Re-write the script:

1. replace curve_fit with leastsq, which supports the analytical Jacobian
2. specify a "class" (contains both residum and jacobian) for each criterion.
3. add the calculation of Jacobian
This commit is contained in:
Haiming Zhang 2015-02-11 16:49:40 +00:00
parent 1b1ed3bbcf
commit eed00007f9
1 changed files with 174 additions and 106 deletions

View File

@ -7,7 +7,7 @@ from scipy.optimize import curve_fit
from scipy.linalg import svd from scipy.linalg import svd
from optparse import OptionParser from optparse import OptionParser
import damask import damask
from damask.util import curve_fit_bound from damask.util import leastsqBound
scriptID = string.replace('$Id$','\n','\\n') scriptID = string.replace('$Id$','\n','\\n')
scriptName = scriptID.split()[1][:-3] scriptName = scriptID.split()[1][:-3]
@ -77,52 +77,55 @@ def get_weight(ndim):
# isotropic yield surfaces # isotropic yield surfaces
# --------------------------------------------------------------------------------------------- # ---------------------------------------------------------------------------------------------
def Tresca(sigmas, sigma0): class Tresca(object):
''' '''
residuum of Tresca yield criterion (eq. 2.26) residuum of Tresca yield criterion (eq. 2.26)
''' '''
def fun(self,sigma0, ydata, sigmas):
lambdas = principalStresses(sigmas) lambdas = principalStresses(sigmas)
r = np.amax(np.array([abs(lambdas[2,:]-lambdas[1,:]),\ r = np.amax(np.array([abs(lambdas[2,:]-lambdas[1,:]),\
abs(lambdas[1,:]-lambdas[0,:]),\ abs(lambdas[1,:]-lambdas[0,:]),\
abs(lambdas[0,:]-lambdas[2,:])]),0) - sigma0 abs(lambdas[0,:]-lambdas[2,:])]),0) - sigma0
return r.ravel() return r.ravel()
def jac(self,sigma0, ydata, sigmas):
return np.ones(len(ydata)) * (-1.0)
class vonMises(object):
def vonMises(sigmas, sigma0):
''' '''
residuum of Huber-Mises-Hencky yield criterion (eq. 2.37) residuum of Huber-Mises-Hencky yield criterion (eq. 2.37)
''' '''
def fun(self, sigma0, ydata, sigmas):
return HosfordBasis(sigma0, 1.0,1.0,1.0, 2.0, sigmas)
def jac(self, sigma0, ydata, sigmas):
return HosfordBasis(sigma0, 1.0,1.0,1.0, 2.0, sigmas, Jac=True, nParas=1)
return Hosford(sigmas, sigma0, 2.0) class Drucker(object):
def Drucker(sigmas, sigma0, C_D):
''' '''
residuum of Drucker yield criterion (eq. 2.41, F = sigma0) residuum of Drucker yield criterion (eq. 2.41, F = sigma0)
''' '''
def fun(sigma0, C_D, ydata, sigmas):
return DruckerBasis(sigma0, C_D, 1.0, ydata, sigmas)
def jac(sigma0, C_D, ydata, sigmas):
pass
return generalDrucker(sigmas, sigma0, C_D, 1.0) class generalDrucker(object):
def generalDrucker(sigmas, sigma0, C_D, p):
''' '''
residuum of general Drucker yield criterion (eq. 2.42, F = sigma0) residuum of general Drucker yield criterion (eq. 2.42, F = sigma0)
''' '''
Is = stressInvariants(principalStresses(sigmas)) def fun(sigma0, C_D, ydata, sigmas):
r = (Is[1,:]**(3.0*p)-C_D*Is[2,:]**(2.0*p)) - sigma0 return DruckerBasis(sigma0, C_D, p, ydata, sigmas)
return r.ravel() def jac(sigma0, C_D, ydata, sigmas):
pass
class Hosford(object):
def Hosford(sigmas, sigma0, a):
''' '''
residuum of Hershey yield criterion (eq. 2.43, Y = sigma0) residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
''' '''
lambdas = principalStresses(sigmas) def fun(self, (sigma0, a), ydata, sigmas):
r = ((abs(lambdas[2,:]-lambdas[1,:]))**a\ return HosfordBasis(sigma0, 1.0,1.0,1.0, a, sigmas)
+ (abs(lambdas[1,:]-lambdas[0,:]))**a\ def jac(self, (sigma0, a), ydata, sigmas):
+ (abs(lambdas[0,:]-lambdas[2,:]))**a) **(1.0/a)\ return HosfordBasis(sigma0, 1.0,1.0,1.0, a, sigmas, Jac=True, nParas=2)
-2.0**(1.0/a)*sigma0
return r.ravel()
#more to do #more to do
# KarafillisAndBoyce # KarafillisAndBoyce
@ -131,83 +134,51 @@ def Hosford(sigmas, sigma0, a):
# isotropic yield surfaces # isotropic yield surfaces
# --------------------------------------------------------------------------------------------- # ---------------------------------------------------------------------------------------------
def Hill1948(sigmas, F,G,H,L,M,N): class Hill1948(object):
''' '''
residuum of Hill 1948 quadratic yield criterion (eq. 2.48) residuum of Hill 1948 quadratic yield criterion (eq. 2.48)
''' '''
r = F*(sigmas[1]-sigmas[2])**2.0\ def fun(self, (F,G,H,L,M,N), ydata, sigmas):
+ G*(sigmas[2]-sigmas[0])**2.0\ r = F*(sigmas[1]-sigmas[2])**2.0 + G*(sigmas[2]-sigmas[0])**2.0 + H*(sigmas[0]-sigmas[1])**2.0\
+ H*(sigmas[0]-sigmas[1])**2.0\ + 2.0*L*sigmas[4]**2.0 + 2.0*M*sigmas[5]**2.0 + 2.0*N*sigmas[3]**2.0 - 1.0
+ 2.0*L* sigmas[4]**2.0\
+ 2.0*M* sigmas[5]**2.0\
+ 2.0*N* sigmas[3]**2.0\
- 1.0
return r.ravel()/2.0 return r.ravel()/2.0
def jac(self, (F,G,H,L,M,N), ydata, sigmas):
pass
#more to do #more to do
# Hill 1979 # Hill 1979
# Hill 1990,1993 need special stresses to fit # Hill 1990,1993 need special stresses to fit
def generalHosford(sigmas, sigma0, a): class generalHosford(object):
''' '''
residuum of Hershey yield criterion (eq. 2.104, sigma = sigma0) residuum of Hershey yield criterion (eq. 2.104, sigmas = sigma0)
''' '''
lambdas = principalStresses(sigmas) def fun(self, (sigma0, F, G, H, a), ydata, sigmas, nParas=5):
r = np.amax(np.array([F*(abs(lambdas[:,1]-lambdas[:,2]))**a,\ return HosfordBasis(sigma0, F, G, H, a, sigmas)
G*(abs(lambdas[:,2]-lambdas[:,0]))**a,\ def jac(self, (sigma0, F, G, H, a), ydata, sigmas):
H*(abs(lambdas[:,0]-lambdas[:,1]))**a]),1) - sigma0**a return HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=True, nParas=5)
return r.ravel()
class Barlat1991iso(object):
def Barlat1991(sigmas, sigma0, order, a, b, c, f, g, h):
'''
residuum of Barlat 1997 yield criterion
'''
cos = np.cos; pi = np.pi; abs = np.abs
A = a*(sigmas[1] - sigmas[2])
B = b*(sigmas[2] - sigmas[0])
C = c*(sigmas[0] - sigmas[1])
F = f*sigmas[4]
G = g*sigmas[5]
H = h*sigmas[3]
I2 = (F*F + G*G + H*H)/3.0 + ((A-C)**2+(C-B)**2+(B-A)**2)/54.0
I3 = (C-B)*(A-C) * (B-A)/54.0 + F*G*H - \
( (C-B)*F*F + (A-C)*G*G + (B-A)*H*H )/6.0
theta = np.arccos(I3/I2**1.5)
Phi = np.sqrt(3.0*I2)* (
(abs(2.0*cos((2.0*theta + pi)/6.0)))**order +
(abs(2.0*cos((2.0*theta + pi*3.0)/6.0)))**order +
(abs(2.0*cos(( 2.0*theta + pi*5.0)/6.0)))**order
)**(1.0/order)
r = Phi/2.0**(1.0/order) - sigma0
return r.ravel()
def Barlat1991iso(sigmas, sigma0, m):
''' '''
residuum of isotropic Barlat 1991 yield criterion (eq. 2.37) residuum of isotropic Barlat 1991 yield criterion (eq. 2.37)
''' '''
return Barlat1991(sigmas, sigma0, m, 1.0,1.0,1.0,1.0,1.0,1.0) def fun(self, (sigma0, m), ydata, sigmas):
return Barlat1991Basis(sigma0, 1.0,1.0,1.0,1.0,1.0,1.0, m, sigmas)
def jac(self, (sigma0, m), ydata, sigmas):
pass
def Barlat1991aniso(sigmas, sigma0, a,b,c,f,g,h, m): class Barlat1991aniso(object):
''' '''
residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37) residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
''' '''
return Barlat1991(sigmas, sigma0, m, a,b,c,f,g,h) def fun(self, (sigma0, a,b,c,f,g,h, m), ydata, sigmas):
return Barlat1991Basis(sigma0, a,b,c,f,g,h, m, sigmas)
def jac(self, (sigma0, a,b,c,f,g,h, m), ydata, sigmas):
pass
def Cazacu_Barlat3D(sigma0,a1,a2,a3,a4,a5,a6, b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11, c,
def Barlat1994(sigmas, sigma0, a): ydata, sigmas):
'''
residuum of Hershey yield criterion (eq. 2.104, sigma_e = sigma0)
'''
return None
def Cazacu_Barlat3D(sigmas, sigma0,
a1,a2,a3,a4,a5,a6, b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11, c):
''' '''
residuum of the CazacuBarlat (CZ) yield criterion residuum of the CazacuBarlat (CZ) yield criterion
''' '''
@ -224,14 +195,13 @@ def Cazacu_Barlat3D(sigmas, sigma0,
( 2.0*b10*s33 - b5*s22 - (2*b10-b5)*s11 )*s12**2 + ( 2.0*b10*s33 - b5*s22 - (2*b10-b5)*s11 )*s12**2 +
( (b6+b7)*s11 - b6*s22 - b7*s33 )*s23**2 ( (b6+b7)*s11 - b6*s22 - b7*s33 )*s23**2
)/3.0 )/3.0
f0 = (J20**3 - c*J30**2)**(1.0/6.0) f0 = (J20**3 - c*J30**2)**(1.0/6.0)
k2 = (sigma0/3.0) *18.0 **(1.0/6.0) k2 = (sigma0/3.0) *18.0 **(1.0/6.0)
r = f0/k2 - 1.0 r = f0/k2 - 1.0
return r.ravel() return r.ravel()
def Cazacu_Barlat2D(sigmas, sigma0, def Cazacu_Barlat2D(sigma0,a1,a2,a3,a6, b1,b2,b3,b4,b5,b10, c,
a1,a2,a3,a6, b1,b2,b3,b4,b5,b10, c): ydata, sigmas):
''' '''
residuum of the CazacuBarlat (CZ) yield criterion for plain stress residuum of the CazacuBarlat (CZ) yield criterion for plain stress
''' '''
@ -242,13 +212,12 @@ def Cazacu_Barlat2D(sigmas, sigma0,
J30 = ( (b1 + b2 )*s11**3 + (b3 +b4 )*s22**3 )/27.0- \ J30 = ( (b1 + b2 )*s11**3 + (b3 +b4 )*s22**3 )/27.0- \
( (b1*s11 + b4*s22)*s11*s22 )/9.0 + \ ( (b1*s11 + b4*s22)*s11*s22 )/9.0 + \
( b5*s22 + (2*b10-b5)*s11 )*s12**2/3.0 ( b5*s22 + (2*b10-b5)*s11 )*s12**2/3.0
f0 = (J20**3 - c*J30**2)**(1.0/6.0) f0 = (J20**3 - c*J30**2)**(1.0/6.0)
k2 = (sigma0/3.0) *18.0 **(1.0/6.0) k2 = (sigma0/3.0) *18.0 **(1.0/6.0)
r = f0/k2 - 1.0 r = f0/k2 - 1.0
return r.ravel() return r.ravel()
def BBC2003(sigmas, sigma0, a,b,c, d,e,f,g, k): def BBC2003(sigma0, a,b,c, d,e,f,g, k, ydata, sigmas):
''' '''
residuum of the BBC2003 yield criterion for plain stress residuum of the BBC2003 yield criterion for plain stress
''' '''
@ -263,6 +232,84 @@ def BBC2003(sigmas, sigma0, a,b,c, d,e,f,g, k):
r = sBar/sigma0 - 1.0 r = sBar/sigma0 - 1.0
return r.ravel() return r.ravel()
def DruckerBasis(sigma0, C_D, p, ydata, sigmas):
s11 = sigmas[0]; s22 = sigmas[1]; s33 = sigmas[2]
s12 = sigmas[3]; s23 = sigmas[4]; s31 = sigmas[5]
I1 = s11 + s22 + s33
I2 = s11*s22 + s22*s33 + s11*s33 - s12**2 - s23**2 - s31**2
I3 = s11*s22*s33 + 2.0*s12*s23*s31 - s12**2*s33 - s23**2*s11 - s31**2*s22
J2 = I1**2/3.0 - I2
J3 = I1**3/13.5 - I1*I2/3.0 + I3
r = (J2**(3.0*p) - C_D*J3**(2.0*p))*27/(sigma0**6.0) - 1.0
return r.ravel()
def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
'''
residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
'''
lambdas = principalStresses(sigmas)
diff23 = abs(lambdas[1,:] - lambdas[2,:])
diff31 = abs(lambdas[2,:] - lambdas[0,:])
diff12 = abs(lambdas[0,:] - lambdas[1,:])
base = F*diff23**a + G*diff31**a + H*diff12**a; expo = 1.0/a
left = base**expo; right = 2.0**expo*sigma0
if not Jac:
if nParas == 1: return (left - right).ravel()
else: return (left/right - 1.0).ravel()
else:
if nParas > 1:
ln = lambda x : np.log(x + 1.0e-32)
dbda = F*ln(diff23)*diff23**a + G*ln(diff31)*diff31**a + H*ln(diff12)*diff12**a
deda = -expo*expo; dldb = expo*left/base; drda = sigma0*(2.0**expo)*ln(2.0)*deda
ones = np.ones(np.shape(sigmas)[1]); jac = []
if nParas == 1: # von Mises
return ones*(-2.0**0.5)
elif nParas == 2: # isotropic Hosford
j1 = ones*(-2.0**expo) # d[]/dsigma0
j2 = dldb*dbda + left*ln(base)*deda - drda # d[]/da
for a,b in zip(j1, j2): jac.append([a,b])
return np.array(jac)
elif nParas == 5: # anisotropic Hosford
j1 = -left/right/sigma0 #ones*(-2.0**expo) # d[]/dsigma0
j2 = dldb*diff23**a/right; j3 = dldb*diff31**a/right; j4 = dldb*diff12**a/right
j5 =(dldb*dbda + left*ln(base)*deda)/right + left*(-right**(-2))*drda # d[]/da
for a,b,c,d,e in zip(j1, j2,j3,j4,j5): jac.append([a,b,c,d,e])
return np.array(jac)
def Barlat1991Basis(sigma0, a, b, c, f, g, h, order, sigmas):
'''
residuum of Barlat 1997 yield criterion
'''
cos = np.cos; pi = np.pi; abs = np.abs
A = a*(sigmas[1] - sigmas[2])
B = b*(sigmas[2] - sigmas[0])
C = c*(sigmas[0] - sigmas[1])
F = f* sigmas[4]
G = g* sigmas[5]
H = h* sigmas[3]
I2 = (F*F + G*G + H*H)/3.0 + ((A-C)**2+(C-B)**2+(B-A)**2)/54.0
I3 = (C-B)*(A-C) * (B-A)/54.0 + F*G*H - \
( (C-B)*F*F + (A-C)*G*G + (B-A)*H*H )/6.0
theta = np.arccos(I3/I2**1.5)
Phi = np.sqrt(3.0*I2)* (
(abs(2.0*cos((2.0*theta + pi)/6.0)))**order +
(abs(2.0*cos((2.0*theta + pi*3.0)/6.0)))**order +
(abs(2.0*cos(( 2.0*theta + pi*5.0)/6.0)))**order
)**(1.0/order)
# r = Phi/2.0**(1.0/order) - sigma0
r = Phi/2.0**(1.0/order)/sigma0 - 1.0
# Phi = (3.0*I2)**(order/2.0) * (
# (abs(2.0*cos((2.0*theta + pi)/6.0))) **order +
# (abs(2.0*cos((2.0*theta + pi*3.0)/6.0)))**order +
# (abs(2.0*cos((2.0*theta + pi*5.0)/6.0)))**order
# )
# r = (Phi - 2.0*sigma0**order)**(1.0/order)
return r.ravel()
fittingCriteria = { fittingCriteria = {
'tresca' :{'func' : Tresca, 'tresca' :{'func' : Tresca,
'num' : 1,'err':np.inf, 'num' : 1,'err':np.inf,
@ -278,18 +325,25 @@ fittingCriteria = {
'text' : '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: ', 'text' : '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: ',
'error': 'The standard deviation error is: ' 'error': 'The standard deviation error is: '
}, },
'hosford' :{'func' : Hosford, 'hosfordiso' :{'func' : Hosford,
'num' : 2,'err':np.inf, 'num' : 2,'err':np.inf,
'name' : 'Gerenal Hosford', 'name' : 'Gerenal isotropic Hosford',
'paras': 'Initial yield stress:', 'paras': 'Initial yield stress, a:',
'text' : '\nCoefficients of Hosford criterion:\nsigma0, a: ', 'text' : '\nCoefficients of Hosford criterion:\nsigma0, a: ',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'hosfordaniso' :{'func' : generalHosford,
'num' : 5,'err':np.inf,
'name' : 'Gerenal isotropic Hosford',
'paras': 'Initial yield stress, F, G, H, a:',
'text' : '\nCoefficients of Hosford criterion:\nsigma0, F, G, H, a: ',
'error': 'The standard deviation errors are: '
},
'hill1948' :{'func' : Hill1948, 'hill1948' :{'func' : Hill1948,
'num' : 6,'err':np.inf, 'num' : 6,'err':np.inf,
'name' : 'Hill1948', 'name' : 'Hill1948',
'paras': 'Normalized [F, G, H, L, M, N]', 'paras': 'Normalized [F, G, H, L, M, N]:',
'text' : '\nCoefficients of Hill1948 criterion:\n[F, G, H, L, M, N]:', 'text' : '\nCoefficients of Hill1948 criterion:\n[F, G, H, L, M, N]:'+' '*16,
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'drucker' :{'func' : Drucker, 'drucker' :{'func' : Drucker,
@ -299,6 +353,13 @@ fittingCriteria = {
'text' : '\nCoefficients of Drucker criterion:\nsigma0, C_D: ', 'text' : '\nCoefficients of Drucker criterion:\nsigma0, C_D: ',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'gdrucker' :{'func' : generalDrucker,
'num' : 3,'err':np.inf,
'name' : 'General Drucker',
'paras': 'Initial yield stress, C_D, p:',
'text' : '\nCoefficients of Drucker criterion:\nsigma0, C_D, p: ',
'error': 'The standard deviation errors are: '
},
'barlat1991iso' :{'func' : Barlat1991iso, 'barlat1991iso' :{'func' : Barlat1991iso,
'num' : 2,'err':np.inf, 'num' : 2,'err':np.inf,
'name' : 'Barlat1991iso', 'name' : 'Barlat1991iso',
@ -309,20 +370,20 @@ fittingCriteria = {
'barlat1991aniso':{'func' : Barlat1991aniso, 'barlat1991aniso':{'func' : Barlat1991aniso,
'num' : 8,'err':np.inf, 'num' : 8,'err':np.inf,
'name' : 'Barlat1991aniso', 'name' : 'Barlat1991aniso',
'paras': 'Initial yield stress, m, a, b, c, f, g, h:', 'paras': 'Initial yield stress, a, b, c, f, g, h, m:',
'text' : '\nCoefficients of anisotropic Barlat 1991 criterion:\nsigma0, a, b, c, f, g, h, m:\n', 'text' : '\nCoefficients of anisotropic Barlat 1991 criterion:\nsigma0, a, b, c, f, g, h, m:\n',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'bbc2003' :{'func' : BBC2003, 'bbc2003' :{'func' : BBC2003,
'num' : 9,'err':np.inf, 'num' : 9,'err':np.inf,
'name' : 'Barlat1991aniso', 'name' : 'Banabic-Balan-Comsa 2003',
'paras': 'Initial yield stress, a, b, c, d, e, f, g, k:', 'paras': 'Initial yield stress, a, b, c, d, e, f, g, k:',
'text' : '\nCoefficients of anisotropic Barlat 1991 criterion:\nsigma0, a, b, c, d, e, f, g, k:\n', 'text' : '\nCoefficients of anisotropic Barlat 1991 criterion:\nsigma0, a, b, c, d, e, f, g, k:\n',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'Cazacu_Barlat2D':{'func' : Cazacu_Barlat2D, 'Cazacu_Barlat2D':{'func' : Cazacu_Barlat2D,
'num' : 12,'err':np.inf, 'num' : 12,'err':np.inf,
'name' : 'Barlat1991aniso', 'name' : 'Cazacu Barlat for plain stress',
'paras': 'Initial yield stress, a1,a2,a3,a6; b1,b2,b3,b4,b5,b10; c:', 'paras': 'Initial yield stress, a1,a2,a3,a6; b1,b2,b3,b4,b5,b10; c:',
'text' : '\nCoefficients of Cazacu Barlat yield criterion for plane stress: \ 'text' : '\nCoefficients of Cazacu Barlat yield criterion for plane stress: \
\n Y, a1,a2,a3,a6; b1,b2,b3,b4,b5,b10; c:\n', \n Y, a1,a2,a3,a6; b1,b2,b3,b4,b5,b10; c:\n',
@ -330,7 +391,7 @@ fittingCriteria = {
}, },
'Cazacu_Barlat3D':{'func' : Cazacu_Barlat3D, 'Cazacu_Barlat3D':{'func' : Cazacu_Barlat3D,
'num' : 19,'err':np.inf, 'num' : 19,'err':np.inf,
'name' : 'Barlat1991aniso', 'name' : 'Cazacu Barlat',
'paras': 'Initial yield stress, a1,a2,a3,a4,a5,a6; b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11; c:', 'paras': 'Initial yield stress, a1,a2,a3,a4,a5,a6; b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11; c:',
'text' : '\nCoefficients of Cazacu Barlat yield criterion for plane stress: \ 'text' : '\nCoefficients of Cazacu Barlat yield criterion for plane stress: \
\n Y, a1,a2,a3,a4,a5,a6; b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11; c\n', \n Y, a1,a2,a3,a4,a5,a6; b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11; c\n',
@ -403,7 +464,7 @@ class Criterion(object):
global fitResults global fitResults
nameCriterion = self.name.lower() nameCriterion = self.name.lower()
funResidum = fittingCriteria[nameCriterion]['func'] criteriaClass = fittingCriteria[nameCriterion]['func']; criteria = criteriaClass()
numParas = fittingCriteria[nameCriterion]['num'] numParas = fittingCriteria[nameCriterion]['num']
textParas = fittingCriteria[nameCriterion]['text'] + formatOutput(numParas) textParas = fittingCriteria[nameCriterion]['text'] + formatOutput(numParas)
textError = fittingCriteria[nameCriterion]['error']+ formatOutput(numParas,'%-14.8f')+'\n' textError = fittingCriteria[nameCriterion]['error']+ formatOutput(numParas,'%-14.8f')+'\n'
@ -413,14 +474,21 @@ class Criterion(object):
if fitResults == [] : initialguess = guess0 if fitResults == [] : initialguess = guess0
else : initialguess = np.array(fitResults[-1]) else : initialguess = np.array(fitResults[-1])
weight = get_weight(np.shape(stress)[1]) weight = get_weight(np.shape(stress)[1])
ydata = np.zeros(np.shape(stress)[1])
try: try:
popt, pcov = \ popt, pcov, infodict, errmsg, ierr = \
curve_fit_bound(funResidum, stress, np.zeros(np.shape(stress)[1]), leastsqBound (criteria.fun, initialguess, args=(ydata,stress),
initialguess, weight, bounds) bounds=bounds, full_output=True)
if ierr not in [1, 2, 3, 4]: raise RuntimeError("Optimal parameters not found: " + errmsg)
if (len(ydata) > len(initialguess)) and pcov is not None:
s_sq = (criteria.fun(popt, *(ydata,stress))**2).sum()/(len(ydata)-len(initialguess))
pcov = pcov * s_sq
perr = np.sqrt(np.diag(pcov)) perr = np.sqrt(np.diag(pcov))
fitResults.append(popt.tolist()) fitResults.append(popt.tolist())
print (textParas%array2tuple(popt)) print (textParas%array2tuple(popt))
print (textError%array2tuple(perr)) print (textError%array2tuple(perr))
print('Number of function calls =', infodict['nfev'])
except Exception as detail: except Exception as detail:
print detail print detail
pass pass