How to use the fluids.numerics.numpy.array function in fluids

1.42566, 1.36641, 1.35191, 1.29626, 1.28859, 1.24598, 1.24005, 1.18197, 1.17596, 1.13745, 1.13449, 1.10514, 1.04611, 1.03299, 1.01551, 1.00639,
        0.975508, 0.956979, 0.921361, 0.906001, 0.886645, 0.876509, 0.861323, 0.848885, 0.833067, 0.820018, 0.790987, 0.781353, 0.757982, 0.750599, 0.73523,
        0.72878, 0.715526, 0.703825, 0.690704, 0.69043, 0.671089, 0.670816, 0.658238, 0.65804, 0.642607, 0.638042, 0.628642, 0.616468, 0.611099, 0.59789,
        0.592593, 0.59088, 0.5807, 0.571709, 0.569635, 0.559848, 0.558786, 0.554428, 0.553416, 0.5491, 0.548097, 0.54293, 0.542793, 0.537633, 0.53548,
        0.52734, 0.525928, 0.522325, 0.519436, 0.51244, 0.510312, 0.502563, 0.501212, 0.497646, 0.483767, 0.483639, 0.479479, 0.478991, 0.469919, 0.469457,
        0.465373, 0.464541, 0.457403, 0.456485, 0.452112, 0.44443, 0.443408, 0.435131, 0.434907, 0.419981, 0.418722, 0.415054, 0.414669, 0.405475, 0.405291,
        0.396251, 0.391403, 0.387694, 0.383945, 0.378953, 0.373041, 0.369506, 0.365933, 0.360178, 0.355541, 0.350503, 0.34544, 0.342437, 0.338861, 0.33562,
        0.326088, 0.324262, 0.319875, 0.312702, 0.30994, 0.303633, 0.301961, 0.295857, 0.293384, 0.286801, 0.285051, 0.281193, 0.27962, 0.27688, 0.276192,
        0.269144, 0.269082, 0.261395, 0.260961, 0.256484, 0.249175, 0.248418, 0.244472, 0.240616, 0.237428, 0.23135, 0.228333, 0.226286, 0.219953, 0.21934,
        0.21432, 0.213615, 0.209306, 0.208785, 0.203406, 0.198042, 0.197549, 0.193322, 0.192633, 0.189133, 0.188615, 0.183883, 0.183431, 0.178539, 0.17805,
        0.173635, 0.172752, 0.171298, 0.171157, 0.169685, 0.169823, 0.171289, 0.171462, 0.172926, 0.173289, 0.176258, 0.176871, 0.181386, 0.182469,
        0.186641, 0.188307, 0.193913, 0.196789, 0.199552, 0.199594, 0.201486, 0.2015, 0.203218, 0.203417, 0.203404, 0.203401, 0.204688, 0.205335, 0.205334,
        0.203367, 0.203354, 0.203352, 0.203338, 0.205273, 0.20528, 0.205265, 0.205258, 0.205257, 0.205243, 0.205241, 0.205227, 0.205226, 0.205212, 0.20521,
        0.205196, 0.205195, 0.205181, 0.205179, 0.205165, 0.205164, 0.205146, 0.205146, 0.205132, 0.205131, 0.205117, 0.205115, 0.205101, 0.2051
    ])
    _dP_staggered_Re_2 = np.array([3.3699, 3.25874, 3.1513, 2.97524, 2.87715, 2.78229, 2.60185, 2.504, 2.4214, 2.36801, 2.2862, 2.21078, 2.08731, 2.01849, 1.89955,
        1.82808, 1.76778, 1.68508, 1.61934, 1.56066, 1.50918, 1.4524, 1.33872, 1.28835, 1.23986, 1.19319, 1.14827, 1.10908, 1.07889, 1.06407, 1.03929,
        1.00291, 0.994386, 0.957472, 0.951802, 0.912623, 0.908283, 0.874086, 0.869647, 0.841073, 0.838095, 0.808676, 0.780364, 0.773598, 0.747536, 0.738905,
        0.721828, 0.717582, 0.690875, 0.682216, 0.671254, 0.666188, 0.658464, 0.647496, 0.633663, 0.625691, 0.607864, 0.60192, 0.586506, 0.581184, 0.573473,
        0.57011, 0.559368, 0.551449, 0.543432, 0.543274, 0.533071, 0.532917, 0.522924, 0.522784, 0.512946, 0.509741, 0.503124, 0.493595, 0.490661, 0.483683,
        0.479474, 0.477483, 0.469851, 0.466187, 0.465338, 0.461708, 0.461273, 0.453348, 0.452088, 0.448562, 0.447435, 0.443915, 0.443836, 0.439616, 0.43882,
        0.43577, 0.434512, 0.431212, 0.430014, 0.427098, 0.426293, 0.423332, 0.422987, 0.422964, 0.422929, 0.422883, 0.414874, 0.414451, 0.410887, 0.410884,
        0.410855, 0.410436, 0.40691, 0.406003, 0.40083, 0.393272, 0.392277, 0.385608, 0.385474, 0.374217, 0.373188, 0.36608, 0.365067, 0.355721, 0.355584,
        0.349483, 0.344411, 0.338995, 0.335717, 0.333088, 0.328254, 0.323091, 0.319967, 0.316935, 0.312854, 0.307934, 0.303486, 0.299932, 0.296289,
        0.293462, 0.288427, 0.286092, 0.279681, 0.274029, 0.271776, 0.265638, 0.264031, 0.260457, 0.258987, 0.253176, 0.251647, 0.24824, 0.2468, 0.243526,
        0.242183, 0.237587, 0.237538, 0.231397, 0.230821, 0.224266, 0.218493, 0.217895, 0.215809, 0.213044, 0.210747, 0.20588, 0.202787, 0.200602, 0.196037,
        0.195433, 0.19047, 0.189774, 0.185568, 0.18506, 0.181897, 0.176863, 0.176379, 0.172288, 0.171368, 0.166958, 0.166501, 0.162215, 0.161773, 0.158952,
        0.15869, 0.15501, 0.154182, 0.153022, 0.152707, 0.151364, 0.15124, 0.152546, 0.152684, 0.155311, 0.155668, 0.158336, 0.158692, 0.162743, 0.164018,
        0.169607, 0.171511, 0.177917, 0.179674, 0.181351, 0.181379, 0.184846, 0.184874, 0.188409, 0.188408, 0.188396, 0.18876, 0.190196, 0.190315, 0.190318,
        0.190617, 0.190888, 0.190917, 0.191188, 0.191489, 0.191491, 0.191793, 0.191913, 0.191942, 0.191929, 0.191928, 0.191915, 0.191913, 0.1919, 0.192079,
        0.193733, 0.193731, 0.193718, 0.193717, 0.193703, 0.193702, 0.193686, 0.193686, 0.193673, 0.193861, 0.195522, 0.195521, 0.195508, 0.195506
    ])

0.166121, 0.165455, 0.165443, 0.165435, 0.163918, 0.163771, 0.162422, 0.162121, 0.160642, 0.1605, 0.16361, 0.163528, 0.158824, 0.158823, 0.158779,
        0.158774, 0.15872, 0.158736, 0.160236, 0.160219, 0.158765, 0.15862, 0.158595, 0.158591, 0.15855, 0.158541, 0.158506, 0.158492, 0.158456, 0.158442,
        0.158407, 0.158392, 0.158362, 0.158343, 0.158313, 0.158293, 0.158244, 0.158257, 0.159755, 0.15974, 0.15815, 0.158145, 0.158101, 0.158095, 0.158051,
        0.158046, 0.158002, 0.157996, 0.157952, 0.157947, 0.157898, 0.157898, 0.157849, 0.157848, 0.157799, 0.157799, 0.15775, 0.15775, 0.157696, 0.157695,
        0.157646, 0.157646, 0.157597, 0.157597, 0.157552, 0.157548, 0.157508, 0.157499, 0.157459, 0.157449, 0.15742, 0.157419, 0.157371, 0.15737, 0.157321,
        0.157321, 0.157272, 0.157272, 0.157223, 0.157223, 0.157174, 0.157173, 0.157129, 0.157125
    ])
    _dP_inline_Re_parameters = np.array([_dP_inline_Re_125, _dP_inline_Re_15, _dP_inline_Re_2, _dP_inline_Re_25]).T
    dP_inline_f = RectBivariateSpline(_dP_inline_Res, np.array([1.25, 1.5, 2, 2.5]), _dP_inline_Re_parameters, kx = 3, ky = 3, s = 0.002)
    
    
    _dP_inline_correction_parameters = np.array([0.0661637, 0.0767956, 0.0811521, 0.091014, 0.0965946, 0.102863, 0.114663, 0.117455, 0.132109, 0.135196, 0.152089,
        0.168558, 0.19133, 0.192037, 0.21534, 0.217736, 0.244667, 0.247747, 0.324839, 0.392087, 0.446129, 2.2286, 2.3885, 2.63783, 2.92864, 3.00382,
        4.05259, 4.2551, 4.54434, 4.84314, 5.09577, 5.59171, 5.71411
    ])
    _dP_inline_correction_Re_1000 = np.array([7.53832, 6.86113, 6.54006, 6.09616, 5.93568, 5.34629, 5.0612, 4.9696, 4.55428, 4.48266, 4.13474, 3.85306, 3.53216,
        3.52323, 3.22988, 3.19898, 2.89667, 2.86799, 2.31194, 1.99054, 1.798, 0.557156, 0.529536, 0.491093, 0.453615, 0.444813, 0.351914, 0.339127,
        0.322613, 0.30739, 0.295752, 0.27562, 0.271127
    ])
    _dP_inline_correction_Re_10000 = np.array([6.19059, 5.63447, 5.44146, 5.0612, 4.86597, 4.66786, 4.34453, 4.27598, 3.95623, 3.88747, 3.57369, 3.37337, 3.09718,
        3.08911, 2.83271, 2.81518, 2.63689, 2.61495, 2.18225, 1.92462, 1.76564, 0.603218, 0.575945, 0.534133, 0.499018, 0.491093, 0.401321, 0.388344,
        0.370649, 0.353159, 0.339788, 0.316659, 0.311496
    ])
    _dP_inline_correction_Re_100000 = np.array([4.50727, 4.13004, 3.99851, 3.73838, 3.61014, 3.47942, 3.31256, 3.27702, 3.10877, 3.0728, 2.87638, 2.71515, 2.52473,
        2.52055, 2.39441, 2.38256, 2.23167, 2.21606, 1.89994, 1.70733, 1.58802, 0.668818, 0.644362, 0.610869, 0.577472, 0.569658, 0.484948, 0.4719,
        0.454805, 0.438843, 0.426501, 0.404846, 0.399958
    ])
    _dP_inline_correction_Re_1000000 = np.array([3.14214, 2.9391, 2.8673, 2.72361, 2.64416, 2.56157, 2.46985, 2.45024, 2.36473, 2.34829, 2.22756, 2.1327, 2.02212,
        2.01899, 1.92414, 1.91509, 1.81755, 1.80738, 1.63471, 1.50647, 1.43004, 0.74756, 0.730366, 0.704554, 0.675458, 0.668194, 0.588052, 0.575945,
        0.563366, 0.551447, 0.540255, 0.520396, 0.515871
    ])

def load_Zukauskas_correlations():
    global _Zukauskas_correlations_loaded, dP_staggered_f, dP_staggered_correction, dP_inline_f, dP_inline_correction
    from scipy.interpolate import RectBivariateSpline
    _Zukauskas_correlations_loaded = True

    _dP_staggered_Res = np.array([10, 10.9129, 11.6733, 13.1024, 14.0153, 14.9918, 17.1536, 18.5267, 19.8182, 20.7261, 22.243, 23.7936, 26.7057, 28.5663, 32.2732,
        34.858, 37.2879, 41.0554, 44.4722, 47.8949, 51.2337, 55.3369, 65.1821, 70.4025, 76.0437, 82.1368, 88.7182, 95.1284, 100.553, 103.386, 108.398,
        116.441, 118.455, 127.808, 129.188, 139.389, 140.899, 153.665, 155.444, 167.595, 168.914, 182.793, 197.771, 201.613, 217.768, 223.559, 241.759,
        246.457, 268.516, 278.915, 292.866, 304.208, 322.535, 335.015, 351.772, 366.482, 402.412, 415.414, 451.79, 465.314, 497.559, 512.453, 542.68,
        570.321, 609.312, 610.163, 671.039, 671.953, 731.917, 732.915, 813.886, 839.919, 896.808, 977.69, 1016.19, 1119.14, 1221.31, 1244.48, 1346.07,
        1455.66, 1482.44, 1603.12, 1616.93, 1748.56, 1780.79, 1925.77, 1961.27, 2056.71, 2060.37, 2266.81, 2308.27, 2474.96, 2542.2, 2723.03, 2799.84,
        2996.9, 3053.95, 3274.27, 3363.57, 3606.09, 4001.84, 4005.75, 4367.03, 4411.71, 4809.6, 4854.24, 5297.21, 5346.19, 5777.99, 5836.5, 6184.44,
        6739.62, 6817.15, 7422.65, 7435.62, 8188.61, 8256.81, 9005.89, 9089.79, 9914.09, 9931.42, 10832, 11357.6, 11913.2, 12508.2, 13011.2, 13642.4,
        14309.8, 15024.5, 15759.5, 16387, 17188.6, 18046.5, 18772.3, 19683.7, 20458.2, 22313.4, 22950.8, 24573.9, 26311.7, 27049.2, 28976.2, 29516.6, 31605,
        32505.6, 34805.6, 35453.4, 37961.9, 39045, 39838.4, 40171.7, 43802.4, 43836, 47853, 48253.3, 52629.1, 57429.8, 57958.7, 60823.7, 63808, 66429.9,
        72454.1, 76644.8, 79791.3, 86914.7, 87727.5, 94796.5, 95846.9, 102543, 103393, 112734, 123172, 124193, 134342, 136770, 147946, 149173, 161368,
        162701, 177710, 179183, 193825, 197329, 203406, 205093, 224028, 225878, 246499, 248787, 268891, 271756, 296172, 299307, 323098, 329652, 355768,
        363073, 388139, 399883, 411321, 411637, 453053, 453370, 494224, 499159, 539099, 549766, 593776, 617117, 617548, 679896, 741914, 748826, 816818,
        899347, 899975, 991217, 1029890, 1039630, 1134310, 1145030, 1249310, 1261120, 1375630, 1388740, 1515150, 1529530, 1668760, 1684660, 1837940,
        1855450, 2063320, 2064190, 2251140, 2273460, 2479450, 2502990, 2730830, 2756750
    ])
    _dP_staggered_Re_125 = np.array([23.9929, 22.6513, 21.1808, 19.0604, 17.8231, 16.6661, 14.5725, 13.6264, 12.8644, 12.1931, 11.3569, 10.7219, 9.55649, 8.93611,

#import matplotlib.pyplot as plt
    #dP_staggered_f_zs = np.array([1.25, 1.5, 2, 2.5])
    #low, high = min(_dP_staggered_Res), max(_dP_staggered_Res)
    #xs = np.linspace(low, high, 50000)
    #for i in range(4):
    #    plt.loglog(_dP_staggered_Res, _dP_staggered_Re_parameters.T[i, :], '.')
    #    plt.loglog(xs, dP_staggered_f(xs, dP_staggered_f_zs[i]), '--')
    #plt.show()
    
    
    _dP_staggered_correction_parameters = np.array([0.4387, 0.470647, 0.494366, 0.52085, 0.542787, 0.583019, 0.609319, 0.659047, 0.685413, 0.729582, 0.800982,
        0.84214, 0.892449, 0.947309, 1.00903, 1.07052, 1.16389, 1.22243, 1.26584, 1.32314, 1.37597, 1.40437, 1.45385, 1.51093, 1.55814, 1.61775, 1.68647,
        1.74589, 1.79853, 1.86586, 1.92335, 1.97322, 2.12053, 2.22751, 2.34521, 2.45793, 2.58193, 2.71226, 2.84909, 2.99282, 3.14389, 3.22668, 3.32915,
        3.54351
    ])
    _dP_staggered_correction_Re_100 = np.array([0.996741, 0.996986, 0.997157, 0.997339, 0.997482, 0.997731, 0.997885, 0.998158, 0.998294, 0.998512, 0.998836,
        0.999011, 0.999213, 0.99942, 0.99964, 0.999846, 1.00241, 1.02216, 1.0392, 1.06545, 1.08705, 1.0995, 1.1206, 1.14708, 1.16583, 1.18871, 1.21407,
        1.23518, 1.25628, 1.27868, 1.29996, 1.31593, 1.36025, 1.39055, 1.42224, 1.45114, 1.48144, 1.51175, 1.54205, 1.57235, 1.60267, 1.62032, 1.64208,
        1.68552
    ])
    _dP_staggered_correction_Re_1000 = np.array([1.03576, 1.02714, 1.02111, 1.01712, 1.01206, 1.00798, 1.00547, 1.001, 0.999839, 0.999378, 0.998689, 0.998319,
        0.997891, 0.997451, 0.996985, 0.999249, 1.00245, 1.0135, 1.02415, 1.03618, 1.04682, 1.0534, 1.06478, 1.07524, 1.0836, 1.09539, 1.10811, 1.11825,
        1.12833, 1.13858, 1.1481, 1.15678, 1.17941, 1.19487, 1.21106, 1.22398, 1.24068, 1.25657, 1.27109, 1.28706, 1.30317, 1.31111, 1.3196, 1.33956
    ])
    _dP_staggered_correction_Re_10000 = np.array([1.20211, 1.18293, 1.16951, 1.15527, 1.14308, 1.12148, 1.10821, 1.09069, 1.08213, 1.06633, 1.04824, 1.04041,
        1.03015, 1.02269, 1.01509, 1.00905, 1.00302, 1.00302, 1.00304, 1.00623, 1.00905, 1.0103, 1.01246, 1.01508, 1.01696, 1.01926, 1.0225, 1.02674,
        1.03074, 1.03432, 1.03618, 1.03931, 1.04813, 1.05451, 1.05855, 1.0674, 1.07355, 1.08006, 1.08719, 1.09572, 1.10324, 1.10854, 1.11428, 1.12663
    ])
    _dP_staggered_correction_Re_100000 = np.array([1.45829, 1.42587, 1.40486, 1.38291, 1.36389, 1.32864, 1.30754, 1.27136, 1.25327, 1.22447, 1.18203, 1.15678,
        1.12845, 1.10251, 1.07182, 1.04763, 1.00824, 0.984925, 0.975402, 0.965711, 0.960152, 0.957646, 0.9534, 0.948334, 0.945015, 0.942714, 0.940164,
        0.937857, 0.936683, 0.936683, 0.934823, 0.933668, 0.933668, 0.933668, 0.933668, 0.933668, 0.933668, 0.936683, 0.936683, 0.936683, 0.939698,
        0.939698, 0.939698, 0.939698

])
    _dP_staggered_correction_Re_1000 = np.array([1.03576, 1.02714, 1.02111, 1.01712, 1.01206, 1.00798, 1.00547, 1.001, 0.999839, 0.999378, 0.998689, 0.998319,
        0.997891, 0.997451, 0.996985, 0.999249, 1.00245, 1.0135, 1.02415, 1.03618, 1.04682, 1.0534, 1.06478, 1.07524, 1.0836, 1.09539, 1.10811, 1.11825,
        1.12833, 1.13858, 1.1481, 1.15678, 1.17941, 1.19487, 1.21106, 1.22398, 1.24068, 1.25657, 1.27109, 1.28706, 1.30317, 1.31111, 1.3196, 1.33956
    ])
    _dP_staggered_correction_Re_10000 = np.array([1.20211, 1.18293, 1.16951, 1.15527, 1.14308, 1.12148, 1.10821, 1.09069, 1.08213, 1.06633, 1.04824, 1.04041,
        1.03015, 1.02269, 1.01509, 1.00905, 1.00302, 1.00302, 1.00304, 1.00623, 1.00905, 1.0103, 1.01246, 1.01508, 1.01696, 1.01926, 1.0225, 1.02674,
        1.03074, 1.03432, 1.03618, 1.03931, 1.04813, 1.05451, 1.05855, 1.0674, 1.07355, 1.08006, 1.08719, 1.09572, 1.10324, 1.10854, 1.11428, 1.12663
    ])
    _dP_staggered_correction_Re_100000 = np.array([1.45829, 1.42587, 1.40486, 1.38291, 1.36389, 1.32864, 1.30754, 1.27136, 1.25327, 1.22447, 1.18203, 1.15678,
        1.12845, 1.10251, 1.07182, 1.04763, 1.00824, 0.984925, 0.975402, 0.965711, 0.960152, 0.957646, 0.9534, 0.948334, 0.945015, 0.942714, 0.940164,
        0.937857, 0.936683, 0.936683, 0.934823, 0.933668, 0.933668, 0.933668, 0.933668, 0.933668, 0.933668, 0.936683, 0.936683, 0.936683, 0.939698,
        0.939698, 0.939698, 0.939698
    ])
    _dP_staggered_correction_Re_parameters = np.array([_dP_staggered_correction_Re_100, _dP_staggered_correction_Re_1000, _dP_staggered_correction_Re_10000, _dP_staggered_correction_Re_100000]).T
    dP_staggered_correction = RectBivariateSpline(_dP_staggered_correction_parameters, np.array([1E2, 1E3, 1E4, 1E5]), _dP_staggered_correction_Re_parameters, kx=1, ky=3, s=0.002)
    
    # Maybe good plot - bad around the middle
    #dP_staggered_correction_zs = np.array([1E2, 1E3, 1E4, 1E5])
    #low, high = min(_dP_staggered_correction_parameters), max(_dP_staggered_correction_parameters)
    #xs = np.linspace(low, high, 50000)
    #for i in range(4):
    #    plt.loglog(_dP_staggered_correction_parameters, _dP_staggered_correction_Re_parameters.T[i, :], '.')
    #    plt.loglog(xs, dP_staggered_correction(xs, dP_staggered_correction_zs[i]), '--')
    #plt.show()
    
    
    _dP_inline_Res = np.array([28.5094, 30.8092, 32.9727, 35.3563, 41.2101, 45.9365, 49.1622, 52.6143, 56.3102, 59.107, 63.7533, 68.3605, 73.1607, 82.9896, 91.2679,
        107.829, 116.528, 124.713, 134.774, 144.237, 157.106, 169.784, 183.484, 202.173, 218.488, 241.163, 278.938, 301.447, 325.772, 352.069, 402.667,
        439.431, 479.551, 528.457, 576.706, 600.39, 654.321, 666.665, 722.026, 795.679, 802.401, 883.594, 965.211, 973.774, 1022.26, 1107.38, 1126.59,
        1220.48, 1343.51, 1368.32, 1468.16, 1616.19, 1646.72, 1764.04, 1814.79, 1944.21, 1998.93, 2038.12, 2041.06, 2246.18, 2249.48, 2455.2, 2476.81,
        2705.84, 2729.59, 2982.07, 3008.17, 3257.9, 3313.34, 3590.4, 3618.29, 3946.71, 4030.55, 4063.47, 4434.98, 4446.05, 4852.32, 4895.14, 5347.3,

0.157646, 0.157646, 0.157597, 0.157597, 0.157552, 0.157548, 0.157508, 0.157499, 0.157459, 0.157449, 0.15742, 0.157419, 0.157371, 0.15737, 0.157321,
        0.157321, 0.157272, 0.157272, 0.157223, 0.157223, 0.157174, 0.157173, 0.157129, 0.157125
    ])
    _dP_inline_Re_parameters = np.array([_dP_inline_Re_125, _dP_inline_Re_15, _dP_inline_Re_2, _dP_inline_Re_25]).T
    dP_inline_f = RectBivariateSpline(_dP_inline_Res, np.array([1.25, 1.5, 2, 2.5]), _dP_inline_Re_parameters, kx = 3, ky = 3, s = 0.002)
    
    
    _dP_inline_correction_parameters = np.array([0.0661637, 0.0767956, 0.0811521, 0.091014, 0.0965946, 0.102863, 0.114663, 0.117455, 0.132109, 0.135196, 0.152089,
        0.168558, 0.19133, 0.192037, 0.21534, 0.217736, 0.244667, 0.247747, 0.324839, 0.392087, 0.446129, 2.2286, 2.3885, 2.63783, 2.92864, 3.00382,
        4.05259, 4.2551, 4.54434, 4.84314, 5.09577, 5.59171, 5.71411
    ])
    _dP_inline_correction_Re_1000 = np.array([7.53832, 6.86113, 6.54006, 6.09616, 5.93568, 5.34629, 5.0612, 4.9696, 4.55428, 4.48266, 4.13474, 3.85306, 3.53216,
        3.52323, 3.22988, 3.19898, 2.89667, 2.86799, 2.31194, 1.99054, 1.798, 0.557156, 0.529536, 0.491093, 0.453615, 0.444813, 0.351914, 0.339127,
        0.322613, 0.30739, 0.295752, 0.27562, 0.271127
    ])
    _dP_inline_correction_Re_10000 = np.array([6.19059, 5.63447, 5.44146, 5.0612, 4.86597, 4.66786, 4.34453, 4.27598, 3.95623, 3.88747, 3.57369, 3.37337, 3.09718,
        3.08911, 2.83271, 2.81518, 2.63689, 2.61495, 2.18225, 1.92462, 1.76564, 0.603218, 0.575945, 0.534133, 0.499018, 0.491093, 0.401321, 0.388344,
        0.370649, 0.353159, 0.339788, 0.316659, 0.311496
    ])
    _dP_inline_correction_Re_100000 = np.array([4.50727, 4.13004, 3.99851, 3.73838, 3.61014, 3.47942, 3.31256, 3.27702, 3.10877, 3.0728, 2.87638, 2.71515, 2.52473,
        2.52055, 2.39441, 2.38256, 2.23167, 2.21606, 1.89994, 1.70733, 1.58802, 0.668818, 0.644362, 0.610869, 0.577472, 0.569658, 0.484948, 0.4719,
        0.454805, 0.438843, 0.426501, 0.404846, 0.399958
    ])
    _dP_inline_correction_Re_1000000 = np.array([3.14214, 2.9391, 2.8673, 2.72361, 2.64416, 2.56157, 2.46985, 2.45024, 2.36473, 2.34829, 2.22756, 2.1327, 2.02212,
        2.01899, 1.92414, 1.91509, 1.81755, 1.80738, 1.63471, 1.50647, 1.43004, 0.74756, 0.730366, 0.704554, 0.675458, 0.668194, 0.588052, 0.575945,
        0.563366, 0.551447, 0.540255, 0.520396, 0.515871
    ])
    
    _dP_inline_correction_zs = np.array([1E3, 1E4, 1E5, 1E6])
    _dP_inline_correction_Re_parameters = np.array([_dP_inline_correction_Re_1000, _dP_inline_correction_Re_10000, _dP_inline_correction_Re_100000, _dP_inline_correction_Re_1000000]).T
    dP_inline_correction = RectBivariateSpline(_dP_inline_correction_parameters, _dP_inline_correction_zs, _dP_inline_correction_Re_parameters, kx=1, ky=3, s=0.002) # s=0.002
    # RectBivariateSpline does a terrible job

])
    _dP_staggered_Re_parameters = np.array([_dP_staggered_Re_125, _dP_staggered_Re_15, _dP_staggered_Re_2, _dP_staggered_Re_25]).T
    dP_staggered_f = RectBivariateSpline(_dP_staggered_Res, np.array([1.25, 1.5, 2, 2.5]), _dP_staggered_Re_parameters, kx=3, ky=3, s=0.002)
    
    # Excellent plot, though it does linear extrapolation on some lines
    #import matplotlib.pyplot as plt
    #dP_staggered_f_zs = np.array([1.25, 1.5, 2, 2.5])
    #low, high = min(_dP_staggered_Res), max(_dP_staggered_Res)
    #xs = np.linspace(low, high, 50000)
    #for i in range(4):
    #    plt.loglog(_dP_staggered_Res, _dP_staggered_Re_parameters.T[i, :], '.')
    #    plt.loglog(xs, dP_staggered_f(xs, dP_staggered_f_zs[i]), '--')
    #plt.show()
    
    
    _dP_staggered_correction_parameters = np.array([0.4387, 0.470647, 0.494366, 0.52085, 0.542787, 0.583019, 0.609319, 0.659047, 0.685413, 0.729582, 0.800982,
        0.84214, 0.892449, 0.947309, 1.00903, 1.07052, 1.16389, 1.22243, 1.26584, 1.32314, 1.37597, 1.40437, 1.45385, 1.51093, 1.55814, 1.61775, 1.68647,
        1.74589, 1.79853, 1.86586, 1.92335, 1.97322, 2.12053, 2.22751, 2.34521, 2.45793, 2.58193, 2.71226, 2.84909, 2.99282, 3.14389, 3.22668, 3.32915,
        3.54351
    ])
    _dP_staggered_correction_Re_100 = np.array([0.996741, 0.996986, 0.997157, 0.997339, 0.997482, 0.997731, 0.997885, 0.998158, 0.998294, 0.998512, 0.998836,
        0.999011, 0.999213, 0.99942, 0.99964, 0.999846, 1.00241, 1.02216, 1.0392, 1.06545, 1.08705, 1.0995, 1.1206, 1.14708, 1.16583, 1.18871, 1.21407,
        1.23518, 1.25628, 1.27868, 1.29996, 1.31593, 1.36025, 1.39055, 1.42224, 1.45114, 1.48144, 1.51175, 1.54205, 1.57235, 1.60267, 1.62032, 1.64208,
        1.68552
    ])
    _dP_staggered_correction_Re_1000 = np.array([1.03576, 1.02714, 1.02111, 1.01712, 1.01206, 1.00798, 1.00547, 1.001, 0.999839, 0.999378, 0.998689, 0.998319,
        0.997891, 0.997451, 0.996985, 0.999249, 1.00245, 1.0135, 1.02415, 1.03618, 1.04682, 1.0534, 1.06478, 1.07524, 1.0836, 1.09539, 1.10811, 1.11825,
        1.12833, 1.13858, 1.1481, 1.15678, 1.17941, 1.19487, 1.21106, 1.22398, 1.24068, 1.25657, 1.27109, 1.28706, 1.30317, 1.31111, 1.3196, 1.33956
    ])
    _dP_staggered_correction_Re_10000 = np.array([1.20211, 1.18293, 1.16951, 1.15527, 1.14308, 1.12148, 1.10821, 1.09069, 1.08213, 1.06633, 1.04824, 1.04041,
        1.03015, 1.02269, 1.01509, 1.00905, 1.00302, 1.00302, 1.00304, 1.00623, 1.00905, 1.0103, 1.01246, 1.01508, 1.01696, 1.01926, 1.0225, 1.02674,
        1.03074, 1.03432, 1.03618, 1.03931, 1.04813, 1.05451, 1.05855, 1.0674, 1.07355, 1.08006, 1.08719, 1.09572, 1.10324, 1.10854, 1.11428, 1.12663

def plot(self, pts=100):  # pragma: no cover
        import matplotlib.pyplot as plt

        Zs = np.linspace(0, self.H_outlet, pts)
        Rs = np.array([self.diameter(Z) for Z in Zs])/2
        plt.plot(Zs, Rs)
        plt.plot(Zs, -Rs)
        plt.show()

#    plt.loglog(_dP_staggered_Res, _dP_staggered_Re_parameters.T[i, :], '.')
    #    plt.loglog(xs, dP_staggered_f(xs, dP_staggered_f_zs[i]), '--')
    #plt.show()
    
    
    _dP_staggered_correction_parameters = np.array([0.4387, 0.470647, 0.494366, 0.52085, 0.542787, 0.583019, 0.609319, 0.659047, 0.685413, 0.729582, 0.800982,
        0.84214, 0.892449, 0.947309, 1.00903, 1.07052, 1.16389, 1.22243, 1.26584, 1.32314, 1.37597, 1.40437, 1.45385, 1.51093, 1.55814, 1.61775, 1.68647,
        1.74589, 1.79853, 1.86586, 1.92335, 1.97322, 2.12053, 2.22751, 2.34521, 2.45793, 2.58193, 2.71226, 2.84909, 2.99282, 3.14389, 3.22668, 3.32915,
        3.54351
    ])
    _dP_staggered_correction_Re_100 = np.array([0.996741, 0.996986, 0.997157, 0.997339, 0.997482, 0.997731, 0.997885, 0.998158, 0.998294, 0.998512, 0.998836,
        0.999011, 0.999213, 0.99942, 0.99964, 0.999846, 1.00241, 1.02216, 1.0392, 1.06545, 1.08705, 1.0995, 1.1206, 1.14708, 1.16583, 1.18871, 1.21407,
        1.23518, 1.25628, 1.27868, 1.29996, 1.31593, 1.36025, 1.39055, 1.42224, 1.45114, 1.48144, 1.51175, 1.54205, 1.57235, 1.60267, 1.62032, 1.64208,
        1.68552
    ])
    _dP_staggered_correction_Re_1000 = np.array([1.03576, 1.02714, 1.02111, 1.01712, 1.01206, 1.00798, 1.00547, 1.001, 0.999839, 0.999378, 0.998689, 0.998319,
        0.997891, 0.997451, 0.996985, 0.999249, 1.00245, 1.0135, 1.02415, 1.03618, 1.04682, 1.0534, 1.06478, 1.07524, 1.0836, 1.09539, 1.10811, 1.11825,
        1.12833, 1.13858, 1.1481, 1.15678, 1.17941, 1.19487, 1.21106, 1.22398, 1.24068, 1.25657, 1.27109, 1.28706, 1.30317, 1.31111, 1.3196, 1.33956
    ])
    _dP_staggered_correction_Re_10000 = np.array([1.20211, 1.18293, 1.16951, 1.15527, 1.14308, 1.12148, 1.10821, 1.09069, 1.08213, 1.06633, 1.04824, 1.04041,
        1.03015, 1.02269, 1.01509, 1.00905, 1.00302, 1.00302, 1.00304, 1.00623, 1.00905, 1.0103, 1.01246, 1.01508, 1.01696, 1.01926, 1.0225, 1.02674,
        1.03074, 1.03432, 1.03618, 1.03931, 1.04813, 1.05451, 1.05855, 1.0674, 1.07355, 1.08006, 1.08719, 1.09572, 1.10324, 1.10854, 1.11428, 1.12663
    ])
    _dP_staggered_correction_Re_100000 = np.array([1.45829, 1.42587, 1.40486, 1.38291, 1.36389, 1.32864, 1.30754, 1.27136, 1.25327, 1.22447, 1.18203, 1.15678,
        1.12845, 1.10251, 1.07182, 1.04763, 1.00824, 0.984925, 0.975402, 0.965711, 0.960152, 0.957646, 0.9534, 0.948334, 0.945015, 0.942714, 0.940164,
        0.937857, 0.936683, 0.936683, 0.934823, 0.933668, 0.933668, 0.933668, 0.933668, 0.933668, 0.933668, 0.936683, 0.936683, 0.936683, 0.939698,
        0.939698, 0.939698, 0.939698
    ])
    _dP_staggered_correction_Re_parameters = np.array([_dP_staggered_correction_Re_100, _dP_staggered_correction_Re_1000, _dP_staggered_correction_Re_10000, _dP_staggered_correction_Re_100000]).T
    dP_staggered_correction = RectBivariateSpline(_dP_staggered_correction_parameters, np.array([1E2, 1E3, 1E4, 1E5]), _dP_staggered_correction_Re_parameters, kx=1, ky=3, s=0.002)
    
    # Maybe good plot - bad around the middle