def alpha_constrain_err(err, d0, d1, d2, d3, valid=True, debug=False):
N = len(d0)
zeros = np.zeros(N)
d0 = np.min([d0, zeros], axis=0)
del_0 = np.abs(d0*100)
d1 = np.max([d1, zeros], axis=0)
del_1 = np.abs(d1*1000)
d2 = np.min([d2, zeros], axis=0)
del_2 = np.abs(d2*5000)
d3 = np.max([d3, zeros], axis=0)
del_3 = np.abs(d3*500000000)
tot = np.sum(del_0) + np.sum(del_1) + np.sum(del_2) + np.sum(del_3)
if tot == 0 and not valid:
# if not Twu91_check_params(params):
del_0 = 1e5*np.ones(N)
if debug:
tot = np.sum(err) + tot
print(tot, err, del_0, del_1, del_2, del_3)
err += del_0
err += del_1
err += del_2
err += del_3
return err
def alpha_poly_objfc(params, Trs, alphas_over_a, domain=None, debug=False):
try:
coeffs = [i.value for i in params.values()] # already sorted
except:
coeffs = params
v, d1, d2, d3 = horner_and_der3(coeffs, Trs)
err = np.abs(v - alphas_over_a)
valid = poly_check_params(coeffs, domain=domain)
err = alpha_constrain_err(err, v, d1, d2, d3, valid=valid, debug=debug)
return err
def alpha_Twu91_objfc(params, Trs, alphas_over_a, debug=False):
try:
c0, c1, c2 = params['c0'].value, params['c1'].value, params['c2'].value
except:
c0, c1, c2 = params
x0 = c1 - 1.0
x1 = c2*x0
x2 = c1*c2
x3 = Trs**x2
x4 = Trs**x1*np.exp(-c0*(x3 - 1.0))
x5 = c0*x3
x6 = c1*x5
x7 = c2*x4
x8 = x1 - 1.0
x9 = x6*(x2*x5 - x2 + 1.0)
x10 = 3.0*x1
x11 = c2*c2
x12 = 3.0*x2
x13 = c1*c1*x11
Trs_inv = 1.0/Trs
Trs_inv2 = Trs_inv*Trs_inv
Trs_inv3 = Trs_inv*Trs_inv2
alphas_calc = d0 = x4
d1 = x7*(x0 - x6)*Trs_inv
d2 = x7*(x0*x8 - 2.0*x1*x6 + x9)*Trs_inv2
d3 = (-x7*(-x0*(x0*x0*x11 - x10 + 2.0) + x10*x6*x8 - x10*x9
+ x6*(Trs**(2.0*x2)*c0*c0*x13 + x12*x5 - x12 - 3.0*x13*x5 + x13 + 2.0)
)*Trs_inv3)
err = np.abs(alphas_calc - alphas_over_a)
valid = Twu91_check_params(params)
err = alpha_constrain_err(err, d0, d1, d2, d3, valid=valid, debug=debug)
return err
def postproc_lmfit(result):
result.aard = np.abs(result.residual).sum()/result.ndata
return result
