#!/usr/bin/env python
import numpy as np

# relative energies (kJ/mol) of the J states compared to j=0. Starts at J=1. Columns indicate different nu states (nu=0,1,2,3)
E_J = np.array(
[[0.247948059,	0.241083342,	0.234290361,	0.227569889],
[0.743690611,	0.723098099,	0.702720904,	0.682561293],
[1.486920669,	1.445740527,	1.404991453,	1.364677614],
[2.47717818,	2.408555485,	2.340652126,	2.273474395],
[3.713850513,	3.610936852,	3.509103885,	3.408359736],
[5.196173174,	5.052128261,	4.909599142,	4.768595012],
[6.923230577,	6.731223859,	6.541242581,	6.353295628],
[8.893957059,	8.647169342,	8.402992142,	8.161431969],
[11.10713806,	10.79876308,	10.49366014,	10.19183055],
[13.56141138,	13.18465738,	12.81191452,	12.44317524],
[16.25526866,	15.80335999,	15.35628033,	14.91400864],
[19.18705699,	18.65323562,	18.12514121,	17.60273369],
[22.35498059,	21.7325077,	21.11674112,	20.50761525],
[25.75710274,	25.03926024,	24.32918621,	23.626782],
[29.39134774,	28.57143978,	27.76044669,	26.95822829],
[33.25550306,	32.32685752,	31.40835899,	30.49981624],
[37.34722151,	36.30319159,	35.27062787,	34.24927781],
[41.66402365,	40.49798924,	39.34482876,	38.20421717],
[46.2033002,	44.90866942,	43.62841013,	42.36211299],
[50.96231456,	49.53252522,	48.11869598,	46.72032088],
[55.93820546,	54.36672649,	52.8128884,	51.27607597],
[61.12798961,	59.40832252,	57.70807021,	56.02649547],
[66.52856452,	64.65424483,	62.80120768,	60.96858134],
[72.13671128,	70.1013099,	68.08915328,	66.09922307],
[77.94909747,	75.74622217,	73.56864856,	71.41520045],
[83.96228009,	81.58557684,	79.23632694,	76.91318639],
[90.17270849,	87.61586284,	85.08871672,	82.58974982],
[96.5767274,	93.83346588,	91.12224387,	88.44135863],
[103.1705799,	100.2346713,	97.33323517,	94.46438261]]
)

# relative energies (kJ/mol) of the nu=1,2,3 states compared to nu=0 for J=0 (first row) and Trot=506 K (i.e., mixture of J states) (second row)
E_v = np.array(
[[33.9578089775581, 66.7479692338022, 98.3949773752105],
[33.240153, 65.3094574, 9999.]]
)

NN_DS1 = np.array(
[[0.94, 0.001103, 0.000332],
[1.18, 0.019620, 0.001388],
[1.29, 0.031829, 0.001756],
[1.55, 0.099649, 0.003001],
[1.80, 0.169751, 0.003765],
[2.12, 0.244622, 0.004320],
[2.56, 0.330693, 0.004741]]
)

NN_DS1_1060 = np.array(
[[0.94, 0.005906, 0.000767],
[1.18, 0.028036, 0.001652],
[1.29, 0.046502, 0.002108],
[1.55, 0.114134, 0.003186],
[1.80, 0.177650, 0.003837],
[2.12, 0.253694, 0.004377],
[2.56, 0.330693, 0.004741]]
)

NN_DS1_v0 = np.array(
[[0.94, 0.000901, 0.000300],
[1.18, 0.021332, 0.001446],
[1.29, 0.033554, 0.001802],
[1.55, 0.100672, 0.003013],
[1.80, 0.167204, 0.003744],
[2.12, 0.240576, 0.004291],
[2.56, 0.324591, 0.004721]]
)

NN_DS1_v0_1060 = np.array(
[[0.94, 0.002001, 0.000447],
[1.18, 0.026740, 0.001614],
[1.29, 0.038361, 0.001922],
[1.55, 0.112270, 0.003162],
[1.80, 0.175098, 0.003811],
[2.12, 0.246415, 0.004331],
[2.56, 0.324591, 0.004721]]
)

NN_DS1_v0_j0 = np.array(
[[0.94, 0.001102, 0.000332],
[1.18, 0.024266, 0.001541],
[1.29, 0.033300, 0.001797],
[1.55, 0.094051, 0.002926],
[1.80, 0.150966, 0.003592],
[2.12, 0.219990, 0.004162],
[2.56, 0.289829, 0.004569]]
)

NN_DS1_v0_j1 = np.array(
[[0.94, 0.000800, 0.000283],
[1.18, 0.020310, 0.001411],
[1.29, 0.032432, 0.001772],
[1.55, 0.093681, 0.002918],
[1.80, 0.155638, 0.003633],
[2.12, 0.223150, 0.004177],
[2.56, 0.288559, 0.004567]]
)

NN_DS1_v0_j2 = np.array(
[[0.94, 0.001000, 0.000316],
[1.18, 0.019614, 0.001387],
[1.29, 0.031851, 0.001757],
[1.55, 0.098196, 0.002979],
[1.80, 0.156049, 0.003638],
[2.12, 0.223477, 0.004183],
[2.56, 0.302868, 0.001463]]
)

NN_DS1_v0_j3 = np.array(
[[0.94, 0.001400, 0.000374],
[1.18, 0.021513, 0.001451],
[1.29, 0.033437, 0.001799],
[1.55, 0.101744, 0.003027],
[1.80, 0.164386, 0.003717],
[2.12, 0.237659, 0.004272],
[2.56, 0.303977, 0.004633]]
)

NN_DS1_v0_j4 = np.array(
[[0.94, 0.001300, 0.000360],
[1.18, 0.021217, 0.001442],
[1.29, 0.035325, 0.001847],
[1.55, 0.109541, 0.003127],
[1.80, 0.168427, 0.003754],
[2.12, 0.242360, 0.004303],
[2.56, 0.316633, 0.004685]]
)

NN_DS1_v0_j5 = np.array(
[[0.94, 0.000900, 0.000300],
[1.18, 0.023219, 0.001507],
[1.29, 0.040260, 0.001967],
[1.55, 0.117506, 0.003224],
[1.80, 0.178263, 0.003838],
[2.12, 0.258003, 0.004397],
[2.56, 0.338177, 0.004769]]
)

NN_DS1_v0_j6 = np.array(
[[0.94, 0.001100, 0.000332],
[1.18, 0.021907, 0.001464],
[1.29, 0.036447, 0.001875],
[1.55, 0.117210, 0.003222],
[1.80, 0.189347, 0.003928],
[2.12, 0.262222, 0.004421],
[2.56, 0.347990, 0.004811]]
)

NN_DS1_v0_j7 = np.array(
[[0.94, 0.002801, 0.000529],
[1.18, 0.029035, 0.001680],
[1.29, 0.038762, 0.001932],
[1.55, 0.117115, 0.003223],
[1.80, 0.188333, 0.003928],
[2.12, 0.268515, 0.004458],
[2.56, 0.353793, 0.004828]]
)

NN_DS1_v0_j8 = np.array(
[[0.94, 0.002302, 0.000480],
[1.18, 0.032180, 0.001767],
[1.29, 0.052943, 0.002242],
[1.55, 0.128633, 0.003358],
[1.80, 0.200000, 0.004018],
[2.12, 0.276568, 0.004502],
[2.56, 0.366443, 0.001540]]
)

NN_DS1_v1_1060 = np.array(
[[0.94, 0.145401, 0.003546],
[1.18, 0.241958, 0.004321],
[1.29, 0.279215, 0.004536],
[1.55, 0.343007, 0.004816],
[1.80, 0.392754, 0.004969],
[2.12, 0.443879, 0.005063],
[2.56, 0.495593, 0.005122]]
)

NN_DS1_v1_j0 = np.array(
[[0.94, 0.111168, 0.003167],
[1.18, 0.188976, 0.003959],
[1.29, 0.221188, 0.004197],
[1.55, 0.282096, 0.004566],
[1.80, 0.337912, 0.004807],
[2.12, 0.382011, 0.004952],
[2.56, 0.430516, 0.005067]]
)

NN_DS1_v1_j1 = np.array(
[[0.94, 0.119559, 0.003264],
[1.18, 0.203012, 0.004058],
[1.29, 0.228997, 0.004243],
[1.55, 0.297322, 0.004630],
[1.80, 0.341584, 0.004816],
[2.12, 0.385476, 0.004954],
[2.56, 0.441798, 0.005065]]
)

NN_DS1_v1_j2 = np.array(
[[0.94, 0.120937, 0.003276],
[1.18, 0.210168, 0.004108],
[1.29, 0.247325, 0.004355],
[1.55, 0.307984, 0.004674],
[1.80, 0.347249, 0.004828],
[2.12, 0.397816, 0.004991],
[2.56, 0.453314, 0.005082]]
)

NN_DS1_v1_j3 = np.array(
[[0.94, 0.124886, 0.003324],
[1.18, 0.215295, 0.004145],
[1.29, 0.250535, 0.004374],
[1.55, 0.309375, 0.004684],
[1.80, 0.362275, 0.004884],
[2.12, 0.408184, 0.005009],
[2.56, 0.453890, 0.005088]]
)

NN_DS1_v1_j4 = np.array(
[[0.94, 0.133873, 0.003427],
[1.18, 0.234099, 0.004268],
[1.29, 0.253894, 0.004391],
[1.55, 0.323786, 0.004741],
[1.80, 0.376083, 0.004920],
[2.12, 0.425550, 0.005035],
[2.56, 0.469623, 0.005104]]
)

NN_DS1_v1_j5 = np.array(
[[0.94, 0.137962, 0.003467],
[1.18, 0.235491, 0.004278],
[1.29, 0.266483, 0.004463],
[1.55, 0.339185, 0.004797],
[1.80, 0.397792, 0.004972],
[2.12, 0.436071, 0.005056],
[2.56, 0.493516, 0.005113]]
)

NN_DS1_v1_j6 = np.array(
[[0.94, 0.145572, 0.003546],
[1.18, 0.249847, 0.004370],
[1.29, 0.274612, 0.004510],
[1.55, 0.358001, 0.004857],
[1.80, 0.408545, 0.005000],
[2.12, 0.460390, 0.005075],
[2.56, 0.519762, 0.005109]]
)

NN_DS1_v1_j7 = np.array(
[[0.94, 0.164158, 0.003736],
[1.18, 0.270433, 0.004490],
[1.29, 0.301115, 0.004639],
[1.55, 0.379969, 0.004932],
[1.80, 0.438656, 0.005045],
[2.12, 0.487500, 0.005102],
[2.56, 0.543540, 0.005099]]
)

NN_DS1_v1_j8 = np.array(
[[0.94, 0.189935, 0.003951],
[1.18, 0.292615, 0.004608],
[1.29, 0.335349, 0.004780],
[1.55, 0.408856, 0.005000],
[1.80, 0.467665, 0.005088],
[2.12, 0.518061, 0.005113],
[2.56, 0.560868, 0.005093]]
)

NN_DS1_v2_1060 = np.array(
[[0.94, 0.323038, 0.004793],
[1.18, 0.385305, 0.004997],
[1.29, 0.400853, 0.005060],
[1.55, 0.459294, 0.005161],
[1.80, 0.493799, 0.005192],
[2.12, 0.536675, 0.005194],
[2.56, 0.576805, 0.005168]]
)

NN_DS1_v2_j0 = np.array(
[[0.94, 0.210016, 0.004160],
[1.18, 0.257705, 0.004486],
[1.29, 0.279992, 0.004604],
[1.55, 0.343856, 0.004897],
[1.80, 0.383651, 0.005033],
[2.12, 0.432304, 0.005148],
[2.56, 0.488746, 0.005225]]
)

NN_DS1_v2_j1 = np.array(
[[0.94, 0.233316, 0.001361],
[1.18, 0.287259, 0.001463],
[1.29, 0.306497, 0.001493],
[1.55, 0.359197, 0.001560],
[1.80, 0.400208, 0.001597],
[2.12, 0.443347, 0.001625],
[2.56, 0.492298, 0.001644]]
)

NN_DS1_v2_j2 = np.array(
[[0.94, 0.261258, 0.004475],
[1.18, 0.307943, 0.004729],
[1.29, 0.332315, 0.004836],
[1.55, 0.376494, 0.004983],
[1.80, 0.409251, 0.005076],
[2.12, 0.451271, 0.005153],
[2.56, 0.502900, 0.001649]]
)

NN_DS1_v2_j3 = np.array(
[[0.94, 0.276425, 0.004562],
[1.18, 0.339493, 0.004838],
[1.29, 0.343321, 0.004877],
[1.55, 0.393096, 0.005034],
[1.80, 0.424586, 0.005110],
[2.12, 0.469825, 0.005190],
[2.56, 0.507073, 0.005215]]
)

NN_DS1_v2_j4 = np.array(
[[0.94, 0.302391, 0.004693],
[1.18, 0.351445, 0.004904],
[1.29, 0.371347, 0.004972],
[1.55, 0.426089, 0.005090],
[1.80, 0.460375, 0.005162],
[2.12, 0.492351, 0.005189],
[2.56, 0.545029, 0.005193]]
)

NN_DS1_v2_j5 = np.array(
[[0.94, 0.326153, 0.004806],
[1.18, 0.373624, 0.004977],
[1.29, 0.398894, 0.005050],
[1.55, 0.446697, 0.005149],
[1.80, 0.490334, 0.005195],
[2.12, 0.531799, 0.005212],
[2.56, 0.574021, 0.005186]]
)

NN_DS1_v2_j6 = np.array(
[[0.94, 0.352222, 0.004902],
[1.18, 0.415411, 0.005094],
[1.29, 0.426189, 0.005113],
[1.55, 0.481025, 0.005195],
[1.80, 0.518945, 0.005206],
[2.12, 0.562663, 0.005174],
[2.56, 0.604374, 0.005139]]
)

NN_DS1_v2_j7 = np.array(
[[0.94, 0.396557, 0.005043],
[1.18, 0.458119, 0.005163],
[1.29, 0.479764, 0.005169],
[1.55, 0.519229, 0.005215],
[1.80, 0.564007, 0.005180],
[2.12, 0.603437, 0.005118],
[2.56, 0.643086, 0.005033]]
)

NN_DS1_v2_j8 = np.array(
[[0.94, 0.443592, 0.005128],
[1.18, 0.510086, 0.005178],
[1.29, 0.530024, 0.005196],
[1.55, 0.577140, 0.005149],
[1.80, 0.611202, 0.005104],
[2.12, 0.645721, 0.005013],
[2.56, 0.685637, 0.001546]]
)

NN_DS2 = np.array(
[[0.49, 0.000130, 0.000036],
[0.69, 0.001401, 0.000374],
[1.03, 0.010215, 0.001006],
[1.27, 0.044480, 0.002063],
[1.47, 0.087645, 0.002832],
[1.76, 0.169316, 0.003762],
[2.56, 0.330693, 0.004741]]
)

NN_DS2_nopara = np.array(
[[0.49, 0.000100, 0.000100],
[0.69, 0.001501, 0.000387],
[1.03, 0.010406, 0.001015],
[1.27, 0.043056, 0.002031],
[1.47, 0.092498, 0.002904],
[1.76, 0.172139, 0.003789],
[2.56, 0.330693, 0.004741]]
)

NN_DS2_v0 = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.007004, 0.000834],
[1.27, 0.039563, 0.001951],
[1.47, 0.086717, 0.002818],
[1.76, 0.167185, 0.003739],
[2.56, 0.324591, 0.004721]]
)

NN_DS2_v0_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.007103, 0.000840],
[1.27, 0.034849, 0.001835],
[1.47, 0.090161, 0.002868],
[1.76, 0.169348, 0.003761],
[2.56, 0.324591, 0.004721]]
)

NN_DS2_v0_j0_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.005711, 0.000754],
[1.27, 0.034987, 0.001840],
[1.47, 0.074885, 0.002637],
[1.76, 0.148647, 0.003568],
[2.56, 0.289829, 0.004569]]
)

NN_DS2_v0_j1 = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.003500, 0.000591],
[1.27, 0.033610, 0.001803],
[1.47, 0.076353, 0.002658],
[1.76, 0.147851, 0.003557],
[2.56, 0.288559, 0.004567]]
)

NN_DS2_v0_j1_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.003301, 0.000574],
[1.27, 0.032226, 0.001767],
[1.47, 0.072982, 0.002604],
[1.76, 0.151814, 0.003597],
[2.56, 0.288559, 0.004567]]
)

NN_DS2_v0_j2 = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.004200, 0.000647],
[1.27, 0.029135, 0.001683],
[1.47, 0.078396, 0.002691],
[1.76, 0.152934, 0.003608],
[2.56, 0.302868, 0.001463]]
)

NN_DS2_v0_j2_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.004900, 0.000698],
[1.27, 0.032445, 0.001773],
[1.47, 0.078443, 0.002693],
[1.76, 0.145411, 0.003533],
[2.56, 0.302868, 0.001463]]
)

NN_DS2_v0_j3 = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.005702, 0.000753],
[1.27, 0.032423, 0.001772],
[1.47, 0.079142, 0.002702],
[1.76, 0.158196, 0.003657],
[2.56, 0.303977, 0.004633]]
)

NN_DS2_v0_j3_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000100, 0.000100],
[1.03, 0.004802, 0.000691],
[1.27, 0.032442, 0.001773],
[1.47, 0.079443, 0.002707],
[1.76, 0.159413, 0.003670],
[2.56, 0.303977, 0.004633]]
)

NN_DS2_v0_j4_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000100, 0.000100],
[1.03, 0.005403, 0.000733],
[1.27, 0.037478, 0.002003],
[1.47, 0.085204, 0.002795],
[1.76, 0.167973, 0.003748],
[2.56, 0.316633, 0.004685]]
)

NN_DS2_v0_j5_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.005801, 0.000760],
[1.27, 0.038539, 0.001926],
[1.47, 0.087083, 0.002823],
[1.76, 0.176748, 0.003824],
[2.56, 0.338177, 0.004769]]
)

NN_DS2_v0_j6_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.004601, 0.000677],
[1.27, 0.039055, 0.001939],
[1.47, 0.082665, 0.002757],
[1.76, 0.168395, 0.003754],
[2.56, 0.347990, 0.004811]]
)

NN_DS2_v0_j7 = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000100, 0.000100],
[1.03, 0.007507, 0.000864],
[1.27, 0.042577, 0.002021],
[1.47, 0.094808, 0.002936],
[1.76, 0.176210, 0.003822],
[2.56, 0.353793, 0.004828]]
)

NN_DS2_v0_j7_nopara = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.000000, 0.000039],
[1.03, 0.008202, 0.000902],
[1.27, 0.042163, 0.002011],
[1.47, 0.091922, 0.002894],
[1.76, 0.177648, 0.003833],
[2.56, 0.353793, 0.004828]]
)

NN_DS2_v0_j8_nopara = np.array(
[[0.49, 1.000000, 0.000000],
[0.69, 1.000000, 0.000000],
[1.03, 0.011615, 0.001072],
[1.27, 0.049719, 0.002176],
[1.47, 0.109984, 0.003136],
[1.76, 0.189497, 0.003935],
[2.56, 0.366443, 0.001540]]
)

NN_DS2_v1 = np.array(
[[0.49, 0.000000, 0.000039],
[0.69, 0.046514, 0.002111],
[1.03, 0.180138, 0.003873],
[1.27, 0.266885, 0.004475],
[1.47, 0.330403, 0.004756],
[1.76, 0.385825, 0.004952],
[2.56, 0.495593, 0.005122]]
)

NN_DS2_v1_nopara = np.array(
[[0.49, 0.003107, 0.000557],
[0.69, 0.046058, 0.002102],
[1.03, 0.187735, 0.003935],
[1.27, 0.273891, 0.004513],
[1.47, 0.336898, 0.004793],
[1.76, 0.392242, 0.004959],
[2.56, 0.495593, 0.005122]]
)

NN_DS2_v1_j0_nopara = np.array(
[[0.49, 0.001304, 0.000361],
[0.69, 0.031002, 0.001739],
[1.03, 0.144501, 0.003548],
[1.27, 0.214066, 0.004153],
[1.47, 0.274212, 0.004521],
[1.76, 0.330716, 0.004782],
[2.56, 0.430516, 0.005067]]
)

NN_DS2_v1_j1 = np.array(
[[0.49, 0.001391, 0.000118],
[0.69, 0.031195, 0.000551],
[1.03, 0.149425, 0.001134],
[1.27, 0.229277, 0.001341],
[1.47, 0.280592, 0.001435],
[1.76, 0.334470, 0.001512],
[2.56, 0.441798, 0.005065]]
)

NN_DS2_v1_j1_nopara = np.array(
[[0.49, 0.001002, 0.000317],
[0.69, 0.029645, 0.001700],
[1.03, 0.153488, 0.003627],
[1.27, 0.228700, 0.004240],
[1.47, 0.283591, 0.004563],
[1.76, 0.332854, 0.004777],
[2.56, 0.441798, 0.005065]]
)

NN_DS2_v1_j2 = np.array(
[[0.49, 0.001302, 0.000361],
[0.69, 0.033055, 0.001792],
[1.03, 0.160608, 0.003695],
[1.27, 0.236193, 0.004287],
[1.47, 0.290742, 0.004593],
[1.76, 0.350021, 0.004836],
[2.56, 0.453314, 0.005082]]
)

NN_DS2_v1_j2_nopara = np.array(
[[0.49, 0.001403, 0.000375],
[0.69, 0.033748, 0.001810],
[1.03, 0.161477, 0.003706],
[1.27, 0.237379, 0.004288],
[1.47, 0.300471, 0.004639],
[1.76, 0.347750, 0.004833],
[2.56, 0.453314, 0.005082]]
)

NN_DS2_v1_j3_nopara = np.array(
[[0.49, 0.001403, 0.000375],
[0.69, 0.037503, 0.001905],
[1.03, 0.167663, 0.003761],
[1.27, 0.252421, 0.004386],
[1.47, 0.301649, 0.004645],
[1.76, 0.360091, 0.004870],
[2.56, 0.453890, 0.005088]]
)

NN_DS2_v1_j4_nopara = np.array(
[[0.49, 0.001203, 0.000347],
[0.69, 0.034899, 0.001840],
[1.03, 0.174871, 0.003827],
[1.27, 0.257791, 0.004422],
[1.47, 0.315174, 0.004706],
[1.76, 0.374266, 0.004912],
[2.56, 0.469623, 0.005104]]
)

NN_DS2_v1_j5_nopara = np.array(
[[0.49, 0.002305, 0.000480],
[0.69, 0.040398, 0.001974],
[1.03, 0.175038, 0.003828],
[1.27, 0.266877, 0.004467],
[1.47, 0.327014, 0.004749],
[1.76, 0.388237, 0.004946],
[2.56, 0.493516, 0.005113]]
)

NN_DS2_v1_j6_nopara = np.array(
[[0.49, 0.002805, 0.000529],
[0.69, 0.049377, 0.002173],
[1.03, 0.179618, 0.003871],
[1.27, 0.278844, 0.004531],
[1.47, 0.335487, 0.004782],
[1.76, 0.402293, 0.004983],
[2.56, 0.519762, 0.005109]]
)

NN_DS2_v1_j7_nopara = np.array(
[[0.49, 0.004911, 0.000700],
[0.69, 0.055120, 0.002289],
[1.03, 0.214213, 0.004137],
[1.27, 0.303009, 0.004649],
[1.47, 0.350627, 0.004836],
[1.76, 0.429945, 0.005034],
[2.56, 0.543540, 0.005099]]
)

NN_DS2_v1_j8 = np.array(
[[0.49, 0.004905, 0.000699],
[0.69, 0.071163, 0.002579],
[1.03, 0.242809, 0.004323],
[1.27, 0.342448, 0.004802],
[1.47, 0.393196, 0.004960],
[1.76, 0.459796, 0.005080],
[2.56, 0.560868, 0.005093]]
)

NN_DS2_v1_j8_nopara = np.array(
[[0.49, 0.004002, 0.000632],
[0.69, 0.066707, 0.002503],
[1.03, 0.241067, 0.004316],
[1.27, 0.333265, 0.004770],
[1.47, 0.390035, 0.004954],
[1.76, 0.452905, 0.005070],
[2.56, 0.560868, 0.005093]]
)

NN_DS2_v2 = np.array(
[[0.49, 0.113783, 0.003212],
[0.69, 0.243364, 0.004370],
[1.03, 0.352613, 0.004904],
[1.27, 0.403781, 0.005056],
[1.47, 0.446943, 0.005145],
[1.76, 0.491425, 0.005192],
[2.56, 0.576805, 0.005168]]
)

NN_DS2_v2_nopara = np.array(
[[0.49, 0.111498, 0.003185],
[0.69, 0.242954, 0.004374],
[1.03, 0.347611, 0.004891],
[1.27, 0.406545, 0.005055],
[1.47, 0.448257, 0.005142],
[1.76, 0.483586, 0.005184],
[2.56, 0.576805, 0.005168]]
)

NN_DS2_v2_j0_nopara = np.array(
[[0.49, 0.062196, 0.002433],
[0.69, 0.143889, 0.003566],
[1.03, 0.235306, 0.004342],
[1.27, 0.289476, 0.004655],
[1.47, 0.326461, 0.004834],
[1.76, 0.381960, 0.005023],
[2.56, 0.488746, 0.005225]]
)

NN_DS2_v2_j1_nopara = np.array(
[[0.49, 0.065788, 0.002496],
[0.69, 0.162132, 0.003742],
[1.03, 0.251911, 0.004442],
[1.27, 0.307059, 0.004738],
[1.47, 0.337399, 0.004870],
[1.76, 0.393683, 0.005055],
[2.56, 0.490025, 0.005219]]
)

NN_DS2_v2_j2 = np.array(
[[0.49, 0.072945, 0.002621],
[0.69, 0.183313, 0.003922],
[1.03, 0.279099, 0.004571],
[1.27, 0.332420, 0.004835],
[1.47, 0.368683, 0.004961],
[1.76, 0.407487, 0.005082],
[2.56, 0.502900, 0.001649]]
)

NN_DS2_v2_j2_nopara = np.array(
[[0.49, 0.073062, 0.002623],
[0.69, 0.177163, 0.003879],
[1.03, 0.278721, 0.004576],
[1.27, 0.325118, 0.004807],
[1.47, 0.364176, 0.004954],
[1.76, 0.406440, 0.005072],
[2.56, 0.502900, 0.001649]]
)

NN_DS2_v2_j3_nopara = np.array(
[[0.49, 0.088613, 0.002868],
[0.69, 0.202066, 0.004081],
[1.03, 0.299520, 0.004679],
[1.27, 0.340735, 0.004862],
[1.47, 0.382922, 0.005000],
[1.76, 0.428297, 0.005113],
[2.56, 0.507073, 0.005215]]
)

NN_DS2_v2_j4_nopara = np.array(
[[0.49, 0.093693, 0.002944],
[0.69, 0.222774, 0.004232],
[1.03, 0.325150, 0.004798],
[1.27, 0.376215, 0.004979],
[1.47, 0.406643, 0.005068],
[1.76, 0.454409, 0.005163],
[2.56, 0.545029, 0.005193]]
)

NN_DS2_v2_j5_nopara = np.array(
[[0.49, 0.108360, 0.003144],
[0.69, 0.240668, 0.004353],
[1.03, 0.349020, 0.004880],
[1.27, 0.400232, 0.005035],
[1.47, 0.437868, 0.005133],
[1.76, 0.486600, 0.005196],
[2.56, 0.574021, 0.005186]]
)

NN_DS2_v2_j6_nopara = np.array(
[[0.49, 0.135830, 0.003475],
[0.69, 0.263706, 0.004499],
[1.03, 0.376737, 0.004972],
[1.27, 0.438063, 0.005136],
[1.47, 0.466517, 0.005164],
[1.76, 0.524274, 0.005199],
[2.56, 0.604374, 0.005139]]
)

NN_DS2_v2_j7_nopara = np.array(
[[0.49, 0.156340, 0.003691],
[0.69, 0.305322, 0.004723],
[1.03, 0.427283, 0.005095],
[1.27, 0.469130, 0.005180],
[1.47, 0.511442, 0.005193],
[1.76, 0.552305, 0.005185],
[2.56, 0.643086, 0.005033]]
)

NN_DS2_v2_j8 = np.array(
[[0.49, 0.163034, 0.003763],
[0.69, 0.327402, 0.004830],
[1.03, 0.466389, 0.005153],
[1.27, 0.530555, 0.005186],
[1.47, 0.561449, 0.005168],
[1.76, 0.604245, 0.005128],
[2.56, 0.685637, 0.001546]]
)

NN_DS2_v2_j8_nopara = np.array(
[[0.49, 0.162952, 0.003760],
[0.69, 0.336228, 0.004862],
[1.03, 0.467022, 0.005154],
[1.27, 0.525329, 0.005184],
[1.47, 0.567342, 0.005157],
[1.76, 0.599692, 0.005137],
[2.56, 0.685637, 0.001546]]
)

# The v=0, j=3 results are replaced with v=1, j=2 for a DS2 molecule beam, i.e., v and j were sampled according to TN=1060 K.
# It is assumed that 0.75 (fraction HCl35 due to the presence of HCl37) * 0.5 (efficiency of laser) is replaced
NN_DS2_v1_j2_laser = np.array(
[[0.49, 0.000300, 0.000068],
[0.69, 0.003931, 0.000517],
[1.03, 0.021864, 0.001297],
[1.27, 0.061400, 0.002357],
[1.47, 0.108590, 0.003129],
[1.76, 0.192735, 0.004027],
[2.56, 0.352655, 0.004931]]
)

# The v=0, j=7 results are replaced with v=1, j=8 for a DS2 molecule beam, i.e., v and j were sampled according to TN=1060 K.
# It is assumed that 0.75 (fraction HCl35 due to the presence of HCl37) * 0.5 (efficiency of laser) is replaced
NN_DS2_v1_j8_laser = np.array(
[[0.49, 0.000387, 0.000068],
[0.69, 0.004349, 0.000498],
[1.03, 0.020706, 0.001220],
[1.27, 0.059925, 0.002286],
[1.47, 0.106043, 0.003051],
[1.76, 0.190937, 0.003956],
[2.56, 0.351001, 0.004864]]
)

# The v=0, j=3 results are replaced with v=2, j=2 for a DS2 molecule beam, i.e., v and j were sampled according to TN=1060 K.
# It is assumed that 0.75 (fraction HCl35 due to the presence of HCl37) * 0.5 (efficiency of laser) is replaced
NN_DS2_v2_j2_laser = np.array(
[[0.49, 0.006418, 0.000213],
[0.69, 0.014578, 0.000637],
[1.03, 0.029847, 0.001343],
[1.27, 0.067608, 0.002384],
[1.47, 0.113622, 0.003147],
[1.76, 0.196607, 0.004039],
[2.56, 0.356011, 0.004936]]
)

# The v=0, j=7 results are replaced with v=2, j=8 for a DS2 molecule beam, i.e., v and j were sampled according to TN=1060 K.
# It is assumed that 0.75 (fraction HCl35 due to the presence of HCl37) * 0.5 (efficiency of laser) is replaced
NN_DS2_v2_j8_laser = np.array(
[[0.49, 0.008274, 0.000181],
[0.69, 0.015199, 0.000567],
[1.03, 0.028952, 0.001236],
[1.27, 0.066714, 0.002289],
[1.47, 0.111824, 0.003050],
[1.76, 0.195686, 0.003952],
[2.56, 0.354818, 0.004854]]
)

NN_Rahinov_v0_j0 = np.array(
[[0.28, 0.000000, 0.000039],
[0.32, 0.000000, 0.000039],
[0.45, 0.000000, 0.000039],
[0.52, 0.000000, 0.000039],
[0.78, 0.000030, 0.000017],
[0.97, 0.001040, 0.000102],
[1.27, 0.029596, 0.000539]]
)

NN_Rahinov_v1_j1 = np.array(
[[0.28, 0.000000, 0.000039],
[0.32, 0.000000, 0.000039],
[0.45, 0.000060, 0.000025],
[0.52, 0.000590, 0.000077],
[0.78, 0.047643, 0.000675],
[0.97, 0.133698, 0.001082],
[1.27, 0.238568, 0.001361]]
)

NN_Rahinov_v2_j1 = np.array(
[[0.28, 0.002913, 0.000171],
[0.32, 0.004088, 0.000202],
[0.45, 0.036735, 0.000598],
[0.52, 0.072576, 0.000827],
[0.78, 0.187709, 0.001254],
[0.97, 0.244648, 0.001386],
[1.27, 0.312299, 0.001505]]
)

NN_Geweke_2016_v1_j1 = np.array(
[[0.59, 0.003773, 0.000194],
[0.64, 0.007950, 0.000281],
[0.92, 0.103694, 0.000969],
[0.94, 0.114537, 0.001012],
[1.04, 0.159731, 0.001166]]
)

# Ei, S0 and error from 2016 paper, lower and upper limit from Jan Geweke's PhD thesis
Shirhatti_DS1_old = np.array(
[[0.94, 0.000006, 0.000005, 2.4E-5, 6.1E-5],
[1.18, 0.00003, 0.00001, 7.1E-5, 1.8E-4],
[1.29, 0.00012, 0.00007, 3.0E-4, 7.8E-4],
[1.55, 0.0012, 0.0001, 3.1E-3, 8.4E-3],
[1.80, 0.0045, 0.0004, 1.2E-2, 3.2E-2],
[2.12, 0.0082, 0.0008, 2.2E-2, 6.0E-2],
[2.56, 0.021, 0.007, 5.6E-2, 1.6E-1]]
)

# New limits due to statistical and systematical errors
Shirhatti_DS1 = np.array(
[[0.94, 0.000006, 0.000005, 0.0000228027, 0.0000630936],
[1.18, 0.00003, 0.00001, 0.0000584038, 0.000215648],
[1.29, 0.00012, 0.00007, 0.0002257, 0.000983799],
[1.55, 0.0012, 0.0001, 0.00302683, 0.00871815],
[1.80, 0.0045, 0.0004, 0.0114649, 0.0336943],
[2.12, 0.0082, 0.0008, 0.019446, 0.0686922],
#[2.31, ?, ?, 0.0212132, 0.0753424],
#[2.48, ?, ?, 0.0230949, 0.119795],
[2.56, 0.021, 0.007, 0.0486626, 0.178205]]
)

Shirhatti_DS2_old = np.array(
[[0.49, 4.00E-05,	5.00E-05, 1.2E-4, 3.1E-4],
[0.69, 1.20E-04,	6.00E-05, 3.8E-4, 1.0E-3],
[1.03, 9.00E-04,	2.00E-04, 3.1E-3, 8.5E-3],
[1.27, 3.00E-03,	3.00E-04, 8.9E-3, 2.5E-2],
[1.47, 5.20E-03,	5.00E-04, 1.8E-2, 4.8E-2],
[1.76, 1.20E-02,	1.00E-03, 3.2E-2, 8.8E-2],
[2.56, 0.021, 0.007, 5.6E-2, 1.6E-1]]
)

Shirhatti_DS2 = np.array(
[[0.49, 4.00E-05,	5.00E-05, 0.0000705324, 0.00043989],
[0.69, 1.20E-04,	6.00E-05, 0.000316964, 0.00117081],
[1.03, 9.00E-04,	2.00E-04, 0.00284713, 0.00943288],
[1.27, 3.00E-03,	3.00E-04, 0.00849043, 0.0260768],
[1.47, 5.20E-03,	5.00E-04, 0.0169108, 0.0512065],
[1.76, 1.20E-02,	1.00E-03, 0.0316775, 0.0910361],
[2.56, 0.021, 0.007, 0.0486626, 0.178205]]
)

# Results from Gernot 2019
SRP32vdW = np.array(
[[1.29, 0.162, 0.017],
[1.80, 0.266, 0.020],
[2.56, 0.382, 0.022]]
)

#Liu 2018 fig 3b, RPBE QD results, it's likely that it's the rovibrational ground state since it's identical to the Liu 2017 results
RPBE_QD = np.array(
[[2.393300208652108, 0.40020795256367436],
[2.309179691194882, 0.3757137963901793],
[2.2355672277090006, 0.3757137963901793],
[2.1514467102517747, 0.35271877006357627],
[2.0988703853108923, 0.34175453456993377],
[2.035775991097511, 0.3364008952186411],
[1.9621735429127842, 0.3108647141022707],
[1.9095972179719023, 0.3012015086781123],
[1.8395001251912653, 0.2783373118598219],
[1.744871553762693, 0.24530945393039963],
[1.6397329253025459, 0.20619860095022213],
[1.5556324384476283, 0.16530489508302307],
[1.4820480178049802, 0.1325213081586614],
[1.4014729169564613, 0.09072234882997098],
[1.3454232855751849, 0.06827877398078833],
[1.2893896786757548, 0.04528975799036213],
[1.233380108499096, 0.024855847255841942],
[1.1983946585060512, 0.01453067249203442],
[1.1599238837112253, 0.007253897734006251],
[1.131977187369593, 0.0033996058776422514],
[1.098780470162749, 0.001495743564188077],
[1.0655957713172908, 0.0005986084693840038],
[1.0306944498539437, 0.00018030177408595737],
[1.0097624704409516, 0.00008187340588223125],
[0.9853571845875646, 0.000028878287023696633],
[0.9696952566420924, 0.000011928104316892801],
[0.9540333286966198, 0.000004926873691570628],
[0.9401481151759634, 0.0000016837814253475731]]
)

# Liu 2017 fig 7a
RPBE_QD_v0j0 = np.array(
[[1.15157, 0.00524],
[1.18280, 0.01120],
[1.20784, 0.01688],
[1.23972, 0.02613],
[1.26911, 0.03666],
[1.29849, 0.04829],
[1.32788, 0.06090],
[1.35726, 0.07398],
[1.38664, 0.08735],
[1.41603, 0.10077],
[1.44541, 0.11425],
[1.47480, 0.12762],
[1.50418, 0.14075],
[1.53357, 0.15388],
[1.56295, 0.16684],
[1.59233, 0.17952],
[1.62172, 0.19207],
[1.65110, 0.20440],
[1.68049, 0.21626],
[1.70987, 0.22794],
[1.73925, 0.23928],
[1.76864, 0.25010],
[1.79802, 0.26046],
[1.82741, 0.27047],
[1.85679, 0.27979],
[1.88618, 0.28875],
[1.91556, 0.29703],
[1.94494, 0.30507],
[1.97255, 0.31304]]
)

RPBE_QD_v0j5m5 = np.array(
[[0.99022, 0.00192],
[1.00986, 0.00209],
[1.02220, 0.00231],
[1.03455, 0.00253],
[1.04689, 0.00295],
[1.05924, 0.00350],
[1.07158, 0.00445],
[1.08393, 0.00579],
[1.09627, 0.00859],
[1.10862, 0.00964],
[1.12097, 0.01178],
[1.13331, 0.01482],
[1.14566, 0.01792],
[1.15800, 0.02116],
[1.17035, 0.02530],
[1.18269, 0.02970],
[1.19504, 0.03408],
[1.20682, 0.03897],
[1.21860, 0.04430],
[1.23039, 0.04950],
[1.24217, 0.05478],
[1.25289, 0.06030],
[1.26339, 0.06541],
[1.27405, 0.07103],
[1.28421, 0.07587],
[1.29444, 0.08125],
[1.30486, 0.08685],
[1.31486, 0.09241],
[1.32482, 0.09757],
[1.33472, 0.10300],
[1.34478, 0.10827],
[1.35504, 0.11370],
[1.36528, 0.11928],
[1.37488, 0.12450],
[1.38498, 0.12978],
[1.39480, 0.13491],
[1.40521, 0.14048],
[1.41613, 0.14609],
[1.42684, 0.15184],
[1.43779, 0.15734],
[1.44811, 0.16272],
[1.45877, 0.16805],
[1.46944, 0.17335],
[1.48066, 0.17875],
[1.49188, 0.18418],
[1.50310, 0.18958],
[1.51433, 0.19481],
[1.52555, 0.19999],
[1.53677, 0.20520],
[1.54800, 0.21019],
[1.55978, 0.21551],
[1.57213, 0.22069],
[1.58447, 0.22593],
[1.59682, 0.23126],
[1.60916, 0.23645],
[1.62151, 0.24140],
[1.63385, 0.24631],
[1.64620, 0.25120],
[1.65854, 0.25591],
[1.67089, 0.26047],
[1.68323, 0.26503],
[1.69558, 0.26950],
[1.70792, 0.27377],
[1.72027, 0.27804],
[1.73261, 0.28224],
[1.74496, 0.28625],
[1.75730, 0.29021],
[1.76965, 0.29418],
[1.78199, 0.29796],
[1.79434, 0.30171],
[1.80668, 0.30538],
[1.81903, 0.30890],
[1.83137, 0.31245],
[1.84372, 0.31588],
[1.85606, 0.31927],
[1.86841, 0.32255],
[1.88075, 0.32570],
[1.89310, 0.32890],
[1.90545, 0.33200],
[1.91779, 0.33497],
[1.93014, 0.33792],
[1.94248, 0.34076],
[1.95483, 0.34362],
[1.96717, 0.34640],
[1.97864, 0.34855],
[1.99074, 0.35264]]
)

RPBE_QD_v0j5m4 = np.array(
[[1.07000, 0.00213],
[1.08000, 0.00257],
[1.09000, 0.00309],
[1.10000, 0.00351],
[1.11000, 0.00404],
[1.12000, 0.00485],
[1.13000, 0.00570],
[1.14000, 0.00675],
[1.15000, 0.00776],
[1.16000, 0.00900],
[1.17000, 0.01047],
[1.18000, 0.01220],
[1.19000, 0.01362],
[1.20000, 0.01536],
[1.21000, 0.01735],
[1.22000, 0.01930],
[1.23000, 0.02145],
[1.24000, 0.02367],
[1.25000, 0.02618],
[1.26000, 0.02879],
[1.27000, 0.03160],
[1.28000, 0.03471],
[1.29000, 0.03770],
[1.30000, 0.04117],
[1.31000, 0.04444],
[1.32000, 0.04784],
[1.33000, 0.05128],
[1.34000, 0.05498],
[1.35000, 0.05888],
[1.36000, 0.06273],
[1.37000, 0.06674],
[1.38000, 0.07092],
[1.39000, 0.07503],
[1.40000, 0.07913],
[1.41000, 0.08338],
[1.42000, 0.08749],
[1.43000, 0.09176],
[1.44000, 0.09618],
[1.45000, 0.10057],
[1.46000, 0.10475],
[1.47000, 0.10923],
[1.48000, 0.11374],
[1.49000, 0.11808],
[1.50000, 0.12242],
[1.51000, 0.12687],
[1.52000, 0.13158],
[1.53000, 0.13565],
[1.54000, 0.14009],
[1.55000, 0.14436],
[1.56000, 0.14875],
[1.57000, 0.15286],
[1.58000, 0.15725],
[1.59000, 0.16167],
[1.60000, 0.16597],
[1.61000, 0.17002],
[1.62000, 0.17439],
[1.63000, 0.17813],
[1.64000, 0.18232],
[1.65000, 0.18650],
[1.66000, 0.19000],
[1.67000, 0.19455],
[1.68000, 0.19756],
[1.69000, 0.20153],
[1.70000, 0.20543],
[1.71000, 0.20903],
[1.72000, 0.21274],
[1.73000, 0.21642],
[1.74000, 0.21969],
[1.75000, 0.22290],
[1.76000, 0.22657],
[1.77000, 0.23007],
[1.78000, 0.23311],
[1.79000, 0.23695],
[1.80000, 0.23839],
[1.81000, 0.24143],
[1.82000, 0.24546],
[1.83000, 0.24833],
[1.84000, 0.25061],
[1.85000, 0.25377],
[1.86000, 0.25637],
[1.87000, 0.25884],
[1.88000, 0.26209],
[1.89000, 0.26465],
[1.90000, 0.26658],
[1.91000, 0.26899],
[1.92000, 0.27110],
[1.93000, 0.27393],
[1.94000, 0.27621],
[1.95000, 0.27721],
[1.96000, 0.28117],
[1.97000, 0.28233],
[1.98000, 0.28320],
[1.99000, 0.28565]]
)

RPBE_QD_v0j5m3 = np.array(
[[1.10000, 0.00581],
[1.11000, 0.00665],
[1.12000, 0.00768],
[1.13000, 0.00898],
[1.14000, 0.01034],
[1.15000, 0.01194],
[1.16000, 0.01380],
[1.17000, 0.01588],
[1.18000, 0.01800],
[1.19000, 0.02024],
[1.20000, 0.02245],
[1.21000, 0.02486],
[1.22000, 0.02722],
[1.23000, 0.02965],
[1.24000, 0.03212],
[1.25000, 0.03464],
[1.26000, 0.03726],
[1.27000, 0.03987],
[1.28000, 0.04258],
[1.29000, 0.04535],
[1.30000, 0.04812],
[1.31000, 0.05095],
[1.32000, 0.05389],
[1.33000, 0.05685],
[1.34000, 0.05993],
[1.35000, 0.06310],
[1.36000, 0.06586],
[1.37000, 0.06931],
[1.38059, 0.07258],
[1.39000, 0.07587],
[1.40000, 0.07910],
[1.41000, 0.08266],
[1.42000, 0.08575],
[1.43000, 0.08883],
[1.44000, 0.09295],
[1.45000, 0.09589],
[1.46000, 0.09939],
[1.47000, 0.10288],
[1.48000, 0.10633],
[1.49000, 0.10978],
[1.50000, 0.11332],
[1.51000, 0.11678],
[1.52000, 0.12027],
[1.53000, 0.12372],
[1.54000, 0.12722],
[1.55000, 0.13069],
[1.56000, 0.13415],
[1.57000, 0.13759],
[1.58000, 0.14100],
[1.59000, 0.14438],
[1.60000, 0.14783],
[1.61000, 0.15121],
[1.62000, 0.15462],
[1.63000, 0.15803],
[1.64000, 0.16140],
[1.65000, 0.16474],
[1.66000, 0.16805],
[1.67000, 0.17138],
[1.68000, 0.17464],
[1.69000, 0.17790],
[1.70000, 0.18115],
[1.71000, 0.18433],
[1.72000, 0.18752],
[1.73000, 0.19063],
[1.74000, 0.19383],
[1.75000, 0.19686],
[1.76000, 0.19996],
[1.77000, 0.20300],
[1.78000, 0.20604],
[1.79000, 0.20892],
[1.80000, 0.21182],
[1.81000, 0.21478],
[1.82000, 0.21759],
[1.83000, 0.22039],
[1.84000, 0.22315],
[1.85000, 0.22593],
[1.86000, 0.22859],
[1.87000, 0.23129],
[1.88000, 0.23387],
[1.89000, 0.23646],
[1.90000, 0.23901],
[1.91000, 0.24147],
[1.92000, 0.24390],
[1.93000, 0.24626],
[1.94000, 0.24868],
[1.95000, 0.25097],
[1.96000, 0.25323],
[1.97000, 0.25547],
[1.98000, 0.25770],
[1.99000, 0.25999]]
)

RPBE_QD_v0j5m2 = np.array(
[[1.16000, 0.01134],
[1.17000, 0.01299],
[1.18000, 0.01479],
[1.19000, 0.01666],
[1.20000, 0.01861],
[1.21000, 0.02056],
[1.22000, 0.02261],
[1.23000, 0.02491],
[1.24000, 0.02729],
[1.25000, 0.02984],
[1.26000, 0.03245],
[1.27000, 0.03523],
[1.28000, 0.03816],
[1.29000, 0.04117],
[1.30000, 0.04357],
[1.31000, 0.04644],
[1.32000, 0.04950],
[1.33000, 0.05253],
[1.34000, 0.05593],
[1.35000, 0.05924],
[1.36000, 0.06207],
[1.37000, 0.06507],
[1.38000, 0.06902],
[1.39000, 0.07226],
[1.40000, 0.07502],
[1.41000, 0.07830],
[1.42000, 0.08160],
[1.43000, 0.08493],
[1.44000, 0.08821],
[1.45000, 0.09152],
[1.46000, 0.09481],
[1.47000, 0.09800],
[1.48000, 0.10127],
[1.49000, 0.10451],
[1.50000, 0.10770],
[1.51000, 0.11091],
[1.52000, 0.11413],
[1.53000, 0.11738],
[1.54000, 0.12054],
[1.55000, 0.12364],
[1.56000, 0.12676],
[1.57000, 0.12982],
[1.58000, 0.13282],
[1.59000, 0.13580],
[1.60000, 0.13877],
[1.61000, 0.14172],
[1.62000, 0.14460],
[1.63000, 0.14744],
[1.64000, 0.15018],
[1.65000, 0.15295],
[1.66000, 0.15565],
[1.67000, 0.15833],
[1.68000, 0.16096],
[1.69000, 0.16356],
[1.70000, 0.16608],
[1.71000, 0.16861],
[1.72000, 0.17112],
[1.73000, 0.17354],
[1.74000, 0.17594],
[1.75000, 0.17833],
[1.76000, 0.18060],
[1.77000, 0.18300],
[1.78000, 0.18521],
[1.79000, 0.18736],
[1.80000, 0.18960],
[1.81000, 0.19168],
[1.82000, 0.19379],
[1.83000, 0.19589],
[1.84000, 0.19795],
[1.85000, 0.19995],
[1.86000, 0.20199],
[1.87000, 0.20401],
[1.88000, 0.20588],
[1.89000, 0.20782],
[1.90000, 0.20977],
[1.91000, 0.21168],
[1.92000, 0.21363],
[1.93000, 0.21554],
[1.94000, 0.21741],
[1.95000, 0.21927],
[1.96000, 0.22112],
[1.97000, 0.22299],
[1.98000, 0.22487],
[1.99000, 0.22682]]
)

RPBE_QD_v0j5m1 = np.array(
[[1.12000, 0.00788],
[1.13000, 0.01007],
[1.14000, 0.01142],
[1.15000, 0.01298],
[1.16000, 0.01465],
[1.17000, 0.01634],
[1.18000, 0.01797],
[1.19000, 0.01945],
[1.20000, 0.02068],
[1.21000, 0.02232],
[1.22000, 0.02474],
[1.23000, 0.02711],
[1.24000, 0.02959],
[1.25000, 0.03212],
[1.26000, 0.03476],
[1.27000, 0.03744],
[1.28000, 0.03953],
[1.29000, 0.04095],
[1.30000, 0.04267],
[1.31000, 0.04441],
[1.32000, 0.04585],
[1.33000, 0.04807],
[1.34000, 0.05107],
[1.35000, 0.05394],
[1.36000, 0.05639],
[1.37000, 0.05890],
[1.38000, 0.06143],
[1.39000, 0.06396],
[1.40000, 0.06658],
[1.41000, 0.06922],
[1.42000, 0.07193],
[1.43000, 0.07460],
[1.44000, 0.07731],
[1.45000, 0.08009],
[1.46000, 0.08290],
[1.47000, 0.08568],
[1.48000, 0.08856],
[1.49000, 0.09143],
[1.50000, 0.09434],
[1.51000, 0.09730],
[1.52000, 0.10029],
[1.53000, 0.10327],
[1.54000, 0.10628],
[1.55000, 0.10932],
[1.56000, 0.11229],
[1.57000, 0.11539],
[1.58000, 0.11846],
[1.59000, 0.12163],
[1.60000, 0.12469],
[1.61000, 0.12779],
[1.62000, 0.13094],
[1.63000, 0.13403],
[1.64000, 0.13717],
[1.65000, 0.14022],
[1.66000, 0.14327],
[1.67000, 0.14632],
[1.68000, 0.14938],
[1.69000, 0.15239],
[1.70000, 0.15538],
[1.71000, 0.15828],
[1.72000, 0.16116],
[1.73000, 0.16405],
[1.74000, 0.16677],
[1.75000, 0.16949],
[1.76000, 0.17231],
[1.77000, 0.17498],
[1.78000, 0.17758],
[1.79000, 0.18024],
[1.80000, 0.18282],
[1.81000, 0.18544],
[1.82000, 0.18791],
[1.83000, 0.19052],
[1.84000, 0.19298],
[1.85000, 0.19547],
[1.86000, 0.19781],
[1.87000, 0.20008],
[1.88000, 0.20227],
[1.89000, 0.20443],
[1.90000, 0.20657],
[1.91000, 0.20874],
[1.92000, 0.21087],
[1.93000, 0.21297],
[1.94000, 0.21513],
[1.95000, 0.21734],
[1.96000, 0.22161],
[1.97000, 0.22582],
[1.98000, 0.22729],
[1.99000, 0.22937]]
)

RPBE_QD_v0j5m0 = np.array(
[[1.08850, 0.00433],
[1.12979, 0.00815],
[1.16873, 0.01502],
[1.21357, 0.02342],
[1.25487, 0.03335],
[1.27257, 0.03819],
[1.29735, 0.04507],
[1.31268, 0.05016],
[1.32212, 0.05245],
[1.33274, 0.05525],
[1.34808, 0.06034],
[1.36224, 0.06442],
[1.37640, 0.06798],
[1.39410, 0.07257],
[1.41416, 0.07766],
[1.43000, 0.08198],
[1.44000, 0.08474],
[1.45000, 0.08776],
[1.46000, 0.09079],
[1.47000, 0.09383],
[1.48000, 0.09671],
[1.49000, 0.09959],
[1.50000, 0.10247],
[1.51000, 0.10535],
[1.52000, 0.10823],
[1.53000, 0.11111],
[1.54000, 0.11398],
[1.55000, 0.11684],
[1.56000, 0.11967],
[1.57000, 0.12252],
[1.58000, 0.12534],
[1.59000, 0.12820],
[1.60000, 0.13107],
[1.61000, 0.13396],
[1.62000, 0.13700],
[1.63000, 0.13988],
[1.64000, 0.14276],
[1.65000, 0.14571],
[1.66000, 0.14867],
[1.67000, 0.15156],
[1.68000, 0.15449],
[1.69000, 0.15737],
[1.70000, 0.16025],
[1.71000, 0.16313],
[1.72000, 0.16593],
[1.73000, 0.16870],
[1.74000, 0.17140],
[1.75000, 0.17412],
[1.76000, 0.17678],
[1.77000, 0.17932],
[1.78000, 0.18176],
[1.79000, 0.18411],
[1.80000, 0.18645],
[1.81000, 0.18875],
[1.82000, 0.19101],
[1.83000, 0.19373],
[1.84000, 0.19669],
[1.85000, 0.19904],
[1.86000, 0.20186],
[1.87000, 0.20452],
[1.88000, 0.20611],
[1.89000, 0.20849],
[1.90000, 0.21052],
[1.91000, 0.21314],
[1.92000, 0.21555],
[1.93000, 0.21789],
[1.94000, 0.22070],
[1.95000, 0.22278],
[1.96000, 0.22493],
[1.97000, 0.22731],
[1.98000, 0.22909],
[1.99000, 0.23142]]
)

#Liu 2018 fig 3b, RPBE QCT results, the rovibrational state distribution is according to the nozzle temperature
RPBE = np.array(
[[0.93999, 0.00309],
[1.18038, 0.04314],
[1.28970, 0.06939],
[1.55034, 0.16114],
[1.80134, 0.21804],
[2.12078, 0.29505],
[2.56150, 0.44002]]
)