#!/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]]
)

#Ei, Reaction, error
NN_DS1 = np.array(
[[0.94, 0.001150, 0.000240],
[1.18, 0.019303, 0.000973],
[1.29, 0.033152, 0.001266],
[1.55, 0.105661, 0.002174],
[1.80, 0.171730, 0.002668],
[2.12, 0.245175, 0.003046],
[2.56, 0.335325, 0.003345]]
)

# DS1 beam parameters, but TN is always 1060 K
NN_DS1_1060 = np.array(
[[0.94, 0.005803, 0.000760],
[1.18, 0.029353, 0.001689],
[1.29, 0.046579, 0.002109],
[1.55, 0.115601, 0.003206],
[1.80, 0.184187, 0.003888],
[2.12, 0.249342, 0.004352],
[2.56, 0.333469, 0.004752]]
)

NN_DS1_v0 = np.array(
[[0.94, 0.001202, 0.000347],
[1.18, 0.019828, 0.001395],
[1.29, 0.029927, 0.001705],
[1.55, 0.101234, 0.003021],
[1.80, 0.162929, 0.003704],
[2.12, 0.243625, 0.004310],
[2.56, 0.332010, 0.004751]]
)

NN_DS1_v0_1060 = np.array(
[[0.94, 0.001501, 0.000387],
[1.18, 0.028131, 0.001654],
[1.29, 0.040268, 0.001968],
[1.55, 0.114168, 0.003187],
[1.80, 0.184081, 0.003890],
[2.12, 0.250101, 0.004355],
[2.56, 0.332010, 0.004751]]
)

NN_DS1_v0_j0 = np.array(
[[0.94, 0.000701, 0.000265],
[1.18, 0.020758, 0.001428],
[1.29, 0.034514, 0.001828],
[1.55, 0.098394, 0.002984],
[1.80, 0.163294, 0.003709],
[2.12, 0.224477, 0.004193],
[2.56, 0.289514, 0.004570]]
)

NN_DS1_v0_j1 = np.array(
[[0.94, 0.001100, 0.000332],
[1.18, 0.020614, 0.001421],
[1.29, 0.029633, 0.001697],
[1.55, 0.092339, 0.002897],
[1.80, 0.151171, 0.003591],
[2.12, 0.214754, 0.004122],
[2.56, 0.289849, 0.004564]]
)

NN_DS1_v0_j2 = np.array(
[[0.94, 0.000600, 0.000245],
[1.18, 0.018009, 0.001330],
[1.29, 0.029721, 0.001699],
[1.55, 0.095668, 0.002945],
[1.80, 0.162396, 0.003697],
[2.12, 0.227694, 0.004208],
[2.56, 0.305140, 0.004632]]
)

NN_DS1_v0_j3 = np.array(
[[0.94, 0.001400, 0.000374],
[1.18, 0.020120, 0.001405],
[1.29, 0.030224, 0.001713],
[1.55, 0.095367, 0.002941],
[1.80, 0.166131, 0.003729],
[2.12, 0.234926, 0.004257],
[2.56, 0.303872, 0.004630]]
)

NN_DS1_v0_j4 = np.array(
[[0.94, 0.001200, 0.000346],
[1.18, 0.022416, 0.001481],
[1.29, 0.036333, 0.001872],
[1.55, 0.110064, 0.003133],
[1.80, 0.173611, 0.003800],
[2.12, 0.234584, 0.004250],
[2.56, 0.314236, 0.004674]]
)

NN_DS1_v0_j5 = np.array(
[[0.94, 0.001601, 0.000400],
[1.18, 0.022730, 0.001491],
[1.29, 0.035943, 0.001863],
[1.55, 0.108615, 0.003116],
[1.80, 0.177056, 0.003828],
[2.12, 0.252972, 0.004363],
[2.56, 0.327001, 0.004728]]
)

NN_DS1_v0_j6 = np.array(
[[0.94, 0.001301, 0.000360],
[1.18, 0.024229, 0.001539],
[1.29, 0.037723, 0.001906],
[1.55, 0.111067, 0.003147],
[1.80, 0.181571, 0.003868],
[2.12, 0.261436, 0.004416],
[2.56, 0.346768, 0.004802]]
)

NN_DS1_v0_j7 = np.array(
[[0.94, 0.001901, 0.000436],
[1.18, 0.026930, 0.001620],
[1.29, 0.043378, 0.002039],
[1.55, 0.120446, 0.003261],
[1.80, 0.189320, 0.003932],
[2.12, 0.263775, 0.004431],
[2.56, 0.353887, 0.004823]]
)

NN_DS1_v0_j8 = np.array(
[[0.94, 0.002301, 0.000479],
[1.18, 0.035188, 0.001845],
[1.29, 0.051655, 0.002217],
[1.55, 0.134004, 0.003417],
[1.80, 0.199516, 0.003704],
[2.12, 0.276369, 0.004504],
[2.56, 0.365045, 0.004862]]
)

NN_DS1_v1_1060 = np.array(
[[0.94, 0.149544, 0.003590],
[1.18, 0.242634, 0.004328],
[1.29, 0.279288, 0.004536],
[1.55, 0.347338, 0.004832],
[1.80, 0.398324, 0.004980],
[2.12, 0.438086, 0.005065],
[2.56, 0.505700, 0.005113]]
)

NN_DS1_v1_j0 = np.array(
[[0.94, 0.111539, 0.003168],
[1.18, 0.194144, 0.003995],
[1.29, 0.223641, 0.004212],
[1.55, 0.284100, 0.004571],
[1.80, 0.334296, 0.004791],
[2.12, 0.370278, 0.004919],
[2.56, 0.434569, 0.005072]]
)

NN_DS1_v1_j1 = np.array(
[[0.94, 0.121319, 0.003284],
[1.18, 0.204111, 0.004066],
[1.29, 0.233072, 0.004266],
[1.55, 0.299959, 0.004639],
[1.80, 0.341068, 0.004804],
[2.12, 0.389490, 0.004950],
[2.56, 0.434588, 0.005061]]
)

NN_DS1_v1_j2 = np.array(
[[0.94, 0.120113, 0.003267],
[1.18, 0.219264, 0.004171],
[1.29, 0.239458, 0.004307],
[1.55, 0.309662, 0.004670],
[1.80, 0.350973, 0.004843],
[2.12, 0.400083, 0.004980],
[2.56, 0.451374, 0.005078]]
)

NN_DS1_v1_j4 = np.array(
[[0.94, 0.129499, 0.003376],
[1.18, 0.218571, 0.004175],
[1.29, 0.248824, 0.004372],
[1.55, 0.320828, 0.004726],
[1.80, 0.368112, 0.004895],
[2.12, 0.417409, 0.005020],
[2.56, 0.472173, 0.005111]]
)

NN_DS1_v1_j6 = np.array(
[[0.94, 0.147690, 0.003571],
[1.18, 0.245873, 0.004347],
[1.29, 0.279077, 0.004543],
[1.55, 0.351351, 0.004830],
[1.80, 0.409743, 0.004996],
[2.12, 0.458923, 0.005082],
[2.56, 0.513411, 0.005126]]
)

NN_DS1_v1_j8 = np.array(
[[0.94, 0.191722, 0.003965],
[1.18, 0.298956, 0.004631],
[1.29, 0.339752, 0.004793],
[1.55, 0.405811, 0.004993],
[1.80, 0.465515, 0.005088],
[2.12, 0.518519, 0.005125],
[2.56, 0.565213, 0.005088]]
)

NN_DS1_v2_j0 = np.array(
[[0.94, 0.207144, 0.004129],
[1.18, 0.261990, 0.004500],
[1.29, 0.281260, 0.004622],
[1.55, 0.341170, 0.005102],
[1.80, 0.395409, 0.005064],
[2.12, 0.432535, 0.005158],
[2.56, 0.483571, 0.005230]]
)

NN_DS1_v2_j1 = np.array(
[[0.94, 0.242503, 0.004381],
[1.18, 0.292862, 0.004666],
[1.29, 0.305270, 0.004742],
[1.55, 0.357135, 0.004947],
[1.80, 0.402199, 0.005090],
[2.12, 0.440970, 0.005155],
[2.56, 0.490919, 0.005213]]
)

NN_DS1_v2_j2 = np.array(
[[0.94, 0.255816, 0.004447],
[1.18, 0.302233, 0.004713],
[1.29, 0.324225, 0.004799],
[1.55, 0.378079, 0.005007],
[1.80, 0.420268, 0.005093],
[2.12, 0.446815, 0.005161],
[2.56, 0.504408, 0.005216]]
)

NN_DS1_v2_j3 = np.array(
[[0.94, 0.275251, 0.004546],
[1.18, 0.323985, 0.004787],
[1.29, 0.339714, 0.004857],
[1.55, 0.392838, 0.005034],
[1.80, 0.431440, 0.005118],
[2.12, 0.476918, 0.005181],
[2.56, 0.518260, 0.005217]]
)

NN_DS1_v2_j4 = np.array(
[[0.94, 0.291208, 0.004632],
[1.18, 0.344392, 0.004876],
[1.29, 0.374670, 0.004971],
[1.55, 0.410484, 0.005083],
[1.80, 0.447929, 0.005145],
[2.12, 0.492478, 0.005201],
[2.56, 0.533005, 0.005216]]
)

NN_DS1_v2_j8 = np.array(
[[0.94, 0.438595, 0.005128],
[1.18, 0.504849, 0.005279],
[1.29, 0.524061, 0.005182],
[1.55, 0.572026, 0.005261],
[1.80, 0.602373, 0.005129],
[2.12, 0.641654, 0.005015],
[2.56, 0.684954, 0.004881]]
)

# The v=0, j=3 results are replaced with v=1, j=2 for a DS1 molecule beam, i.e., v and j were sampled according to TN=1060 K.
NN_DS1_v1_j2_laser = np.array(
[[0.94, 0.027783, 0.001249],
[1.18, 0.067788, 0.002321],
[1.29, 0.084200, 0.002665],
[1.55, 0.162603, 0.003809],
[1.80, 0.226407, 0.004400],
[2.12, 0.291494, 0.004824],
[2.56, 0.362978, 0.005148]]
)

# The v=0, j=7 results are replaced with v=1, j=8 for a DS1 molecule beam, i.e., v and j were sampled according to TN=1060 K.
NN_DS1_v1_j8_laser = np.array(
[[0.94, 0.025267, 0.001060],
[1.18, 0.059588, 0.002084],
[1.29, 0.076841, 0.002436],
[1.55, 0.154758, 0.003606],
[1.80, 0.218277, 0.004194],
[2.12, 0.291099, 0.004669],
[2.56, 0.358596, 0.004969]]
)


# The v=0, j=3 results are replaced with v=2, j=2 for a DS1 molecule beam, i.e., v and j were sampled according to TN=1060 K.
NN_DS1_v2_j2_laser = np.array(
[[0.94, 0.056661, 0.001451],
[1.18, 0.087469, 0.002419],
[1.29, 0.102980, 0.002748],
[1.55, 0.178194, 0.003865],
[1.80, 0.240586, 0.004436],
[2.12, 0.302183, 0.004850],
[2.56, 0.375000, 0.005164]]
)

# The v=0, j=7 results are replaced with v=2, j=8 for a DS1 molecule beam, i.e., v and j were sampled according to TN=1060 K.
NN_DS1_v2_j8_laser = np.array(
[[0.94, 0.052217, 0.001137],
[1.18, 0.080923, 0.002114],
[1.29, 0.096243, 0.002445],
[1.55, 0.171604, 0.003607],
[1.80, 0.232024, 0.004180],
[2.12, 0.303537, 0.004643],
[2.56, 0.370167, 0.004936]]
)

NN_DS2 = np.array(
[[0.69, 0.001201, 0.000346],
[1.03, 0.024031, 0.001532],
[1.27, 0.066934, 0.002503],
[1.47, 0.104422, 0.003066],
[1.76, 0.189336, 0.003930],
[2.56, 0.335325, 0.003345]]
)

NN_DS2_nopara = np.array(
[[0.49, 0.000100, 0.000100],
[0.69, 0.001001, 0.000316],
[1.03, 0.011807, 0.001080],
[1.27, 0.046006, 0.002097],
[1.47, 0.089130, 0.002855],
[1.76, 0.171664, 0.003783],
[2.56, 0.335325, 0.003345]]
)

NN_DS2_v0 = np.array(
[[0.49, 0., 0.00010216],
[0.69, 0.0002, 0.000141],
[1.03, 0.006403, 0.000798],
[1.27, 0.040773, 0.001979],
[1.47, 0.091757, 0.002891],
[1.76, 0.173029, 0.003796],
[2.56, 0.332010, 0.004751]]
)

NN_DS2_v0_nopara = np.array(
[[0.49, 0., 0.00010216],
[0.69, 0.000300, 0.000173],
[1.03, 0.005603, 0.000747],
[1.27, 0.042777, 0.002025],
[1.47, 0.094932, 0.002936],
[1.76, 0.172438, 0.003790],
[2.56, 0.332010, 0.004751]]
)

NN_DS2_v0_j0 = np.array(
[[0.49, 0., 0.00010216],
[0.69, 0.0002, 0.000142],
[1.03, 0.013732, 0.001165],
[1.27, 0.054631, 0.002275],
[1.47, 0.084212, 0.002782],
[1.76, 0.148338, 0.003567],
[2.56, 0.297111, 0.004601]]
)

NN_DS2_v0_j0_nopara = np.array(
[[0.49, 0., 0.00010216],
[0.69, 0., 0.00010216],
[1.03, 0.003808, 0.000617],
[1.27, 0.035396, 0.001850],
[1.47, 0.080574, 0.002726],
[1.76, 0.153707, 0.003617],
[2.56, 0.297111, 0.004601]]
)

NN_DS2_v0_j4 = np.array(
[[0.49, 0., 0.00010216],
[0.69, 0.000300, 0.000173],
[1.03, 0.013707, 0.001163],
[1.27, 0.047581, 0.002131],
[1.47, 0.094923, 0.002936],
[1.76, 0.165611, 0.003726],
[2.56, 0.312247, 0.004666]]
)

NN_DS2_v0_j8 = np.array(
[[0.49, 0., 0.00010216],
[0.69, 0.000300, 0.000173],
[1.03, 0.027533, 0.001637],
[1.27, 0.079000, 0.002703],
[1.47, 0.137987, 0.003459],
[1.76, 0.219596, 0.004161],
[2.56, 0.366755, 0.004870]]
)

NN_DS2_v1 = np.array(
[[0.49, 0.002805, 0.000529],
[0.69, 0.045816, 0.002098],
[1.03, 0.191023, 0.003961],
[1.27, 0.274101, 0.004508],
[1.47, 0.325462, 0.004748],
[1.76, 0.389937, 0.004952],
[2.56, 0.505700, 0.005113]]
)

NN_DS2_v1_nopara = np.array(
[[0.49, 0.003507, 0.000592],
[0.69, 0.041541, 0.002001],
[1.03, 0.181431, 0.003882],
[1.27, 0.273593, 0.004510],
[1.47, 0.330461, 0.004764],
[1.76, 0.394121, 0.004971],
[2.56, 0.505700, 0.005113]]
)

NN_DS2_v1_j0 = np.array(
[[0.49, 0.005412, 0.000734],
[0.69, 0.041256, 0.001995],
[1.03, 0.175458, 0.003838],
[1.27, 0.231155, 0.004266],
[1.47, 0.288082, 0.004581],
[1.76, 0.328912, 0.004764],
[2.56, 0.432503, 0.005068]]
)

NN_DS2_v1_j0_nopara = np.array(
[[0.49, 0.001003, 0.000317],
[0.69, 0.028896, 0.001681],
[1.03, 0.144947, 0.003548],
[1.27, 0.218847, 0.004269],
[1.47, 0.266509, 0.004488],
[1.76, 0.319711, 0.004738],
[2.56, 0.434569, 0.005072]]
)

NN_DS2_v1_j2_nopara = np.array(
[[0.49, 0.001603, 0.000400],
[0.69, 0.033829, 0.001811],
[1.03, 0.161993, 0.003707],
[1.27, 0.246372, 0.004356],
[1.47, 0.294656, 0.004599],
[1.76, 0.355345, 0.004862],
[2.56, 0.451374, 0.005078]]
)

NN_DS2_v1_j4_nopara = np.array(
[[0.49, 0.001203, 0.000347],
[0.69, 0.036669, 0.001884],
[1.03, 0.170662, 0.003792],
[1.27, 0.241901, 0.004329],
[1.47, 0.301586, 0.004642],
[1.76, 0.362867, 0.004886],
[2.56, 0.472173, 0.005111]]
)

NN_DS2_v1_j6_nopara = np.array(
[[0.49, 0.003005, 0.000548],
[0.69, 0.047509, 0.002134],
[1.03, 0.182684, 0.003888],
[1.27, 0.269411, 0.004478],
[1.47, 0.331828, 0.004771],
[1.76, 0.406660, 0.004995],
[2.56, 0.513411, 0.005126]]
)

NN_DS2_v1_j8_nopara = np.array(
[[0.49, 0.006005, 0.000773],
[0.69, 0.063887, 0.002451],
[1.03, 0.237634, 0.004298],
[1.27, 0.320460, 0.004729],
[1.47, 0.390385, 0.004955],
[1.76, 0.455254, 0.005077],
[2.56, 0.565213, 0.005088]]
)

NN_DS2_v2_j0_nopara = np.array(
[[0.49, 0.060846, 0.002407],
[0.69, 0.141666, 0.003538],
[1.03, 0.233250, 0.004320],
[1.27, 0.290987, 0.004658],
[1.47, 0.327881, 0.004838],
[1.76, 0.379240, 0.005011],
[2.56, 0.483571, 0.005230]]
)

NN_DS2_v2_j2_nopara = np.array(
[[0.49, 0.070334, 0.002576],
[0.69, 0.179751, 0.003895],
[1.03, 0.274412, 0.004553],
[1.27, 0.327468, 0.004817],
[1.47, 0.365379, 0.004955],
[1.76, 0.406528, 0.005081],
[2.56, 0.504408, 0.005216]]
)

NN_DS2_v2_j3_nopara = np.array(
[[0.49, 0.081525, 0.002762],
[0.69, 0.194270, 0.004016],
[1.03, 0.293152, 0.004640],
[1.27, 0.341763, 0.004859],
[1.47, 0.376210, 0.004997],
[1.76, 0.429885, 0.005118],
[2.56, 0.518260, 0.005217]]
)

NN_DS2_v2_j4_nopara = np.array(
[[0.49, 0.093272, 0.002936],
[0.69, 0.219218, 0.004203],
[1.03, 0.315861, 0.004755],
[1.27, 0.369061, 0.004964],
[1.47, 0.405967, 0.005069],
[1.76, 0.436038, 0.005137],
[2.56, 0.533005, 0.005216]]
)

NN_DS2_v2_j6_nopara = np.array(
[[0.49, 0.130623, 0.003423],
[0.69, 0.269859, 0.004529],
[1.03, 0.371719, 0.004972],
[1.27, 0.421396, 0.005110],
[1.47, 0.469612, 0.005181],
[1.76, 0.514233, 0.005210],
[2.56, 0.603058, 0.005131]]
)

NN_DS2_v2_j8_nopara = np.array(
[[0.49, 0.166960, 0.003797],
[0.69, 0.334105, 0.004839],
[1.03, 0.468292, 0.005160],
[1.27, 0.533824, 0.005190],
[1.47, 0.569323, 0.005174],
[1.76, 0.599014, 0.005129],
[2.56, 0.684954, 0.004881]]
)

# 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.
NN_DS2_v1_j2_laser_nopara = np.array(
[[0.49, 0.00040490742363798854, 0.00043628197684872513],
[0.69, 0.0083282169237773943, 0.0007018391972253283],
[1.03, 0.043055939976946754, 0.0017377428964113024],
[1.27, 0.089633503885980348, 0.0027690452617736231],
[1.47, 0.1402663408820409, 0.0035336003616661168],
[1.76, 0.21550525496206679, 0.0043039619232780411],
[2.56, 0.362978, 0.005148]]
)

# 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.
NN_DS2_v1_j8_laser_nopara = np.array(
[[0.49, 0.00076388634559960511, 0.00017336269104156651],
[0.69, 0.0083465040981153763, 0.00061128620860095308],
[1.03, 0.036628719219556127, 0.0014902855385935325],
[1.27, 0.078534144366592271, 0.0025144736559710203],
[1.47, 0.13052657183007485, 0.0033106777659066364],
[1.76, 0.20669971998614201, 0.0040983642090755305],
[2.56, 0.358596, 0.004969]]
)

# 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, HD-NNP
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]]
)

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]]
)