1. Replace curve_fit(see minpack.py) with curve_fit_bound(see damask/util.py), now the boundary constraint can be applied.
2. Transfer the output information into dictionary 'fittingCriteria', compact subroutine 'fit'.
This commit is contained in:
Haiming Zhang
parent
ce09e992cc
commit
0350b1829c
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@ -7,6 +7,7 @@ from scipy.optimize import curve_fit
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from scipy.linalg import svd
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from optparse import OptionParser
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import damask
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from damask.util import curve_fit_bound
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = scriptID.split()[1][:-3]
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@ -208,27 +209,48 @@ def Barlat1994(sigmas, sigma0, a):
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fittingCriteria = {
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'Tresca' :{'fit' :np.ones(1,'d'),'err':np.inf,
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'Tresca' :{'num' : 1,'err':np.inf,
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'name' : 'Tresca',
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'paras':'Initial yield stress:'},
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'vonMises' :{'fit' :np.ones(1,'d'),'err':np.inf,
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'paras': 'Initial yield stress:',
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'text' : '\nCoefficient of Tresca criterion:\nsigma0: ',
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'error': 'The standard deviation error is: '
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},
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'vonMises' :{'num' : 1,'err':np.inf,
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'name' : 'Huber-Mises-Hencky(von Mises)',
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'paras':'Initial yield stress:'},
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'Hosford' :{'fit' :np.ones(2,'d'),'err':np.inf,
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'paras': 'Initial yield stress:',
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'text' : '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: ',
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'error': 'The standard deviation error is: '
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},
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'Hosford' :{'num' : 2,'err':np.inf,
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'name' : 'Gerenal Hosford',
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'paras':'Initial yield stress:'},
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'Hill1948' :{'fit' :np.ones(6,'d'),'err':np.inf,
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'paras': 'Initial yield stress:',
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'text' : '\nCoefficient of Hosford criterion:\nsigma0, a: ',
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'error': 'The standard deviation errors are: '
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},
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'Hill1948' :{'num' : 6,'err':np.inf,
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'name' : 'Hill1948',
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'paras':'Normalized [F, G, H, L, M, N]'},
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'Drucker' :{'fit' :np.ones(2,'d'),'err':np.inf,
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'paras': 'Normalized [F, G, H, L, M, N]',
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'text' : '\nCoefficient of Hill1948 criterion:\n[F, G, H, L, M, N]:',
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'error': 'The standard deviation errors are: '
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},
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'Drucker' :{'num' : 2,'err':np.inf,
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'name' : 'Drucker',
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'paras':'Initial yield stress, C_D:'},
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'Barlat1991iso' :{'fit' :np.ones(1,'d'),'err':np.inf,
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'paras': 'Initial yield stress, C_D:',
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'text' : '\nCoefficient of Drucker criterion:\nsigma0, C_D: ',
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'error': 'The standard deviation errors are: '
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},
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'Barlat1991iso' :{'num' : 2,'err':np.inf,
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'name' : 'Barlat1991iso',
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'paras':'Initial yield stress, m:'},
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'Barlat1991aniso':{'fit' :np.ones(7,'d'),'err':np.inf,
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'paras': 'Initial yield stress, m:',
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'text' : '\nCoefficient of isotropic Barlat 1991 criterion:\nsigma0, m:\n',
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'error': 'The standard deviation errors are: '
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},
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'Barlat1991aniso':{'num' : 7,'err':np.inf,
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'name' : 'Barlat1991aniso',
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'paras':'Initial yield stress, m, a, b, c, f, g, h:'},
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'paras': 'Initial yield stress, m, a, b, c, f, g, h:',
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'text' : '\nCoefficient of anisotropic Barlat 1991 criterion:\nsigma0, \m, a, b, c, f, g, h:\n',
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'error': 'The standard deviation errors are: '
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},
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'worst' :{'err':np.inf},
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'best' :{'err':np.inf}
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}
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@ -290,48 +312,31 @@ class Criterion(object):
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def fit(self,stress):
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global fitResults
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if self.name.lower() == 'tresca':
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funResidum = Tresca
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text = '\nCoefficient of Tresca criterion:\nsigma0: '+formatOutput(1)
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error='The standard deviation error is: '+formatOutput(1,'%-14.8f')+'\n'
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elif self.name.lower() == 'vonmises':
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funResidum = vonMises
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text = '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: '+formatOutput(1)
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error='The standard deviation error is: '+formatOutput(1,'%-14.8f')+'\n'
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elif self.name.lower() == 'hosford':
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funResidum = Hosford
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text = '\nCoefficient of general Hosford criterion:\nsigma0, a: '+formatOutput(2)
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error='The standard deviation error is: '+formatOutput(2,'%-14.8f')+'\n'
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elif self.name.lower() == 'drucker':
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funResidum = Drucker
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text = '\nCoefficient of Drucker criterion:\nsigma0, C_D: '+formatOutput(2)
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error='The standard deviation errors are: '+formatOutput(2,'%-14.8f')+'\n'
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elif self.name.lower() == 'hill1948':
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funResidum = Hill1948
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text = '\nCoefficient of Hill1948 criterion:\n[F, G, H, L, M, N]:'+' '*16+formatOutput(6)
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error='The standard deviation errors are: '+formatOutput(6,'%-14.8f')+'\n'
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elif self.name.lower() == 'barlat1991iso':
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funResidum = Barlat1991iso
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text = '\nCoefficient of isotropic Barlat 1991 criterion:\nsigma0, m:\n'+formatOutput(2)
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error='The standard deviation errors are: '+formatOutput(1,'%-14.8f')+'\n'
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elif self.name.lower() == 'barlat1991aniso':
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funResidum = Barlat1991aniso
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text = '\nCoefficient of anisotropic Barlat 1991 criterion:\nsigma0, \m, a, b, c, f, g, h:\n' \
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+formatOutput(8)
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error='The standard deviation errors are: '+formatOutput(7,'%-14.8f')
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if fitResults == []:
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initialguess = fittingCriteria[funResidum.__name__]['fit']
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else:
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initialguess = np.array(fitResults[-1])
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if self.name.lower() == 'tresca' : funResidum = Tresca
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elif self.name.lower() == 'vonmises' : funResidum = vonMises
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elif self.name.lower() == 'hosford' : funResidum = Hosford
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elif self.name.lower() == 'drucker' : funResidum = Drucker
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elif self.name.lower() == 'hill1948' : funResidum = Hill1948
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elif self.name.lower() == 'barlat1991iso' : funResidum = Barlat1991iso
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elif self.name.lower() == 'barlat1991aniso' : funResidum = Barlat1991aniso
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nameCriterion = funResidum.__name__
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numParas = fittingCriteria[nameCriterion]['num']
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textParas = fittingCriteria[nameCriterion]['text'] + formatOutput(numParas)
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textError = fittingCriteria[nameCriterion]['error']+ formatOutput(numParas,'%-14.8f')+'\n'
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bounds = [(None,None)]*numParas # Default bounds, no bound
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initialguess0 = np.ones(numParas,'d') # Default initial guess, depends on bounds
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if fitResults == [] : initialguess = initialguess0
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else : initialguess = np.array(fitResults[-1])
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weight = get_weight(np.shape(stress)[1])
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try:
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popt, pcov = \
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curve_fit(funResidum, stress, np.zeros(np.shape(stress)[1]),
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initialguess, weight)
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curve_fit_bound(funResidum, stress, np.zeros(np.shape(stress)[1]),
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initialguess, weight, bounds)
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perr = np.sqrt(np.diag(pcov))
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fitResults.append(popt.tolist())
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print (text%array2tuple(popt))
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print (error%array2tuple(perr))
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print (textParas%array2tuple(popt))
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print (textError%array2tuple(perr))
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except Exception as detail:
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print detail
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pass
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