*! xtfaclm 1.0.0  23jul2026
*! Factor-augmented panel LM unit-root test with two structural breaks
*! Payne-Lee-Islam-Nazlioglu greenhouse-gas convergence test (PANIC + LM breaks)
*! Author: Dr Merwan Roudane  (merwanroudane920@gmail.com)
*! https://github.com/merwanroudane
program xtfaclm, rclass
    version 14.0
    syntax varname [if] [in] , [ Model(string) Kmax(integer 5) ///
        ICfactor(integer 2) Pmax(integer 5) IClag(integer 2) ///
        Trim(real 0.3) Factors(integer -1) ]

    if ("`model'"=="") local model "constant"
    local model = strlower("`model'")
    if !inlist("`model'","constant","trend") {
        di as error "model() must be constant (level breaks) or trend (level & trend breaks)"
        exit 198
    }
    local mnum = cond("`model'"=="constant",1,2)
    if `iclag'<1 | `iclag'>3 {
        di as error "iclag() must be 1 (AIC), 2 (BIC) or 3 (t-stat)"
        exit 198
    }

    _xt, treq
    local id "`r(ivar)'"
    local tv "`r(tvar)'"
    marksample touse
    markout `touse' `varlist'
    qui xtset
    if "`r(balanced)'"!="strongly balanced" {
        di as error "xtfaclm requires a strongly balanced panel"
        exit 451
    }
    tempvar tag
    qui by `id': gen byte `tag' = _n==1 if `touse'
    qui count if `tag'
    local N = r(N)
    qui count if `touse'
    local T = r(N)/`N'
    if `T'*`N' != r(N) {
        di as error "panel is not balanced over the estimation sample"
        exit 451
    }
    qui su `tv' if `touse', meanonly
    local tmin = r(min)

    tempname OUT STAT
    mata: _xfl_run("`varlist'","`touse'", `N', `T', `mnum', `kmax', ///
        `icfactor', `pmax', `iclag', `trim', `factors', `tmin', "`OUT'", "`STAT'")

    // ---- display -----------------------------------------------------
    local mlab = cond(`mnum'==1,"level breaks","level & trend breaks")
    di ""
    di as txt "Factor-augmented panel LM unit-root test (two breaks)" ///
        _col(64) "N = " as res %4.0f `N'
    di as txt "H0: unit root (all panels)" _col(64) "T = " as res %4.0f `T'
    di as txt "Deterministics: `mlab'" _col(50) "Common factors = " as res %2.0f `STAT'[1,3]
    di as txt "{hline 66}"
    di as txt "  id" _col(11) "LM" _col(23) "p-value" _col(36) "break1" _col(48) "break2" _col(60) "lag"
    di as txt "{hline 66}"
    forvalues i = 1/`N' {
        di as res %4.0f `OUT'[`i',1] _col(7) %9.3f `OUT'[`i',2] ///
            _col(21) %8.3f `OUT'[`i',3] _col(34) %6.0f `OUT'[`i',4] ///
            _col(46) %6.0f `OUT'[`i',5] _col(59) %4.0f `OUT'[`i',6]
    }
    di as txt "{hline 66}"
    di as txt "Panel Fisher P (chi2, " as res %3.0f 2*`N' as txt " df)" ///
        _col(40) as res %9.3f `STAT'[1,1] as txt "   p = " as res %6.3f `STAT'[1,4]
    di as txt "Pm (standardized)" _col(40) as res %9.3f `STAT'[1,2] ///
        as txt "   p = " as res %6.3f `STAT'[1,5]
    di as txt "{hline 66}"
    di as txt "Reject H0 (=> stationary) for a large negative LM / large P/Pm."

    // ---- returns -----------------------------------------------------
    return scalar P    = `STAT'[1,1]
    return scalar Pm   = `STAT'[1,2]
    return scalar P_p  = `STAT'[1,4]
    return scalar Pm_p = `STAT'[1,5]
    return scalar nf   = `STAT'[1,3]
    return matrix units = `OUT'
    return local model  "`model'"
    return local cmd    "xtfaclm"
    return scalar N = `N'
    return scalar T = `T'
end

version 14.0
mata:

real colvector _xfl_lagn(real colvector x, real scalar n)
{
    real scalar r
    r = rows(x)
    if (n > 0) return((J(n,1,0) \ x[1..r-n]))
    if (n < 0) return((x[(1-n)..r] \ J(-n,1,0)))
    return(x)
}
real matrix _xfl_lagnM(real matrix x, real scalar n)
{
    real scalar r
    r = rows(x)
    if (n > 0) return((J(n,cols(x),0) \ x[1..r-n,.]))
    return(x)
}
real colvector _xfl_diff(real colvector x)
{
    real scalar r
    r = rows(x)
    return((0 \ (x[2..r]-x[1..r-1])))
}
real matrix _xfl_diffM(real matrix x)
{
    real scalar r
    r = rows(x)
    return((J(1,cols(x),0) \ (x[2..r,.]-x[1..r-1,.])))
}
void _xfl_ols(real colvector y, real matrix X, real colvector b,
    real colvector e, real scalar sig2, real colvector se, real scalar ssr)
{
    real matrix m
    m = invsym(cross(X,X))
    b = m*cross(X,y)
    e = y - X*b
    ssr = cross(e,e)
    sig2 = ssr/(rows(y)-cols(X))
    se = sqrt(diagonal(m)*sig2)
}
real scalar _xfl_argmin(real colvector v)
{
    real scalar i, im, mn
    mn = v[1]; im = 1; i = 2
    while (i <= rows(v)) {
        if (v[i] < mn) {
            mn = v[i]; im = i
        }
        i = i + 1
    }
    return(im)
}
real matrix _xfl_z2breaks(real scalar model, real scalar t, real scalar tb1, real scalar tb2)
{
    real colvector dt, du1, du2, dt1, dt2
    dt = (1::t)
    du1 = (J(tb1,1,0) \ J(t-tb1,1,1))
    du2 = (J(tb2,1,0) \ J(t-tb2,1,1))
    if (model==1) return((dt, du1, du2))
    dt1 = (J(tb1,1,0) \ (1::(t-tb1)))
    dt2 = (J(tb2,1,0) \ (1::(t-tb2)))
    return((dt, du1, dt1, du2, dt2))
}
real scalar _xfl_getlag(real scalar ic, real scalar pmax,
    real colvector aicp, real colvector sicp, real colvector tstatp)
{
    real scalar p, j
    if (ic==1) return(_xfl_argmin(aicp))
    if (ic==2) return(_xfl_argmin(sicp))
    j = pmax + 1
    p = 1
    while (j >= 1) {
        if (abs(tstatp[j]) > 1.645 | j==1) {
            p = j; j = 0
        }
        else j = j - 1
    }
    return(p)
}
// PCA (Bai-Ng) returning Fhat (T x nf), lambda (n x nf), residual SS-based ehat
void _xfl_pca(real matrix X, real scalar nf, real matrix Fhat, real matrix lambda, real matrix ehat)
{
    real matrix vecs
    real colvector vals, idx
    real scalar t, n
    t = rows(X); n = cols(X)
    if (t < n) {
        symeigensystem(cross(X',X'), vecs, vals)
        idx = _xfl_topidx(vals', nf)
        Fhat = sqrt(t)*vecs[.,idx]
        lambda = cross(X, Fhat)/t
    }
    else {
        symeigensystem(cross(X,X), vecs, vals)
        idx = _xfl_topidx(vals', nf)
        lambda = sqrt(n)*vecs[.,idx]
        Fhat = X*lambda/n
    }
    ehat = X - Fhat*lambda'
}
real colvector _xfl_topidx(real colvector vals, real scalar nf)
{
    real colvector o, top
    o = order(vals, -1)          // descending eigenvalue order
    top = o[1..nf]
    return(top[(nf::1)])         // reverse => ascending among top nf (last = largest), matching GAUSS eighv
}
// Bai-Ng (2002) number of factors; returns ICp2 minimizer (0..kmax)
real scalar _xfl_fnumber(real matrix X, real scalar kmax, real scalar ic)
{
    real matrix Fhat, lambda, ehat
    real colvector V, IC2
    real scalar t, n, sigma2, k, cmin, pen
    t = rows(X); n = cols(X)
    _xfl_pca(X, kmax, Fhat, lambda, ehat)
    sigma2 = sum(ehat:^2)/(n*t)
    V = J(kmax,1,0)
    IC2 = J(kmax+1,1,0)
    cmin = min((n,t))
    k = 1
    while (k <= kmax) {
        _xfl_pca(X, k, Fhat, lambda, ehat)
        V[k] = sum(ehat:^2)/(n*t)
        if (ic==1)      pen = k*sigma2*(n+t)/(n*t)*ln(cmin)
        else if (ic==2) pen = k*(n+t)/(n*t)*ln(cmin)
        else if (ic==3) pen = k*sigma2*(2/n)
        else            pen = k*sigma2*(ln(n)/n)
        if (ic==2) IC2[k+1] = ln(V[k]) + pen
        else       IC2[k+1] = V[k] + pen
        k = k + 1
    }
    // k = 0 term
    if (ic==2) IC2[1] = ln(sum((X:-mean(X)):^2)/(n*t))
    else       IC2[1] = sum((X:-mean(X)):^2)/(n*t)
    return(_xfl_argmin(IC2) - 1)
}
// break search on one differenced series (min SSR), returns tb1,tb2 + residual + fitted
void _xfl_resid_iter1(real colvector dy, real scalar model, real scalar Tf,
    real scalar trim, real scalar tb1o, real scalar tb2o, real colvector resid, real colvector dyh)
{
    real scalar T1, T2, tb1, tb2, ssr, ssrmin, gap
    real matrix z, dz
    real colvector b, w
    T1 = round(trim*Tf)
    T2 = round((1-trim)*Tf)
    ssrmin = 1e18
    tb1o = 0; tb2o = 0
    if (model==1) gap = 2
    else gap = 3
    tb1 = T1
    while (tb1 <= T2) {
        tb2 = tb1 + gap
        while (tb2 <= T2) {
            z = _xfl_z2breaks(model, Tf, tb1, tb2)
            dz = _xfl_diffM(z)[2..rows(z),.]
            b = invsym(cross(dz,dz))*cross(dz,dy)
            w = dy - dz*b
            ssr = cross(w,w)
            if (ssr < ssrmin) {
                ssrmin = ssr
                tb1o = tb1; tb2o = tb2
                resid = w
                dyh = dz*b
            }
            tb2 = tb2 + 1
        }
        tb1 = tb1 + 1
    }
}
// factor-augmented 2-break LM statistic for one series; returns (LM, lag)
real rowvector _xfl_lm2br(real colvector y, real colvector ww, real scalar tb1,
    real scalar tb2, real matrix Fhat, real scalar model, real scalar pmax, real scalar iclag)
{
    real scalar Tf, nobs, p, j, laglm, LM
    real colvector st, s2, ylm, dylm, dep, ls, dyf, taup, aicp, sicp, tstatp, b, e, se
    real scalar sig2, ssr
    real matrix z, dz, F, ds, lmat, x
    Tf = rows(y)
    st = runningsum(ww)
    z = _xfl_z2breaks(model, Tf, tb1, tb2)
    nobs = Tf - 1
    s2 = J(nobs,1,0)
    s2[1..tb1] = st[1..tb1] :/ (tb1/nobs)
    s2[(tb1+1)..tb2] = st[(tb1+1)..tb2] :/ ((tb2-tb1)/nobs)
    s2[(tb2+1)..nobs] = st[(tb2+1)..nobs] :/ ((nobs-tb2)/nobs)
    ylm = s2
    dylm = _xfl_diff(ylm)[2..rows(ylm)]
    lmat = J(rows(dylm), pmax, 0)
    j = 1
    while (j <= pmax) {
        lmat[.,j] = _xfl_lagn(dylm, j)
        j = j + 1
    }
    taup = J(pmax+1,1,0); aicp = J(pmax+1,1,0); sicp = J(pmax+1,1,0); tstatp = J(pmax+1,1,0)
    p = 0
    while (p <= pmax) {
        dep = _xfl_diff(y)[(p+3)..Tf]                          // trimr(diff(y), p+2, 0)
        ls  = _xfl_lagn(ylm,1)[(p+2)..rows(ylm)]               // trimr(lagn(ylm,1), p+1, 0)
        dz  = _xfl_diffM(z)[(p+3)..rows(z),.]                  // trimr(diff(z), p+2, 0)
        F   = Fhat[(p+2)..rows(Fhat),.]                        // trimr(Fhat, p+1, 0)
        ds  = lmat[(p+1)..rows(lmat),.]                        // trimr(lmat, p, 0)
        if (p==0) x = ls, dz, F
        else      x = ls, dz, ds[.,1..p], F
        _xfl_ols(dep, x, b, e, sig2, se, ssr)
        taup[p+1] = b[1]/se[1]
        aicp[p+1] = ln(cross(e,e)/rows(x)) + 2*(cols(x)+2)/rows(x)
        sicp[p+1] = ln(cross(e,e)/rows(x)) + (cols(x)+2)*ln(rows(x))/rows(x)
        tstatp[p+1] = abs(b[cols(x)]/se[cols(x)])
        p = p + 1
    }
    laglm = _xfl_getlag(iclag, pmax, aicp, sicp, tstatp)
    LM = taup[laglm]
    return((LM, laglm-1))
}
real colvector _xfl_pgrid()
{
    return((0.0001 \ 0.0002 \ 0.0005) \ ((1::9)/1000) \ (0.005:*(2::198)) \ ((991::999)/1000) \ (0.9995 \ 0.9998 \ 0.9999))
}
real scalar _xfl_pval(real scalar test, real scalar model, real scalar Targ, real scalar plags)
{
    real matrix params, Xw
    real colvector p, reg, cv, yv, bw, idx
    real scalar it, pt, mn, mx, bands, lower, lo, hi, pval
    if (model==1) params = _xfl_rs0()
    else          params = _xfl_rs2()
    it = 1/Targ
    pt = plags/Targ
    reg = (1 \ it \ it^2 \ pt \ pt^2)
    cv = params*reg
    p = _xfl_pgrid()
    mn = min(cv); mx = max(cv)
    if (test < mn) return(0.0001)
    if (test > mx) return(1)
    bands = 10
    idx = selectindex(cv :<= test)
    lower = max(idx)
    lo = lower - bands + 1
    hi = lower + bands
    if (lower < bands) {
        lo = 1
        hi = bands
    }
    if (lower > rows(cv)-bands) {
        lo = rows(cv) - bands - 1
        hi = rows(cv)
    }
    yv = invnormal(p[lo..hi])
    Xw = J(hi-lo+1,1,1) , cv[lo..hi] , cv[lo..hi]:^2 , cv[lo..hi]:^3
    bw = qrsolve(Xw, yv)
    pval = normal((1, test, test^2, test^3) * bw)
    if (pval > 1) pval = 1
    if (pval < 0) pval = 0.0001
    return(pval)
}

// ---------- driver ---------------------------------------------------
void _xfl_run(string scalar vname, string scalar touse, real scalar N,
    real scalar T, real scalar model, real scalar kmax, real scalar icfac,
    real scalar pmax, real scalar iclag, real scalar trim, real scalar forcenf,
    real scalar tmin, string scalar outname, string scalar statname)
{
    real matrix Y, dy, dy_iter, rr, dyh, Fhat, lambda, ehat, de, ww, out
    real colvector v, Ntb1, Ntb2, resid, dyhi, LMv, pv, lags
    real scalar i, nf, ssr1, ssr2, count, tb1, tb2, P, Pm, lag
    real rowvector lm
    v = st_data(., vname, touse)
    Y = colshape(v, T)'                 // T x N
    dy = _xfl_diffM(Y)[2..T,.]          // (T-1) x N
    dy_iter = dy
    Ntb1 = J(N,1,0); Ntb2 = J(N,1,0)
    rr = J(T-1, N, 0); dyh = J(T-1, N, 0)
    ssr1 = (vec(dy)'vec(dy))/(T*N)
    count = 0
    nf = 0
    while (count < 50) {
        count = count + 1
        for (i=1; i<=N; i++) {
            _xfl_resid_iter1(dy_iter[.,i], model, T, trim, tb1, tb2, resid, dyhi)
            rr[.,i] = resid
            dyh[.,i] = dyhi
            Ntb1[i] = tb1
            Ntb2[i] = tb2
        }
        if (forcenf >= 0) nf = forcenf
        else nf = _xfl_fnumber(rr, kmax, icfac)
        if (nf < 1) nf = 1
        _xfl_pca(rr, nf, Fhat, lambda, ehat)
        de = dy_iter - Fhat*lambda'
        ssr2 = (vec(de)'vec(de))/(T*N)
        if (abs(ssr1-ssr2) > 0.001) {
            ssr1 = ssr2
            dy_iter = dy - Fhat*lambda'
        }
        else count = 100
    }
    ww = dy - dyh - Fhat*lambda'
    LMv = J(N,1,0); pv = J(N,1,0); lags = J(N,1,0)
    for (i=1; i<=N; i++) {
        lm = _xfl_lm2br(Y[.,i], ww[.,i], Ntb1[i], Ntb2[i], Fhat, model, pmax, iclag)
        LMv[i] = lm[1]
        lag = lm[2]
        lags[i] = lag
        pv[i] = _xfl_pval(lm[1], model, T-lag, lag)
    }
    P  = -2*sum(ln(pv))
    Pm = (P - 2*N)/sqrt(4*N)
    out = (1::N), LMv, pv, (tmin :+ Ntb1 :- 1), (tmin :+ Ntb2 :- 1), lags
    st_matrix(outname, out)
    st_matrixcolstripe(outname, (J(6,1,""), ("id","LM","pval","break1","break2","lag")'))
    st_matrix(statname, (P, Pm, nf, chi2tail(2*N,P), 1-normal(Pm)))
}

real matrix _xfl_rs0()
{
    string scalar s
    s = "-4.7480 -13.5800 -516.3892 2.8912 -13.0181 -4.6070 -20.4473 -104.8436 4.5926 -22.8536 -4.3706 -11.8003 -234.5157 2.2330 -7.0824 -4.1999 -4.6180 -342.9552 1.6870 -4.7840 -4.0248 -4.9395 -247.3786 1.9143 -5.5037 -3.9186 -5.3078 -190.2720 1.9736 -5.4511 -3.8416 -5.1203 -166.5557 2.0483 -5.5842 -3.7784 -4.5166 -155.7500 1.9520 -4.7384 -3.7212 -3.3371 -175.9123 1.7927 -4.0604 -3.6738 -3.4936 -156.2013 1.7329 -3.6432 -3.6380 -3.4416 -142.3012 1.8166 -4.0790 -3.6010 -4.0279 -117.0013 1.8725 -4.0154 -3.5693 -4.2716 -106.7147 1.9049 -4.0269 -3.4421 -4.2137 -73.9793 1.9073 -3.8232 -3.3516 -5.1391 -31.1904 2.0167 -3.6326 -3.2764 -3.7086 -53.3277 1.8505 -2.9624 -3.2109 -3.6780 -38.0539 1.7633 -2.4970 -3.1570 -2.8544 -49.0467 1.6488 -1.9284 -3.1106 -2.8440 -39.4280 1.6638 -1.9731 -3.0655 -3.2536 -20.2545 1.6535 -1.8102 -3.0259 -2.9091 -21.8076 1.5933 -1.5253 -2.9895 -2.6281 -19.8104 1.5518 -1.3819 "
    s = s + "-2.9557 -2.3640 -20.9831 1.5110 -1.2258 -2.9244 -2.1624 -21.2208 1.4802 -1.0817 -2.8955 -2.1416 -17.8012 1.4850 -1.1305 -2.8670 -2.1618 -13.6436 1.4428 -0.8800 -2.8406 -1.6025 -25.6782 1.3691 -0.5909 -2.8159 -1.8582 -15.3937 1.3982 -0.6688 -2.7931 -1.6479 -15.8752 1.3846 -0.6675 -2.7704 -1.5426 -17.4257 1.3691 -0.5971 -2.7492 -1.4277 -18.3037 1.3689 -0.6175 -2.7292 -1.4194 -15.2740 1.3655 -0.5777 -2.7087 -1.3511 -14.7567 1.3242 -0.2962 -2.6885 -1.2380 -13.6761 1.2812 -0.1345 -2.6706 -1.1609 -12.5563 1.2935 -0.2213 -2.6530 -1.2045 -9.1052 1.3013 -0.2662 -2.6351 -1.2526 -4.4055 1.2829 -0.1797 -2.6185 -0.9449 -11.5467 1.2481 -0.0356 -2.6027 -0.9228 -10.0815 1.2605 -0.1187 -2.5871 -1.0145 -6.3174 1.2705 -0.1408 -2.5710 -0.6479 -13.9005 1.2196 0.0255 -2.5565 -0.4582 -17.6157 1.2181 -0.0327 -2.5414 -0.4810 -14.6606 1.2119 -0.0112 -2.5274 -0.2927 -18.1240 1.2048 -0.0274 "
    s = s + "-2.5137 -0.2826 -16.9892 1.2155 -0.1184 -2.5001 -0.0032 -23.7296 1.1920 -0.0690 -2.4864 0.1027 -24.5349 1.1727 -0.0282 -2.4731 -0.0344 -19.7656 1.1767 -0.0107 -2.4600 0.0255 -20.7647 1.1596 0.0779 -2.4480 0.1157 -22.2596 1.1640 0.0340 -2.4356 0.0641 -19.1862 1.1693 0.0022 -2.4228 0.2880 -23.5811 1.1380 0.0673 -2.4104 0.3030 -22.7877 1.1237 0.1064 -2.3988 0.2764 -21.9945 1.1357 0.0402 -2.3872 0.3920 -23.9844 1.1261 0.0306 -2.3759 0.4492 -24.5001 1.1173 0.0513 -2.3651 0.3415 -20.5277 1.1394 -0.0460 -2.3540 0.3233 -20.0819 1.1390 -0.0394 -2.3427 0.2425 -17.1561 1.1416 -0.0581 -2.3315 0.2090 -16.1296 1.1392 -0.0600 -2.3212 0.2106 -14.6515 1.1457 -0.1138 -2.3108 0.2716 -15.0091 1.1318 -0.0527 -2.3012 0.2166 -12.9604 1.1504 -0.1309 -2.2906 0.1862 -11.1599 1.1383 -0.0661 -2.2806 0.2156 -10.3990 1.1314 -0.0489 -2.2703 0.2454 -10.0480 1.1187 -0.0178 "
    s = s + "-2.2602 0.3376 -11.9177 1.0959 0.0670 -2.2504 0.4398 -13.5954 1.0813 0.1172 -2.2412 0.4764 -13.5338 1.0841 0.0820 -2.2316 0.4542 -12.3727 1.0855 0.0633 -2.2222 0.4961 -13.0957 1.0810 0.0553 -2.2125 0.6174 -15.1630 1.0589 0.1177 -2.2030 0.6385 -15.2157 1.0505 0.1414 -2.1934 0.6367 -14.6073 1.0376 0.2022 -2.1847 0.7665 -17.3994 1.0278 0.2259 -2.1763 0.5607 -11.9779 1.0604 0.1006 -2.1673 0.5171 -10.4806 1.0639 0.0682 -2.1585 0.5052 -10.0326 1.0594 0.0834 -2.1498 0.5075 -9.6090 1.0568 0.0797 -2.1413 0.4674 -8.2939 1.0599 0.0700 -2.1328 0.4772 -8.3135 1.0549 0.0863 -2.1238 0.4888 -7.6105 1.0418 0.1207 -2.1151 0.4925 -7.2028 1.0328 0.1540 -2.1062 0.4544 -5.7450 1.0248 0.1769 -2.0976 0.4927 -6.6026 1.0089 0.2483 -2.0896 0.4860 -6.1062 1.0140 0.2036 -2.0807 0.4602 -5.1630 0.9969 0.2972 -2.0723 0.4796 -4.8529 0.9883 0.3062 "
    s = s + "-2.0638 0.5389 -5.9806 0.9691 0.3833 -2.0555 0.5018 -4.9203 0.9649 0.4035 -2.0473 0.4427 -2.8996 0.9599 0.4311 -2.0390 0.4601 -2.5485 0.9447 0.5026 -2.0305 0.4572 -1.9009 0.9207 0.6380 -2.0229 0.4075 0.0409 0.9298 0.5770 -2.0152 0.2927 3.4994 0.9410 0.5332 -2.0077 0.3322 2.5033 0.9387 0.5396 -1.9999 0.3653 1.9310 0.9296 0.5638 -1.9923 0.3305 2.6147 0.9367 0.5183 -1.9842 0.3036 3.2255 0.9304 0.5356 -1.9766 0.2366 4.9949 0.9313 0.5445 -1.9694 0.2345 5.4516 0.9375 0.5109 -1.9618 0.2583 4.9873 0.9293 0.5347 -1.9543 0.2398 5.8223 0.9221 0.5725 -1.9468 0.2471 5.4326 0.9218 0.5624 -1.9395 0.1911 7.0207 0.9231 0.5794 -1.9321 0.2067 6.6892 0.9181 0.5949 -1.9244 0.2468 5.7302 0.9054 0.6371 -1.9170 0.2497 5.9343 0.8949 0.6943 -1.9097 0.3415 4.1591 0.8824 0.7302 -1.9025 0.3860 3.2203 0.8794 0.7281 "
    s = s + "-1.8948 0.3851 3.5823 0.8636 0.8065 -1.8876 0.3952 3.3950 0.8637 0.7953 -1.8803 0.4154 3.2214 0.8546 0.8323 -1.8728 0.3864 3.8932 0.8489 0.8604 -1.8657 0.4133 3.2405 0.8432 0.8842 -1.8579 0.4315 3.2905 0.8278 0.9325 -1.8505 0.5012 1.6998 0.8079 1.0245 -1.8433 0.4267 3.6822 0.8187 0.9671 -1.8357 0.4160 4.7241 0.7968 1.0879 -1.8286 0.4314 4.4413 0.7879 1.1344 -1.8213 0.4175 4.8743 0.7801 1.1764 -1.8144 0.3345 6.7385 0.7914 1.1348 -1.8074 0.2543 8.7170 0.7950 1.1497 -1.8003 0.2556 8.8900 0.7863 1.1986 -1.7929 0.2266 10.2908 0.7762 1.2514 -1.7863 0.1590 12.4273 0.7881 1.2032 -1.7786 0.2539 10.5834 0.7586 1.3237 -1.7714 0.3021 9.6936 0.7439 1.3816 -1.7645 0.2464 11.2453 0.7478 1.3699 -1.7573 0.2707 10.4585 0.7382 1.4245 -1.7505 0.2467 10.8929 0.7401 1.4235 -1.7429 0.2731 10.5015 0.7143 1.5651 "
    s = s + "-1.7355 0.2157 11.9964 0.7031 1.6541 -1.7285 0.2519 11.3557 0.6926 1.7125 -1.7211 0.2928 10.3554 0.6762 1.7934 -1.7143 0.3112 10.0280 0.6703 1.8387 -1.7069 0.3241 9.5745 0.6548 1.9230 -1.6995 0.2490 12.0455 0.6476 1.9843 -1.6918 0.2467 12.9717 0.6216 2.1329 -1.6847 0.2667 12.5302 0.6109 2.2062 -1.6777 0.2358 13.3152 0.6067 2.2602 -1.6706 0.2442 12.8176 0.5983 2.3239 -1.6634 0.2368 13.0626 0.5845 2.4379 -1.6560 0.2625 12.7115 0.5620 2.5786 -1.6487 0.2678 12.5253 0.5454 2.6933 -1.6417 0.1256 16.0042 0.5561 2.7091 -1.6343 0.0973 16.9663 0.5394 2.8369 -1.6270 0.0751 17.4088 0.5333 2.8955 -1.6196 0.0840 17.0915 0.5140 3.0389 -1.6125 0.0360 18.1887 0.5055 3.1522 -1.6055 0.0115 18.5589 0.4996 3.2386 -1.5981 0.0522 17.5231 0.4830 3.3540 -1.5912 0.0509 18.1951 0.4814 3.3910 -1.5836 0.0980 17.2981 0.4560 3.5536 "
    s = s + "-1.5764 0.0662 18.0041 0.4506 3.6418 -1.5693 0.0617 17.9816 0.4431 3.7339 -1.5617 0.1038 17.1458 0.4244 3.8698 -1.5544 0.1133 17.1874 0.4181 3.9387 -1.5469 0.0491 19.3391 0.4127 4.0306 -1.5391 -0.0258 21.4267 0.4034 4.1484 -1.5317 -0.0892 23.2447 0.4013 4.2411 -1.5239 -0.1631 26.0836 0.3944 4.3408 -1.5163 -0.1671 26.2826 0.3814 4.4812 -1.5082 -0.0336 24.0349 0.3369 4.7466 -1.5001 -0.1013 26.2650 0.3252 4.8872 -1.4922 -0.0182 24.4038 0.3012 5.0600 -1.4847 -0.1611 28.0451 0.3094 5.1440 -1.4767 -0.2344 30.3256 0.2982 5.2962 -1.4689 -0.2514 30.5576 0.2889 5.4565 -1.4609 -0.3384 32.7976 0.2813 5.6275 -1.4528 -0.3084 32.2630 0.2645 5.8125 -1.4446 -0.2878 31.9584 0.2493 6.0049 -1.4365 -0.3798 34.1851 0.2531 6.1250 -1.4280 -0.5020 37.8824 0.2441 6.3255 -1.4198 -0.4795 37.8064 0.2402 6.4441 -1.4113 -0.5110 38.1890 0.2292 6.6572 "
    s = s + "-1.4030 -0.4331 36.1139 0.2194 6.8449 -1.3949 -0.4377 36.3808 0.2220 6.9898 -1.3868 -0.5796 40.0462 0.2434 7.0735 -1.3777 -0.6114 40.8015 0.2319 7.3093 -1.3686 -0.5733 40.3613 0.2145 7.5629 -1.3593 -0.5493 41.1513 0.1990 7.7810 -1.3501 -0.4449 39.3668 0.1792 8.0556 -1.3410 -0.2529 34.7693 0.1573 8.3544 -1.3314 -0.3040 36.3760 0.1510 8.6301 -1.3224 -0.3072 36.3568 0.1565 8.8643 -1.3134 -0.3470 37.6771 0.1749 9.0379 -1.3035 -0.4308 40.5096 0.1866 9.2703 -1.2924 -0.0831 32.2384 0.1216 9.8288 -1.2824 0.0901 28.0839 0.1163 10.1291 -1.2726 0.0978 29.1098 0.1349 10.3443 -1.2620 0.3197 23.7519 0.1320 10.6379 -1.2513 0.2622 25.2847 0.1644 10.8520 -1.2402 0.3054 24.0160 0.1775 11.2372 -1.2290 0.4443 20.1401 0.1939 11.5882 -1.2174 0.6749 15.4569 0.2061 11.9594 -1.2059 0.5908 18.4121 0.2806 12.0747 -1.1941 0.5515 21.6586 0.3529 12.2637 "
    s = s + "-1.1821 0.7875 17.6461 0.4176 12.4304 -1.1697 0.5790 24.6577 0.5444 12.5003 -1.1566 0.6705 22.9357 0.6417 12.7339 -1.1438 0.7756 21.7849 0.8010 12.6731 -1.1294 0.5411 29.1098 1.0024 12.6127 -1.1128 0.8081 21.6646 1.1510 12.9273 -1.0951 0.6915 26.6991 1.4021 12.8963 -1.0761 0.9444 19.5538 1.7221 12.5316 -1.0533 0.9488 15.5398 2.0728 12.6923 -1.0328 0.2320 30.8127 2.8602 10.9959 -0.9997 0.7523 22.1086 3.6956 9.3038 -0.9909 0.8603 18.1032 3.9025 8.9544 -0.9802 0.2961 33.0367 4.1538 8.6278 -0.9730 0.1076 39.4868 4.5577 7.4534 -0.9588 1.2720 11.3819 4.6800 7.6250 -0.9453 1.1262 16.5054 5.0767 6.8156 -0.9314 0.1292 30.5393 5.7117 5.4351 -0.9144 -1.1824 64.2963 6.4626 4.2870 -0.8841 -0.5241 47.9358 7.1943 2.8545 -0.8337 -3.7815 154.9539 8.6509 0.6656 -0.7981 -9.1527 259.6170 10.9214 -4.3866 -0.7132 -6.0434 178.4443 11.2430 1.2116 "
    s = s + "-0.6619 11.5971 -278.8345 10.1069 7.6559 "
    return(rowshape(strtoreal(tokens(s)), 221))
}
real matrix _xfl_rs1()
{
    string scalar s
    s = "-5.2754 7.2110 -1662.8430 -0.6925 -2.1077 -5.0970 -11.5740 -789.7909 0.7089 -6.2217 -4.9410 -13.8200 -610.0030 2.2940 -11.8215 -4.7917 -14.2836 -479.5526 3.3989 -18.4393 -4.6004 -9.2214 -455.0882 2.5519 -14.7328 -4.4985 -12.3138 -320.2862 2.7870 -14.3399 -4.4435 -8.0965 -388.3604 2.8929 -15.2560 -4.3761 -5.2920 -422.7412 2.4535 -13.2255 -4.3296 -3.4730 -443.6142 2.4242 -13.2894 -4.2852 -3.3679 -429.0076 2.4292 -13.1105 -4.2506 -1.7914 -453.8951 2.3193 -12.7244 -4.2147 -1.5218 -444.4815 2.2579 -12.3354 -4.1828 0.2118 -478.6255 2.0914 -11.6386 -4.0662 0.5427 -430.8607 2.1256 -11.1778 -3.9782 0.7761 -394.7241 2.0940 -10.6830 -3.9077 2.0442 -396.1041 1.9383 -9.8600 -3.8481 1.4916 -357.6679 1.9560 -9.5930 -3.7953 1.5454 -335.7742 1.8635 -8.9247 -3.7492 1.5907 -318.4360 1.8406 -8.7354 -3.7085 1.5513 -304.7592 1.8481 -8.6871 -3.6702 0.8312 -275.2202 1.8954 -8.6509 -3.6370 0.3314 -251.7525 1.9614 -8.8265 "
    s = s + "-3.6049 0.4204 -243.1916 1.9472 -8.7710 -3.5756 -0.2352 -218.8200 1.9894 -8.6711 -3.5483 0.0494 -218.8455 1.9587 -8.5270 -3.5202 0.0193 -210.6589 1.8960 -8.1275 -3.4938 -0.3645 -190.8028 1.8732 -7.9340 -3.4700 -0.3797 -183.4647 1.8307 -7.6481 -3.4465 -0.3953 -176.8427 1.7935 -7.4538 -3.4245 -0.3281 -171.1962 1.7600 -7.2703 -3.4015 -0.1628 -170.5907 1.6742 -6.8540 -3.3822 -0.1181 -168.0315 1.6641 -6.7685 -3.3624 -0.1020 -162.2282 1.6179 -6.4854 -3.3436 -0.1996 -155.4350 1.5868 -6.2319 -3.3243 -0.3752 -146.6076 1.5526 -5.9887 -3.3064 -0.3567 -144.1629 1.5136 -5.7597 -3.2892 -0.4186 -137.3522 1.4760 -5.5095 -3.2730 -0.5573 -130.1832 1.4749 -5.4466 -3.2566 -0.4645 -129.2011 1.4237 -5.1481 -3.2408 -0.5896 -123.1612 1.4031 -4.9698 -3.2258 -0.9868 -110.8281 1.4086 -4.8209 -3.2116 -1.0430 -107.5370 1.3906 -4.6339 -3.1969 -0.9152 -108.4532 1.3581 -4.4772 -3.1831 -1.1449 -99.2914 1.3583 -4.3892 "
    s = s + "-3.1686 -1.2508 -93.1267 1.3254 -4.1756 -3.1557 -1.3553 -87.8660 1.3248 -4.1224 -3.1414 -1.4165 -84.2224 1.2770 -3.8384 -3.1280 -1.6069 -76.8620 1.2612 -3.6795 -3.1147 -1.6162 -74.0907 1.2241 -3.4793 -3.1021 -1.7753 -66.8380 1.2063 -3.3249 -3.0887 -1.7473 -65.2134 1.1536 -3.0479 -3.0770 -1.8720 -60.3293 1.1566 -3.0050 -3.0651 -1.9020 -56.7556 1.1297 -2.8508 -3.0528 -1.8680 -55.1555 1.0821 -2.5947 -3.0414 -1.9833 -50.9068 1.0673 -2.4526 -3.0300 -2.1274 -45.2484 1.0560 -2.3307 -3.0192 -2.2189 -41.7451 1.0418 -2.1617 -3.0075 -2.1690 -41.1243 0.9873 -1.8438 -2.9970 -2.4509 -31.9547 0.9909 -1.7497 -2.9860 -2.4048 -31.3893 0.9444 -1.4821 -2.9753 -2.4300 -29.4683 0.9226 -1.3611 -2.9645 -2.4745 -26.6702 0.8879 -1.1391 -2.9536 -2.5701 -23.3914 0.8623 -0.9545 -2.9432 -2.6210 -20.1684 0.8393 -0.8078 -2.9334 -2.6613 -17.3070 0.8202 -0.6658 -2.9235 -2.6067 -16.8485 0.7871 -0.4865 "
    s = s + "-2.9140 -2.6378 -14.5035 0.7724 -0.3766 -2.9040 -2.6445 -12.9490 0.7421 -0.1965 -2.8941 -2.7820 -8.1257 0.7263 -0.0520 -2.8843 -3.0302 -0.3341 0.7140 0.1122 -2.8751 -3.1210 3.5873 0.6993 0.2421 -2.8665 -3.0659 2.4861 0.6873 0.3353 -2.8570 -3.0907 4.8568 0.6530 0.5571 -2.8481 -3.2828 10.9242 0.6488 0.6577 -2.8396 -3.2833 12.2018 0.6349 0.7666 -2.8304 -3.3201 14.1843 0.6062 0.9701 -2.8215 -3.2722 14.7893 0.5747 1.1380 -2.8125 -3.2809 16.3357 0.5420 1.3448 -2.8037 -3.3665 19.6634 0.5214 1.5091 -2.7952 -3.4233 22.1798 0.5068 1.6283 -2.7863 -3.3837 22.4855 0.4683 1.8484 -2.7779 -3.5120 26.8430 0.4607 1.9393 -2.7692 -3.5897 30.0186 0.4389 2.0861 -2.7607 -3.6563 32.9647 0.4148 2.2528 -2.7526 -3.8033 37.8470 0.4144 2.3105 -2.7446 -3.8732 40.6648 0.4010 2.4288 -2.7362 -3.8032 40.1653 0.3650 2.6332 -2.7280 -3.8044 41.4356 0.3344 2.8356 "
    s = s + "-2.7201 -3.8696 43.6342 0.3223 2.9648 -2.7121 -3.7967 42.8022 0.2938 3.1242 -2.7041 -3.7044 41.3956 0.2596 3.3145 -2.6960 -3.7828 44.5922 0.2337 3.5229 -2.6878 -3.8070 46.0546 0.2037 3.7304 -2.6799 -3.9069 49.4708 0.1845 3.8881 -2.6718 -3.9037 50.2285 0.1505 4.1229 -2.6636 -4.0117 54.4774 0.1219 4.3364 -2.6558 -4.0675 57.4158 0.1054 4.4435 -2.6478 -4.0677 58.5681 0.0733 4.6484 -2.6400 -4.0589 59.1266 0.0473 4.8134 -2.6323 -4.0934 61.1212 0.0230 4.9893 -2.6248 -4.1044 62.7451 0.0056 5.0933 -2.6170 -3.9911 60.8040 -0.0326 5.3102 -2.6089 -3.9857 62.1649 -0.0735 5.5564 -2.6011 -4.0158 64.3447 -0.1061 5.7692 -2.5936 -3.9842 64.2490 -0.1250 5.8806 -2.5861 -3.9907 65.6565 -0.1541 6.0712 -2.5788 -4.0436 67.8327 -0.1711 6.2207 -2.5716 -4.0929 70.2144 -0.1817 6.3162 -2.5646 -4.1210 71.8893 -0.1957 6.4406 -2.5576 -4.1147 72.3988 -0.2103 6.5618 "
    s = s + "-2.5501 -4.1502 74.1794 -0.2355 6.7445 -2.5432 -4.1199 74.5524 -0.2583 6.9111 -2.5361 -4.0811 74.4342 -0.2792 7.0542 -2.5284 -4.0170 73.5096 -0.3212 7.2974 -2.5214 -4.0627 75.7655 -0.3326 7.4123 -2.5143 -4.1261 78.2579 -0.3482 7.5605 -2.5073 -4.0436 77.4673 -0.3754 7.7184 -2.4998 -3.9226 74.9951 -0.4200 7.9743 -2.4925 -3.9054 75.9976 -0.4530 8.2018 -2.4856 -3.8608 75.4821 -0.4734 8.3477 -2.4787 -3.8713 76.9130 -0.4959 8.5204 -2.4716 -3.8596 77.5856 -0.5230 8.7154 -2.4644 -3.8734 78.3762 -0.5443 8.8917 -2.4569 -3.8994 80.1448 -0.5782 9.1357 -2.4496 -3.9177 81.9607 -0.6099 9.3587 -2.4424 -3.8883 81.9080 -0.6331 9.5130 -2.4351 -3.8090 80.7928 -0.6687 9.7344 -2.4282 -3.8372 82.0874 -0.6812 9.8548 -2.4208 -3.8098 81.8177 -0.7173 10.1066 -2.4136 -3.7879 82.3029 -0.7461 10.3007 -2.4066 -3.7827 83.4433 -0.7671 10.4544 -2.3994 -3.7632 84.3404 -0.7940 10.6227 "
    s = s + "-2.3919 -3.6673 83.3148 -0.8397 10.8878 -2.3852 -3.5899 82.2318 -0.8612 11.0394 -2.3782 -3.5971 83.2357 -0.8836 11.2342 -2.3714 -3.6377 85.0501 -0.8996 11.4055 -2.3641 -3.7987 90.3512 -0.9153 11.5898 -2.3567 -3.8569 92.6491 -0.9346 11.7572 -2.3495 -3.7974 91.7836 -0.9652 11.9636 -2.3422 -3.7502 91.5088 -0.9957 12.1769 -2.3349 -3.7130 91.7170 -1.0270 12.3870 -2.3276 -3.6534 91.2272 -1.0559 12.5792 -2.3207 -3.5981 90.5513 -1.0770 12.7482 -2.3141 -3.4883 88.2279 -1.0930 12.8721 -2.3073 -3.4455 87.6897 -1.1129 13.0525 -2.3007 -3.4871 89.3598 -1.1220 13.2105 -2.2940 -3.5206 91.3605 -1.1299 13.3281 -2.2869 -3.4641 90.7255 -1.1566 13.5325 -2.2804 -3.5454 93.4239 -1.1518 13.6192 -2.2733 -3.4611 92.3762 -1.1797 13.8259 -2.2655 -3.4163 92.4058 -1.2211 14.1114 -2.2582 -3.3493 92.0407 -1.2550 14.3618 -2.2512 -3.2950 91.1294 -1.2730 14.5280 -2.2437 -3.2418 90.6040 -1.3097 14.8212 "
    s = s + "-2.2365 -3.2166 90.7578 -1.3285 15.0069 -2.2292 -3.1065 88.2967 -1.3580 15.2462 -2.2215 -3.2575 93.6449 -1.3750 15.4647 -2.2140 -3.1569 92.1871 -1.4086 15.7232 -2.2068 -3.1695 93.2311 -1.4256 15.9331 -2.1993 -3.0702 91.5529 -1.4596 16.2162 -2.1917 -3.0237 91.3527 -1.4924 16.5023 -2.1837 -2.9862 91.4997 -1.5293 16.7990 -2.1759 -2.9217 91.2299 -1.5564 17.0234 -2.1679 -2.8038 89.2875 -1.6017 17.3676 -2.1604 -2.9283 93.8679 -1.6089 17.5647 -2.1529 -2.8385 91.5962 -1.6343 17.8330 -2.1452 -2.7266 90.1302 -1.6612 18.0709 -2.1376 -2.6740 90.1088 -1.6763 18.2662 -2.1298 -2.6448 91.0289 -1.7010 18.5359 -2.1218 -2.7026 94.4155 -1.7163 18.7675 -2.1136 -2.6535 94.1532 -1.7363 19.0034 -2.1058 -2.6668 96.1733 -1.7440 19.1811 -2.0974 -2.6281 96.0575 -1.7689 19.4814 -2.0893 -2.5913 96.0147 -1.7849 19.7392 -2.0812 -2.4927 94.5548 -1.8052 19.9934 -2.0727 -2.3477 92.4348 -1.8376 20.3074 "
    s = s + "-2.0640 -2.2976 92.4985 -1.8615 20.6047 -2.0559 -2.1154 88.8506 -1.8735 20.7965 -2.0472 -2.0879 89.5965 -1.8866 21.0336 -2.0387 -2.1010 91.1538 -1.8897 21.2561 -2.0301 -2.0179 89.8271 -1.9092 21.5985 -2.0208 -1.9773 90.1491 -1.9271 21.9232 -2.0117 -1.8381 87.3821 -1.9435 22.2012 -2.0025 -1.7710 87.1008 -1.9519 22.4705 -1.9931 -1.5274 83.1971 -1.9872 22.8565 -1.9834 -1.2067 76.7497 -2.0279 23.2822 -1.9739 -1.0738 74.5168 -2.0259 23.5118 -1.9637 -1.0299 75.9511 -2.0442 23.9023 -1.9539 -1.0215 77.9662 -2.0243 24.1087 -1.9440 -0.8738 76.7851 -2.0197 24.3550 -1.9341 -0.7619 74.7871 -1.9995 24.5786 -1.9228 -0.4986 69.4749 -2.0181 24.9923 -1.9112 -0.3136 66.5888 -2.0397 25.4874 -1.8999 -0.3780 68.9372 -2.0151 25.8471 -1.8894 -0.3551 70.8665 -1.9439 25.8609 -1.8778 -0.1455 68.9320 -1.9165 26.1327 -1.8643 0.2178 61.4004 -1.9253 26.5870 -1.8520 0.3048 62.6786 -1.8792 26.8827 "
    s = s + "-1.8391 0.7349 53.1172 -1.8320 27.1551 -1.8250 1.0105 47.0836 -1.7906 27.6190 -1.8112 0.9593 48.6616 -1.6488 27.6356 -1.7960 0.9504 54.7945 -1.5331 27.8507 -1.7777 1.6112 40.2596 -1.5067 28.5605 -1.7603 1.9166 34.4004 -1.3588 28.8918 -1.7390 2.5777 22.0397 -1.2636 29.4081 -1.7172 3.3814 5.7721 -1.0682 29.6786 -1.6914 3.1318 15.3970 -0.6906 29.5731 -1.6617 3.9816 -0.9740 -0.2651 29.5240 -1.6245 4.3933 -9.4463 0.5644 28.3907 -1.6163 3.4388 16.6046 0.8925 27.8271 -1.6067 3.7291 13.1320 1.1265 27.4208 -1.5941 4.4680 3.7627 1.2970 27.4756 -1.5788 4.7374 1.0103 1.5008 27.6780 -1.5632 4.9183 -5.3089 1.8900 27.0933 -1.5514 5.4420 -6.7213 2.5242 25.1076 -1.5289 6.6170 -37.8558 3.0508 24.4513 -1.4925 11.1061 -158.6080 3.4076 24.6803 -1.4562 13.0706 -223.3671 5.4497 18.4647 -1.4016 8.5444 -110.3832 7.2618 16.7480 -1.3110 22.3117 -432.9735 7.0042 21.4237 "
    s = s + "-1.2913 22.4701 -471.7700 8.7821 20.8709 "
    return(rowshape(strtoreal(tokens(s)), 221))
}
real matrix _xfl_rs2()
{
    string scalar s
    s = "-5.7270 41.6983 -3293.1407 -3.2077 -9.1151 -5.6039 31.8242 -2960.0100 -1.8514 -11.2443 -5.4149 22.5055 -2435.4914 -0.3492 -14.6757 -5.2575 15.3948 -1997.1326 0.4798 -17.6309 -5.0975 7.1717 -1554.7771 1.3054 -18.7973 -4.9939 2.3472 -1305.3386 1.6727 -18.7358 -4.9180 1.2838 -1228.8940 1.7423 -18.0489 -4.8509 0.2546 -1151.9057 1.7057 -17.3973 -4.8008 0.8054 -1115.9589 1.5892 -16.5855 -4.7586 -1.3028 -1015.0959 1.7423 -16.5754 -4.7239 -1.7857 -971.2000 1.8324 -16.6668 -4.6897 -0.4049 -974.6230 1.6370 -15.7993 -4.6626 -2.1098 -899.9134 1.8437 -16.3352 -4.5489 -3.2442 -785.2185 1.8664 -14.9105 -4.4576 -4.9260 -673.4229 1.7868 -13.3580 -4.3844 -6.5110 -579.9913 1.7638 -12.5934 -4.3242 -6.2672 -551.0087 1.6226 -11.5127 -4.2718 -6.9501 -501.0003 1.5264 -10.3210 -4.2256 -6.2896 -489.5561 1.3514 -9.3076 -4.1860 -6.1456 -465.0024 1.2828 -8.8084 -4.1489 -6.5923 -431.6951 1.2284 -8.0975 -4.1148 -6.9679 -401.1287 1.1770 -7.4881 "
    s = s + "-4.0825 -6.4319 -398.9056 1.0471 -6.7337 -4.0539 -6.0297 -393.3151 0.9489 -6.1071 -4.0250 -6.0274 -378.5572 0.8577 -5.4692 -3.9991 -6.5428 -348.7627 0.8263 -4.9876 -3.9728 -6.5471 -333.7919 0.7123 -4.2122 -3.9483 -6.7681 -314.1939 0.6274 -3.5079 -3.9252 -7.0951 -294.8959 0.5858 -3.0590 -3.9035 -7.1940 -279.4025 0.5148 -2.4908 -3.8821 -7.1934 -267.7255 0.4212 -1.8470 -3.8620 -7.0415 -261.2550 0.3298 -1.2403 -3.8431 -7.2418 -245.8731 0.2781 -0.7260 -3.8250 -7.5233 -230.1024 0.2452 -0.3091 -3.8068 -7.8359 -211.4690 0.1930 0.2114 -3.7883 -7.7572 -204.4803 0.1002 0.7995 -3.7714 -7.8795 -192.4763 0.0354 1.3558 -3.7547 -8.0042 -180.3179 -0.0246 1.8570 -3.7386 -8.2654 -165.4261 -0.0618 2.2282 -3.7228 -8.4015 -154.6772 -0.1239 2.7359 -3.7075 -8.3352 -149.6299 -0.1963 3.2263 -3.6930 -8.4893 -138.4008 -0.2388 3.6424 -3.6791 -8.5834 -128.9664 -0.2830 4.0463 -3.6653 -8.8423 -115.4081 -0.3118 4.3971 "
    s = s + "-3.6521 -8.8197 -109.8149 -0.3610 4.8142 -3.6380 -8.7999 -103.7263 -0.4252 5.2596 -3.6249 -8.8935 -96.5736 -0.4644 5.6281 -3.6125 -8.6645 -96.7927 -0.5291 6.0501 -3.6007 -8.8087 -87.9904 -0.5367 6.2209 -3.5880 -8.6544 -86.5107 -0.6159 6.7340 -3.5755 -8.8030 -78.0556 -0.6686 7.2038 -3.5633 -8.8999 -71.2712 -0.7175 7.6307 -3.5513 -8.8675 -66.1122 -0.7743 8.0267 -3.5397 -8.8586 -60.9965 -0.8242 8.3861 -3.5283 -8.8474 -56.3842 -0.8764 8.7747 -3.5177 -9.0377 -47.2145 -0.8867 8.9786 -3.5073 -9.0582 -42.5075 -0.9137 9.2343 -3.4964 -8.9346 -41.0094 -0.9752 9.6298 -3.4852 -8.8540 -38.7061 -1.0441 10.0810 -3.4748 -8.8705 -33.6898 -1.0881 10.4301 -3.4642 -8.8418 -29.9781 -1.1468 10.8579 -3.4537 -8.7783 -27.9129 -1.2013 11.2607 -3.4436 -8.6642 -27.4097 -1.2565 11.6390 -3.4331 -8.5959 -24.2670 -1.3213 12.0610 -3.4238 -8.4642 -24.7240 -1.3650 12.3887 -3.4143 -8.5238 -18.8630 -1.4000 12.7047 "
    s = s + "-3.4047 -8.5736 -13.0353 -1.4366 13.0290 -3.3948 -8.6771 -6.2160 -1.4779 13.3811 -3.3853 -8.8517 2.2409 -1.5107 13.7281 -3.3755 -8.9196 7.5674 -1.5639 14.1481 -3.3662 -8.9557 11.7377 -1.6008 14.4572 -3.3570 -8.8644 13.1721 -1.6472 14.7709 -3.3478 -8.8364 16.7525 -1.6969 15.1280 -3.3390 -8.7420 17.9257 -1.7377 15.4225 -3.3294 -8.7277 21.2223 -1.7930 15.8036 -3.3208 -8.7795 26.2475 -1.8264 16.1160 -3.3117 -8.7541 29.2797 -1.8826 16.5254 -3.3023 -8.7741 33.7229 -1.9435 16.9681 -3.2940 -8.7316 36.8209 -1.9818 17.2629 -3.2854 -8.5432 35.4742 -2.0389 17.6179 -3.2768 -8.6959 43.1140 -2.0682 17.9176 -3.2682 -8.7386 47.7956 -2.1104 18.2545 -3.2600 -8.6453 48.6101 -2.1570 18.5741 -3.2522 -8.7246 53.8253 -2.1793 18.8310 -3.2439 -8.8501 60.8112 -2.2053 19.1058 -3.2360 -8.8873 64.6680 -2.2321 19.3667 -3.2283 -8.7776 64.2079 -2.2679 19.6356 -3.2208 -8.7290 65.6750 -2.3001 19.9165 "
    s = s + "-3.2125 -8.7601 69.4336 -2.3313 20.1672 -3.2050 -8.7178 71.7528 -2.3657 20.4516 -3.1971 -8.5911 71.3807 -2.4171 20.8087 -3.1894 -8.5529 73.6299 -2.4502 21.0575 -3.1815 -8.4872 74.9611 -2.4981 21.4138 -3.1740 -8.2816 72.4846 -2.5516 21.7502 -3.1666 -8.3620 77.3472 -2.5726 22.0031 -3.1592 -8.3174 78.9737 -2.6022 22.2544 -3.1513 -8.2789 81.0928 -2.6473 22.5974 -3.1437 -8.2466 83.2551 -2.6851 22.9008 -3.1359 -8.3468 89.2962 -2.7227 23.2397 -3.1286 -8.2653 90.4004 -2.7616 23.5167 -3.1213 -8.1734 90.9315 -2.7964 23.7822 -3.1139 -8.0733 91.3038 -2.8450 24.1208 -3.1064 -8.1227 95.4765 -2.8758 24.4150 -3.0994 -8.1954 100.3030 -2.8959 24.6573 -3.0922 -8.1614 101.7284 -2.9283 24.9302 -3.0851 -8.1085 102.7517 -2.9565 25.1629 -3.0780 -8.0355 103.6396 -2.9939 25.4620 -3.0706 -7.9594 104.1918 -3.0389 25.8208 -3.0639 -8.0063 107.3306 -3.0548 26.0570 -3.0566 -7.9904 109.8610 -3.0862 26.3084 "
    s = s + "-3.0499 -7.9187 110.6273 -3.1192 26.5915 -3.0429 -8.0027 115.1595 -3.1455 26.8831 -3.0363 -8.0194 117.7252 -3.1616 27.0998 -3.0295 -7.9896 119.2084 -3.1831 27.3165 -3.0230 -7.9468 120.8897 -3.2045 27.5366 -3.0160 -7.8860 121.2849 -3.2339 27.7894 -3.0091 -7.9284 125.3024 -3.2602 28.0679 -3.0023 -7.8959 126.8340 -3.2935 28.3772 -2.9954 -7.7738 126.1706 -3.3416 28.7621 -2.9887 -7.7446 127.3668 -3.3678 29.0225 -2.9819 -7.8067 130.8870 -3.3841 29.2609 -2.9750 -7.8303 133.6357 -3.4146 29.5693 -2.9678 -7.7801 134.7413 -3.4536 29.8904 -2.9613 -7.6798 135.2154 -3.4802 30.1119 -2.9547 -7.7679 139.9603 -3.4939 30.3485 -2.9480 -7.8926 146.0952 -3.5107 30.6172 -2.9413 -7.8507 146.8252 -3.5359 30.8779 -2.9345 -7.8439 149.0556 -3.5599 31.1270 -2.9279 -7.7916 150.0688 -3.5889 31.3986 -2.9209 -7.7974 153.2120 -3.6228 31.7138 -2.9146 -7.7769 154.8790 -3.6362 31.9201 -2.9078 -7.8627 159.7753 -3.6503 32.1361 "
    s = s + "-2.9013 -7.7889 160.6079 -3.6793 32.4017 -2.8946 -7.7531 162.0350 -3.7101 32.7115 -2.8880 -7.7431 163.6600 -3.7268 32.9367 -2.8816 -7.7869 166.4576 -3.7358 33.1476 -2.8746 -7.7747 168.8140 -3.7644 33.4236 -2.8676 -7.7755 172.0129 -3.7967 33.7498 -2.8611 -7.8043 175.2492 -3.8134 33.9997 -2.8536 -7.8533 178.0928 -3.8562 34.4345 -2.8471 -7.8223 179.6132 -3.8737 34.6635 -2.8402 -7.7744 180.7939 -3.9006 34.9491 -2.8333 -7.7074 181.1315 -3.9292 35.2335 -2.8264 -7.6204 181.2635 -3.9651 35.5736 -2.8196 -7.6553 185.0078 -3.9865 35.8595 -2.8129 -7.7282 190.0674 -3.9979 36.0946 -2.8058 -7.7135 192.7571 -4.0292 36.4051 -2.7991 -7.6917 194.5185 -4.0480 36.6669 -2.7921 -7.8073 200.1838 -4.0636 36.9545 -2.7855 -7.8984 204.7143 -4.0680 37.1787 -2.7780 -7.7562 203.8620 -4.1209 37.6193 -2.7712 -7.8333 208.7472 -4.1334 37.8762 -2.7642 -7.8248 211.2468 -4.1590 38.1951 -2.7571 -7.9103 215.7735 -4.1779 38.5205 "
    s = s + "-2.7501 -7.9479 218.9646 -4.1939 38.8175 -2.7429 -7.9674 221.1593 -4.2176 39.1644 -2.7363 -7.9133 222.3580 -4.2243 39.3632 -2.7291 -7.7496 220.9771 -4.2658 39.7355 -2.7220 -7.7089 222.5027 -4.2898 40.0565 -2.7146 -7.6022 222.6042 -4.3276 40.4250 -2.7073 -7.5316 222.9697 -4.3533 40.7560 -2.7001 -7.5455 226.1213 -4.3690 41.0511 -2.6928 -7.5425 228.7902 -4.3904 41.3745 -2.6859 -7.6944 235.0520 -4.3848 41.6259 -2.6785 -7.5944 234.6500 -4.4105 41.9767 -2.6712 -7.6641 239.1850 -4.4160 42.2558 -2.6640 -7.6981 243.0273 -4.4198 42.5114 -2.6561 -7.6402 244.5877 -4.4513 42.9141 -2.6486 -7.7223 249.8322 -4.4542 43.1892 -2.6411 -7.8534 255.4858 -4.4521 43.4693 -2.6336 -7.9545 260.8210 -4.4503 43.7461 -2.6259 -7.9288 262.4254 -4.4657 44.1121 -2.6182 -7.9607 265.6587 -4.4730 44.4407 -2.6102 -7.9385 268.2955 -4.4839 44.7486 -2.6019 -7.8665 269.8494 -4.5086 45.1451 -2.5932 -7.9938 276.4082 -4.5246 45.5722 "
    s = s + "-2.5851 -7.9797 279.7553 -4.5332 45.9013 -2.5764 -7.8212 278.6240 -4.5785 46.4423 -2.5682 -7.7033 278.7523 -4.5948 46.8165 -2.5594 -7.7321 282.9571 -4.6043 47.1916 -2.5505 -7.6807 285.0644 -4.6282 47.6813 -2.5418 -7.9233 295.8316 -4.6060 47.9472 -2.5328 -7.8566 297.2648 -4.6227 48.3893 -2.5242 -7.7640 297.8363 -4.6175 48.7066 -2.5152 -7.7185 299.6504 -4.6218 49.1393 -2.5062 -7.6253 300.2205 -4.6207 49.5083 -2.4962 -7.2584 293.3108 -4.6646 50.0936 -2.4867 -7.0155 291.1868 -4.6707 50.4492 -2.4763 -6.9702 294.3565 -4.6794 50.9278 -2.4665 -7.0076 299.5691 -4.6517 51.2287 -2.4560 -6.9995 302.3880 -4.6283 51.5486 -2.4446 -6.9037 304.0253 -4.6355 52.0574 -2.4341 -6.8111 306.6156 -4.6096 52.4563 -2.4213 -6.2816 296.4995 -4.6713 53.2260 -2.4095 -6.2995 300.4792 -4.6317 53.6055 -2.3979 -5.9465 296.5461 -4.6056 53.9594 -2.3854 -5.7528 294.2529 -4.5650 54.3467 -2.3710 -5.9166 303.8387 -4.5353 54.9223 "
    s = s + "-2.3567 -5.4621 296.5829 -4.5289 55.5385 -2.3425 -5.2441 297.6697 -4.4566 55.8484 -2.3279 -4.9536 294.5805 -4.3894 56.3856 -2.3119 -4.6894 291.8118 -4.3002 56.8379 -2.2951 -4.3841 286.6707 -4.1731 57.1663 -2.2768 -4.5992 300.0658 -3.9835 57.3917 -2.2553 -5.0316 315.5651 -3.7494 57.6700 -2.2342 -5.3645 332.9532 -3.3824 57.3548 -2.2086 -4.7984 327.3976 -3.0155 57.0416 -2.1753 -2.6628 283.1818 -2.7752 57.5722 -2.1324 -2.4825 289.5706 -2.0944 57.0633 -2.1212 -2.1323 286.8887 -1.9642 57.0406 -2.1106 -2.3650 293.9240 -1.6666 56.3293 -2.0975 -2.4984 300.3965 -1.3749 55.8344 -2.0819 -1.5902 280.5769 -1.2086 55.7626 -2.0639 -2.2677 296.2108 -0.8463 55.4947 -2.0488 -2.9841 309.2914 -0.1625 53.7095 -2.0256 -2.1032 284.8919 0.4157 52.5089 -1.9982 -4.1310 321.5029 1.7601 48.9467 -1.9401 -3.2324 269.5321 3.2546 46.0706 -1.8998 -0.7879 172.7536 5.5170 36.7865 -1.8393 -13.5447 503.4799 9.2955 26.7283 "
    s = s + "-1.7956 -9.8992 407.5748 11.1309 19.1155 "
    return(rowshape(strtoreal(tokens(s)), 221))
}

end