
|||
else:
|
||||
p0 = [1.0] * (len(args)-1)
|
||||
|
||||
if isscalar(p0):
|
||||
p0 = array([p0])
|
||||
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/asarray(sigma),)
|
||||
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)
|
||||
|
|
@ -440,7 +428,7 @@ def curve_fit_bound(f, xdata, ydata, p0=None, sigma=None, bounds=None, **kw):
|
|||
s_sq = (func(popt, *args)**2).sum()/(len(ydata)-len(p0))
|
||||
pcov = pcov * s_sq
|
||||
else:
|
||||
pcov = inf
|
||||
pcov = np.inf
|
||||
|
||||
if return_full:
|
||||
return popt, pcov, infodict, errmsg, ier
|
||||
|
|
@ -449,13 +437,11 @@ def curve_fit_bound(f, xdata, ydata, p0=None, sigma=None, bounds=None, **kw):
|
|||
|
||||
|
||||
def execute(cmd,streamIn=None,wd='./'):
|
||||
'''
|
||||
executes a command in given directory and returns stdout and stderr for optional stdin
|
||||
'''
|
||||
"""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)
|
||||
if streamIn != None:
|
||||
if streamIn is not None:
|
||||
out,error = process.communicate(streamIn.read())
|
||||
else:
|
||||
out,error = process.communicate()
|
||||
|
|
|
|||
Loading…
Reference in New Issue