Exemplo n.º 1
0
def SCFeqns_test():

    # away from solution
    phi_z = np.linspace(.5, 0, 50)
    chi = 0.1
    chi_s = 0.05
    sigma = .1
    navgsegments = 95.5
    pdi = 1.2
    p_i = SZdist(pdi, navgsegments)
    data = np.array(
        (0.24809791, 0.19944421, 0.16973004, 0.14729207, 0.13005919,
         0.11657451, 0.1058576, 0.09727039, 0.09038682, 0.08490298, 0.08058535,
         0.07724325, 0.07471421, 0.07285592, 0.07154151, 0.07065667, 0.0700978,
         0.06977082, 0.0695903, 0.06947884, 0.0693665, 0.06919036, 0.06889397,
         0.06842689, 0.06774423, 0.06680614, 0.06557737, 0.06402683, 0.0621272,
         0.05985452, 0.05718783, 0.05410874, 0.05060095, 0.0466496, 0.04224034,
         0.037358, 0.03198467, 0.02609722, 0.01966451, 0.01264599, 0.00499654,
         -0.00331032, -0.01221627, -0.02143066, -0.03017221, -0.0368608,
         -0.0390676, -0.03438821, -0.02291077, -0.01063797))
    result = SCFeqns(phi_z, chi, chi_s, sigma, navgsegments, p_i)
    check(result, data, rtol=2e-6)

    # at solution
    phi_z = easy_phi_z.copy()
    data = np.array(
        (-9.29275601e-10, -5.71888092e-11, -6.36132924e-10, -1.55644492e-09,
         -1.13562165e-09, -9.72811443e-10, -1.63384295e-09, -1.18849514e-09,
         -2.89755997e-10, -7.75140563e-10, -1.77285464e-10, 9.26357491e-10,
         1.16035537e-09, 1.35558342e-10, 3.92842869e-10, -1.16742438e-09,
         -1.13646220e-09, 1.31389510e-10, -7.78504983e-10, -1.16462257e-10,
         1.06623532e-09, 1.31860881e-09, 1.29663683e-09, 1.62908659e-10,
         1.10570872e-09, 1.12047058e-09, 2.64739120e-10, -9.46467349e-11,
         -7.11506659e-10, -1.17296972e-09, -1.00791650e-09, -1.64910530e-10,
         -1.19973027e-09, -6.62134042e-10, -6.10394263e-10, -6.62704530e-10,
         -7.97200791e-10, -9.37733571e-10, -1.10498946e-09, -1.24565040e-09,
         -1.41609789e-09, -1.67821481e-09, -2.02143809e-09, -2.48336908e-09,
         -2.98173400e-09, -3.44456770e-09, -3.97225693e-09, -4.38723307e-09,
         -4.62610414e-09, -4.77197874e-09, -4.73971093e-09, -4.48724133e-09,
         -4.05071319e-09, -3.46513582e-09, -2.77379160e-09, -2.04575741e-09,
         -1.32608356e-09, -6.40953530e-10, -2.51873555e-11, 5.10576483e-10,
         9.60606714e-10, 1.32984397e-09, 1.62342470e-09, 1.85145266e-09,
         2.02196043e-09, 2.14281230e-09, 2.21889979e-09, 2.25142468e-09,
         2.23470713e-09, -6.43817608e-06, -4.05512658e-06, -2.53549899e-06,
         -1.57360459e-06, -9.69199656e-07, -5.92195683e-07, -3.58754495e-07,
         -2.15266255e-07, -1.27713614e-07, -7.46744540e-08, -4.27578070e-08,
         -2.36527590e-08, -1.22420735e-08, -5.42986558e-09, -1.49766885e-09))
    result = SCFeqns(phi_z, chi, chi_s, sigma, navgsegments, p_i)
    check(result, data)
Exemplo n.º 2
0
def SCFeqns_test():
    
    # away from solution
    phi_z = np.linspace(.5,0,50)
    chi = 0.1
    chi_s = 0.05
    sigma = .1
    navgsegments = 95.5
    pdi = 1.2
    p_i = SZdist(pdi,navgsegments)
    data = np.array((
        0.24810378,  0.19945097,  0.16973725,  0.14729956,  0.13006686,
        0.11658225,  0.10586535,  0.0972781 ,  0.09039446,  0.08491051,
        0.08059274,  0.07725048,  0.07472126,  0.07286278,  0.07154816,
        0.0706631 ,  0.07010401,  0.06977679,  0.06959604,  0.06948434,
        0.06937178,  0.0691954 ,  0.06889878,  0.06843147,  0.06774859,
        0.06681028,  0.06558131,  0.06403057,  0.06213075,  0.05985788,
        0.05719101,  0.05411175,  0.05060381,  0.04665231,  0.04224292,
        0.03736045,  0.03198701,  0.02609946,  0.01966667,  0.01264807,
        0.00499856, -0.00330835, -0.01221432, -0.02142874, -0.03017032,
       -0.036859  , -0.03906598, -0.03438694, -0.02291   , -0.01063772))
    result = SCFeqns(phi_z,chi,chi_s,sigma,navgsegments,p_i)
    assert np.allclose(result, data, atol=1e-14)
    
    # at solution
    phi_z = easy_phi_z.copy()
    data = np.array((
         -8.94301463e-08,  -8.54926846e-08,  -8.48261388e-08,
        -8.63975905e-08,  -8.69395192e-08,  -8.93377617e-08,
        -9.09841657e-08,  -9.27694738e-08,  -9.40202529e-08,
        -9.15368746e-08,  -9.14763008e-08,  -9.09397544e-08,
        -8.86826744e-08,  -8.53626849e-08,  -8.09892757e-08,
        -7.78940357e-08,  -7.61289833e-08,  -7.04561874e-08,
        -6.17791860e-08,  -5.30517064e-08,  -5.05450191e-08,
        -6.12440418e-08,  -9.95948151e-08,  -1.80944723e-07,
        -3.13346818e-07,  -4.69662996e-07,  -6.22535829e-07,
        -9.26554049e-07,  -1.82794947e-06,  -3.85118182e-06,
        -5.49741579e-06,  -3.57205254e-06,  -1.60524494e-06,
        -9.99775760e-07,  -9.79287226e-07,  -1.22893515e-06,
        -1.68575805e-06,  -2.29902384e-06,  -3.00785458e-06,
        -3.78510399e-06,  -4.63784940e-06,  -5.56981451e-06,
        -6.57126744e-06,  -7.58582431e-06,  -8.52386576e-06,
        -9.28524056e-06,  -9.77055209e-06,  -9.89221388e-06,
        -9.58838679e-06,  -8.83634616e-06,  -7.66057845e-06,
        -6.13119467e-06,  -4.33559537e-06,  -2.37547500e-06,
        -3.70371253e-07,   1.56485541e-06,   3.34331420e-06,
         4.91516031e-06,   6.26511768e-06,   7.40554630e-06,
         8.36923052e-06,   9.20310167e-06,   9.96358757e-06,
         1.07179875e-05,   1.15255367e-05,   1.23926773e-05,
         1.31522431e-05,   1.31565523e-05))
    result = SCFeqns(phi_z,chi,chi_s,sigma,navgsegments,p_i)
    assert np.allclose(result, data, atol=1e-14)
    
    # check that penalty penalizes
    phi_z[0]=.999
    result = SCFeqns(phi_z,chi,chi_s,sigma,navgsegments,p_i)
    below = np.linalg.norm(result,np.inf)
    phi_z[0] = 1.0
    result = SCFeqns(phi_z,chi,chi_s,sigma,navgsegments,p_i)
    above = np.linalg.norm(result,np.inf)
    assert above > (below + 1e5*(phi_z[0]-.99999))
Exemplo n.º 3
0
def SZdist_test():
    
    # uniform
    pdi=1
    nn=100
    data = np.array(((
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.)))
    assert np.allclose(SZdist(pdi,nn), data, atol=1e-14)
    
    # too narrow
    pdi=1.0000000001
    nn=100
    assert np.allclose(SZdist(pdi,nn), data, atol=1e-14)
         
    # broad
    pdi=2
    nn=30 
    data = np.array(((
          3.22405367e-02,   3.11835662e-02,   3.01612473e-02,
          2.91724440e-02,   2.82160575e-02,   2.72910251e-02,
          2.63963189e-02,   2.55309446e-02,   2.46939407e-02,
          2.38843770e-02,   2.31013540e-02,   2.23440015e-02,
          2.16114780e-02,   2.09029695e-02,   2.02176887e-02,
          1.95548740e-02,   1.89137890e-02,   1.82937212e-02,
          1.76939817e-02,   1.71139040e-02,   1.65528435e-02,
          1.60101767e-02,   1.54853007e-02,   1.49776321e-02,
          1.44866070e-02,   1.40116795e-02,   1.35523220e-02,
          1.31080240e-02,   1.26782919e-02,   1.22626480e-02,
          1.18606306e-02,   1.14717929e-02,   1.10957028e-02,
          1.07319424e-02,   1.03801075e-02,   1.00398071e-02,
          9.71066304e-03,   9.39230964e-03,   9.08439310e-03,
          8.78657127e-03,   8.49851320e-03,   8.21989880e-03,
          7.95041846e-03,   7.68977274e-03,   7.43767200e-03,
          7.19383611e-03,   6.95799411e-03,   6.72988393e-03,
          6.50925209e-03,   6.29585343e-03,   6.08945080e-03,
          5.88981486e-03,   5.69672376e-03,   5.50996294e-03,
          5.32932487e-03,   5.15460882e-03,   4.98562064e-03,
          4.82217255e-03,   4.66408293e-03,   4.51117611e-03,
          4.36328216e-03,   4.22023676e-03,   4.08188094e-03,
          3.94806097e-03,   3.81862813e-03,   3.69343861e-03,
          3.57235329e-03,   3.45523762e-03,   3.34196146e-03,
          3.23239893e-03,   3.12642829e-03,   3.02393178e-03,
          2.92479550e-03,   2.82890930e-03,   2.73616662e-03,
          2.64646441e-03,   2.55970299e-03,   2.47578594e-03,
          2.39462002e-03,   2.31611504e-03,   2.24018376e-03,
          2.16674180e-03,   2.09570755e-03,   2.02700209e-03,
          1.96054905e-03,   1.89627461e-03,   1.83410734e-03,
          1.77397814e-03,   1.71582022e-03,   1.65956895e-03,
          1.60516180e-03,   1.55253834e-03,   1.50164008e-03,
          1.45241046e-03,   1.40479478e-03,   1.35874013e-03,
          1.31419533e-03,   1.27111088e-03,   1.22943891e-03,
          1.18913311e-03,   1.15014869e-03,   1.11244233e-03,
          1.07597213e-03,   1.04069757e-03,   1.00657945e-03,
          9.73579848e-04,   9.41662104e-04,   9.10790748e-04,
          8.80931476e-04,   8.52051107e-04,   8.24117549e-04,
          7.97099762e-04,   7.70967724e-04,   7.45692395e-04,
          7.21245691e-04,   6.97600444e-04,   6.74730382e-04,
          6.52610088e-04,   6.31214985e-04,   6.10521296e-04,
          5.90506027e-04,   5.71146937e-04,   5.52422513e-04,
          5.34311949e-04,   5.16795120e-04,   4.99852561e-04,
          4.83465445e-04,   4.67615562e-04,   4.52285300e-04,
          4.37457625e-04,   4.23116058e-04,   4.09244663e-04,
          3.95828028e-04,   3.82851241e-04,   3.70299885e-04,
          3.58160010e-04,   3.46418129e-04,   3.35061191e-04,
          3.24076579e-04,   3.13452085e-04,   3.03175903e-04,
          2.93236615e-04,   2.83623175e-04,   2.74324902e-04,
          2.65331462e-04,   2.56632862e-04,   2.48219436e-04,
          2.40081835e-04,   2.32211016e-04,   2.24598233e-04,
          2.17235027e-04,   2.10113216e-04,   2.03224886e-04,
          1.96562381e-04,   1.90118300e-04,   1.83885481e-04,
          1.77856998e-04,   1.72026152e-04,   1.66386464e-04,
          1.60931666e-04,   1.55655699e-04,   1.50552698e-04,
          1.45616994e-04,   1.40843101e-04,   1.36225715e-04,
          1.31759704e-04,   1.27440108e-04,   1.23262124e-04,
          1.19221111e-04,   1.15312578e-04,   1.11532182e-04,
          1.07875722e-04,   1.04339135e-04,   1.00918492e-04,
          9.76099898e-05,   9.44099537e-05,   9.13148273e-05,
          8.83211712e-05,   8.54256588e-05,   8.26250726e-05,
          7.99163005e-05,   7.72963325e-05,   7.47622573e-05,
          7.23112590e-05,   6.99406139e-05,   6.76476879e-05,
          6.54299329e-05,   6.32848845e-05,   6.12101592e-05,
          5.92034515e-05,   5.72625315e-05,   5.53852424e-05,
          5.35694982e-05,   5.18132812e-05,   5.01146398e-05,
          4.84716865e-05,   4.68825956e-05,   4.53456013e-05,
          4.38589956e-05,   4.24211267e-05,   4.10303968e-05,
          3.96852604e-05,   3.83842228e-05,   3.71258383e-05,
          3.59087085e-05,   3.47314810e-05,   3.35928476e-05,
          3.24915431e-05,   3.14263436e-05,   3.03960655e-05,
          2.93995640e-05,   2.84357316e-05,   2.75034974e-05,
          2.66018255e-05,   2.57297140e-05,   2.48861936e-05,
          2.40703271e-05,   2.32812080e-05,   2.25179592e-05,
          2.17797327e-05,   2.10657081e-05,   2.03750920e-05,
          1.97071171e-05,   1.90610409e-05,   1.84361457e-05,
          1.78317369e-05,   1.72471431e-05,   1.66817144e-05,
          1.61348228e-05,   1.56058604e-05,   1.50942394e-05,
          1.45993914e-05,   1.41207664e-05,   1.36578326e-05,
          1.32100756e-05,   1.27769978e-05,   1.23581180e-05,
          1.19529707e-05,   1.15611057e-05,   1.11820876e-05,
          1.08154952e-05,   1.04609211e-05,   1.01179713e-05,
          9.78626472e-06,   9.46543280e-06,   9.15511900e-06,
          8.85497850e-06,   8.56467777e-06,   8.28389424e-06,
          8.01231588e-06,   7.74964092e-06,   7.49557747e-06,
          7.24984321e-06,   7.01216508e-06,   6.78227897e-06,
          6.55992941e-06,   6.34486935e-06,   6.13685979e-06,
          5.93566959e-06,   5.74107520e-06,   5.55286037e-06,
          5.37081595e-06,   5.19473966e-06,   5.02443584e-06,
          4.85971524e-06,   4.70039482e-06,   4.54629755e-06,
          4.39725219e-06,   4.25309311e-06,   4.11366014e-06,
          3.97879832e-06,   3.84835779e-06,   3.72219362e-06,
          3.60016559e-06,   3.48213813e-06,   3.36798006e-06,
          3.25756454e-06,   3.15076887e-06,   3.04747438e-06,
          2.94756629e-06,   2.85093357e-06,   2.75746885e-06,
          2.66706827e-06,   2.57963137e-06,   2.49506100e-06,
          2.41326317e-06,   2.33414699e-06,   2.25762455e-06,
          2.18361081e-06,   2.11202354e-06,   2.04278317e-06,
          1.97581277e-06,   1.91103792e-06,   1.84838665e-06,
          1.78778933e-06,   1.72917862e-06,   1.67248940e-06,
          1.61765868e-06,   1.56462552e-06,   1.51333099e-06,
          1.46371810e-06,   1.41573171e-06,   1.36931851e-06,
          1.32442691e-06,   1.28100703e-06,   1.23901062e-06,
          1.19839102e-06,   1.15910309e-06,   1.12110317e-06,
          1.08434904e-06,   1.04879985e-06,   1.01441610e-06)))
    assert np.allclose(SZdist(pdi,nn), data, atol=1e-14)
Exemplo n.º 4
0
def SZdist_test():

    # uniform
    pdi = 1
    nn = 100
    data = np.array(
        ((0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
          0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.)))
    check(SZdist(pdi, nn), data)

    # too narrow
    pdi = 1.0000000001
    nn = 100
    check(SZdist(pdi, nn), data)

    # broad
    pdi = 2
    nn = 30
    data = np.array(
        (3.27848972e-02, 3.17100805e-02, 3.06705004e-02, 2.96650018e-02,
         2.86924673e-02, 2.77518164e-02, 2.68420036e-02, 2.59620181e-02,
         2.51108819e-02, 2.42876492e-02, 2.34914054e-02, 2.27212655e-02,
         2.19763738e-02, 2.12559026e-02, 2.05590512e-02, 1.98850454e-02,
         1.92331360e-02, 1.86025988e-02, 1.79927331e-02, 1.74028611e-02,
         1.68323275e-02, 1.62804982e-02, 1.57467599e-02, 1.52305197e-02,
         1.47312039e-02, 1.42482576e-02, 1.37811442e-02, 1.33293445e-02,
         1.28923566e-02, 1.24696949e-02, 1.20608897e-02, 1.16654867e-02,
         1.12830465e-02, 1.09131443e-02, 1.05553688e-02, 1.02093227e-02,
         9.87462129e-03, 9.55089270e-03, 9.23777719e-03, 8.93492683e-03,
         8.64200509e-03, 8.35868646e-03, 8.08465612e-03, 7.81960957e-03,
         7.56325228e-03, 7.31529937e-03, 7.07547533e-03, 6.84351366e-03,
         6.61915660e-03, 6.40215483e-03, 6.19226723e-03, 5.98926056e-03,
         5.79290925e-03, 5.60299509e-03, 5.41930706e-03, 5.24164105e-03,
         5.06979961e-03, 4.90359181e-03, 4.74283295e-03, 4.58734439e-03,
         4.43695335e-03, 4.29149272e-03, 4.15080086e-03, 4.01472142e-03,
         3.88310319e-03, 3.75579993e-03, 3.63267016e-03, 3.51357707e-03,
         3.39838831e-03, 3.28697589e-03, 3.17921600e-03, 3.07498890e-03,
         2.97417878e-03, 2.87667360e-03, 2.78236502e-03, 2.69114825e-03,
         2.60292191e-03, 2.51758798e-03, 2.43505163e-03, 2.35522114e-03,
         2.27800781e-03, 2.20332583e-03, 2.13109222e-03, 2.06122670e-03,
         1.99365165e-03, 1.92829198e-03, 1.86507505e-03, 1.80393062e-03,
         1.74479074e-03, 1.68758969e-03, 1.63226392e-03, 1.57875194e-03,
         1.52699430e-03, 1.47693347e-03, 1.42851383e-03, 1.38168158e-03,
         1.33638467e-03, 1.29257277e-03, 1.25019719e-03, 1.20921085e-03,
         1.16956821e-03, 1.13122520e-03, 1.09413923e-03, 1.05826908e-03,
         1.02357489e-03, 9.90018113e-04, 9.57561458e-04, 9.26168860e-04,
         8.95805433e-04, 8.66437437e-04, 8.38032240e-04, 8.10558275e-04,
         7.83985014e-04, 7.58282928e-04, 7.33423457e-04, 7.09378976e-04,
         6.86122767e-04, 6.63628987e-04, 6.41872641e-04, 6.20829553e-04,
         6.00476339e-04, 5.80790383e-04, 5.61749809e-04, 5.43333460e-04,
         5.25520871e-04, 5.08292247e-04, 4.91628445e-04, 4.75510948e-04,
         4.59921844e-04, 4.44843813e-04, 4.30260098e-04, 4.16154494e-04,
         4.02511327e-04, 3.89315436e-04, 3.76552158e-04, 3.64207310e-04,
         3.52267174e-04, 3.40718482e-04, 3.29548402e-04, 3.18744520e-04,
         3.08294832e-04, 2.98187725e-04, 2.88411969e-04, 2.78956700e-04,
         2.69811411e-04, 2.60965941e-04, 2.52410460e-04, 2.44135461e-04,
         2.36131748e-04, 2.28390429e-04, 2.20902900e-04, 2.13660842e-04,
         2.06656206e-04, 1.99881210e-04, 1.93328324e-04, 1.86990268e-04,
         1.80859998e-04, 1.74930702e-04, 1.69195791e-04, 1.63648893e-04,
         1.58283844e-04, 1.53094683e-04, 1.48075642e-04, 1.43221145e-04,
         1.38525797e-04, 1.33984382e-04, 1.29591851e-04, 1.25343325e-04,
         1.21234082e-04, 1.17259556e-04, 1.13415331e-04, 1.09697134e-04,
         1.06100834e-04, 1.02622435e-04, 9.92580712e-05, 9.60040046e-05,
         9.28566190e-05, 8.98124169e-05, 8.68680156e-05, 8.40201433e-05,
         8.12656354e-05, 7.86014310e-05, 7.60245696e-05, 7.35321877e-05,
         7.11215159e-05, 6.87898752e-05, 6.65346749e-05, 6.43534088e-05,
         6.22436531e-05, 6.02030634e-05, 5.82293722e-05, 5.63203864e-05,
         5.44739845e-05, 5.26881148e-05, 5.09607930e-05, 4.92900995e-05,
         4.76741778e-05, 4.61112323e-05, 4.45995263e-05, 4.31373799e-05,
         4.17231684e-05, 4.03553202e-05, 3.90323155e-05, 3.77526840e-05,
         3.65150038e-05, 3.53178996e-05, 3.41600411e-05, 3.30401417e-05,
         3.19569570e-05, 3.09092834e-05, 2.98959565e-05, 2.89158505e-05,
         2.79678762e-05, 2.70509801e-05, 2.61641435e-05, 2.53063809e-05,
         2.44767390e-05, 2.36742961e-05, 2.28981603e-05, 2.21474693e-05,
         2.14213889e-05, 2.07191123e-05, 2.00398590e-05, 1.93828742e-05,
         1.87474280e-05, 1.81328142e-05, 1.75383499e-05, 1.69633744e-05,
         1.64072488e-05, 1.58693552e-05, 1.53490959e-05, 1.48458927e-05,
         1.43591864e-05, 1.38884363e-05, 1.34331192e-05, 1.29927292e-05,
         1.25667768e-05, 1.21547889e-05, 1.17563075e-05, 1.13708899e-05,
         1.09981078e-05, 1.06375469e-05, 1.02888067e-05, 9.95149945e-06,
         9.62525049e-06, 9.30969725e-06, 9.00448907e-06, 8.70928680e-06,
         8.42376242e-06, 8.14759864e-06, 7.88048858e-06, 7.62213544e-06,
         7.37225212e-06, 7.13056094e-06, 6.89679335e-06, 6.67068957e-06,
         6.45199835e-06, 6.24047669e-06, 6.03588953e-06, 5.83800953e-06,
         5.64661681e-06, 5.46149870e-06, 5.28244947e-06, 5.10927018e-06,
         4.94176838e-06, 4.77975794e-06, 4.62305884e-06, 4.47149694e-06,
         4.32490383e-06, 4.18311662e-06, 4.04597775e-06, 3.91333482e-06,
         3.78504044e-06, 3.66095206e-06, 3.54093177e-06, 3.42484622e-06,
         3.31256641e-06, 3.20396756e-06, 3.09892901e-06, 2.99733403e-06,
         2.89906974e-06, 2.80402693e-06, 2.71209999e-06, 2.62318678e-06,
         2.53718848e-06, 2.45400955e-06, 2.37355755e-06, 2.29574308e-06,
         2.22047967e-06, 2.14768368e-06, 2.07727424e-06, 2.00917309e-06,
         1.94330456e-06, 1.87959546e-06, 1.81797499e-06, 1.75837468e-06,
         1.70072830e-06, 1.64497180e-06, 1.59104321e-06, 1.53888260e-06,
         1.48843203e-06, 1.43963543e-06, 1.39243856e-06, 1.34678900e-06,
         1.30263600e-06, 1.25993051e-06, 1.21862508e-06, 1.17867380e-06,
         1.14003227e-06, 1.10265757e-06, 1.06650815e-06, 1.03154386e-06))
    check(SZdist(pdi, nn), data)