Python Group.init_chi方法代码示例

# 需要导入模块: from larch import Group [as 别名]
# 或者: from larch.Group import init_chi [as 别名]

#.........这里部分代码省略.........
    # pre-load FT window
    ftwin = kout**kweight * ftwindow(kout, xmin=kmin, xmax=kmax,
                                     window=win, dx=dk)
    # calc k-value and initial guess for y-values of spline params
    nspl = max(4, min(128, 2*int(rbkg*(kmax-kmin)/np.pi) + 1))
    spl_y, spl_k, spl_e  = np.zeros(nspl), np.zeros(nspl), np.zeros(nspl)
    for i in range(nspl):
        q  = kmin + i*(kmax-kmin)/(nspl - 1)
        ik = index_nearest(kraw, q)
        i1 = min(len(kraw)-1, ik + 5)
        i2 = max(0, ik - 5)
        spl_k[i] = kraw[ik]
        spl_e[i] = energy[ik+ie0]
        spl_y[i] = (2*mu[ik+ie0] + mu[i1+ie0] + mu[i2+ie0] ) / 4.0

    # get spline represention: knots, coefs, order=3
    # coefs will be varied in fit.
    knots, coefs, order = splrep(spl_k, spl_y)

    # set fit parameters from initial coefficients
    params = Group()
    for i in range(len(coefs)):
        name = FMT_COEF % i
        p = Parameter(coefs[i], name=name, vary=i<len(spl_y))
        p._getval()
        setattr(params, name, p)

    initbkg, initchi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
                                   knots, coefs, order, kout)

    # do fit
    fit = Minimizer(__resid, params, _larch=_larch, toler=1.e-4,
                    fcn_kws = dict(ncoefs=len(coefs), chi_std=chi_std,
                                   knots=knots, order=order,
                                   kraw=kraw[:iemax-ie0+1],
                                   mu=mu[ie0:iemax+1], irbkg=irbkg, kout=kout,
                                   ftwin=ftwin, kweight=kweight,
                                   nfft=nfft, nclamp=nclamp,
                                   clamp_lo=clamp_lo, clamp_hi=clamp_hi))
    fit.leastsq()

    # write final results
    coefs = [getattr(params, FMT_COEF % i) for i in range(len(coefs))]
    bkg, chi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
                           knots, coefs, order, kout)
    obkg = np.copy(mu)
    obkg[ie0:ie0+len(bkg)] = bkg

    # outputs to group
    group = set_xafsGroup(group, _larch=_larch)
    group.bkg  = obkg
    group.chie = (mu-obkg)/edge_step
    group.k    = kout
    group.chi  = chi/edge_step

    # now fill in 'autobk_details' group
    params.init_bkg = np.copy(mu)
    params.init_bkg[ie0:ie0+len(bkg)] = initbkg
    params.init_chi = initchi/edge_step
    params.knots_e  = spl_e
    params.knots_y  = np.array([coefs[i] for i in range(nspl)])
    params.init_knots_y = spl_y
    params.nfev = params.fit_details.nfev
    params.kmin = kmin
    params.kmax = kmax  
    group.autobk_details = params

    # uncertainties in mu0 and chi:  fairly slow!!
    if HAS_UNCERTAIN and calc_uncertainties:
        vbest, vstd = [], []
        for n in fit.var_names:
            par = getattr(params, n)
            vbest.append(par.value)
            vstd.append(par.stderr)
        uvars = uncertainties.correlated_values(vbest, params.covar)
        # uncertainty in bkg (aka mu0)
        # note that much of this is working around
        # limitations in the uncertainty package that make it
        #  1. take an argument list (not array)
        #  2. work on returned scalars (but not arrays)
        #  3. not handle kw args and *args well (so use
        #     of global "index" is important here)
        nkx = iemax-ie0 + 1
        def my_dsplev(*args):
            coefs = np.array(args)
            return splev(kraw[:nkx], [knots, coefs, order])[index]
        fdbkg = uncertainties.wrap(my_dsplev)
        dmu0  = [fdbkg(*uvars).std_dev() for index in range(len(bkg))]
        group.delta_bkg = np.zeros(len(mu))
        group.delta_bkg[ie0:ie0+len(bkg)] = np.array(dmu0)

        # uncertainty in chi (see notes above)
        def my_dchi(*args):
            coefs = np.array(args)
            b,chi = spline_eval(kraw[:nkx], mu[ie0:iemax+1],
                                knots, coefs, order, kout)
            return chi[index]
        fdchi = uncertainties.wrap(my_dchi)
        dchi  = [fdchi(*uvars).std_dev() for index in range(len(kout))]
        group.delta_chi = np.array(dchi)/edge_step

# 需要导入模块: from larch import Group [as 别名]
# 或者: from larch.Group import init_chi [as 别名]

#.........这里部分代码省略.........
    # coefs will be varied in fit.
    knots, coefs, order = splrep(spl_k, spl_y)

    # set fit parameters from initial coefficients
    params = Parameters()
    for i in range(len(coefs)):
        params.add(name = FMT_COEF % i, value=coefs[i], vary=i<len(spl_y))

    initbkg, initchi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
                                   knots, coefs, order, kout)

    # do fit
    result = minimize(__resid, params, method='leastsq',
                      gtol=1.e-5, ftol=1.e-5, xtol=1.e-5, epsfcn=1.e-5,
                      kws = dict(ncoefs=len(coefs), chi_std=chi_std,
                                 knots=knots, order=order,
                                 kraw=kraw[:iemax-ie0+1],
                                 mu=mu[ie0:iemax+1], irbkg=irbkg, kout=kout,
                                 ftwin=ftwin, kweight=kweight,
                                 nfft=nfft, nclamp=nclamp,
                                 clamp_lo=clamp_lo, clamp_hi=clamp_hi))

    # write final results
    coefs = [result.params[FMT_COEF % i].value for i in range(len(coefs))]
    bkg, chi = spline_eval(kraw[:iemax-ie0+1], mu[ie0:iemax+1],
                           knots, coefs, order, kout)
    obkg = np.copy(mu)
    obkg[ie0:ie0+len(bkg)] = bkg

    # outputs to group
    group = set_xafsGroup(group, _larch=_larch)
    group.bkg  = obkg
    group.chie = (mu-obkg)/edge_step
    group.k    = kout
    group.chi  = chi/edge_step
    group.e0   = e0

    # now fill in 'autobk_details' group
    details = Group(params=result.params)

    details.init_bkg = np.copy(mu)
    details.init_bkg[ie0:ie0+len(bkg)] = initbkg
    details.init_chi = initchi/edge_step
    details.knots_e  = spl_e
    details.knots_y  = np.array([coefs[i] for i in range(nspl)])
    details.init_knots_y = spl_y
    details.nfev = result.nfev
    details.kmin = kmin
    details.kmax = kmax
    group.autobk_details = details

    # uncertainties in mu0 and chi: can be fairly slow.
    if calc_uncertainties:
        nchi = len(chi)
        nmue = iemax-ie0 + 1
        redchi = result.redchi
        covar  = result.covar / redchi
        jac_chi = np.zeros(nchi*nspl).reshape((nspl, nchi))
        jac_bkg = np.zeros(nmue*nspl).reshape((nspl, nmue))

        cvals, cerrs = [], []
        for i in range(len(coefs)):
             par = result.params[FMT_COEF % i]
             cvals.append(getattr(par, 'value', 0.0))
             cdel = getattr(par, 'stderr', 0.0)
             if cdel is None:
                 cdel = 0.0
             cerrs.append(cdel/2.0)
        cvals = np.array(cvals)
        cerrs = np.array(cerrs)

        # find derivatives by hand!
        _k = kraw[:nmue]
        _m = mu[ie0:iemax+1]
        for i in range(nspl):
            cval0 = cvals[i]
            cvals[i] = cval0 + cerrs[i]
            bkg1, chi1 = spline_eval(_k, _m, knots, cvals, order, kout)

            cvals[i] = cval0 - cerrs[i]
            bkg2, chi2 = spline_eval(_k, _m, knots, cvals, order, kout)

            cvals[i] = cval0
            jac_chi[i] = (chi1 - chi2) / (2*cerrs[i])
            jac_bkg[i] = (bkg1 - bkg2) / (2*cerrs[i])

        dfchi = np.zeros(nchi)
        dfbkg = np.zeros(nmue)
        for i in range(nspl):
            for j in range(nspl):
                dfchi += jac_chi[i]*jac_chi[j]*covar[i,j]
                dfbkg += jac_bkg[i]*jac_bkg[j]*covar[i,j]

        prob = 0.5*(1.0 + erf(err_sigma/np.sqrt(2.0)))
        dchi = t.ppf(prob, nchi-nspl) * np.sqrt(dfchi*redchi)
        dbkg = t.ppf(prob, nmue-nspl) * np.sqrt(dfbkg*redchi)

        group.delta_chi = dchi
        group.delta_bkg = 0.0*mu
        group.delta_bkg[ie0:ie0+len(dbkg)] = dbkg