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

########## CHD3

# E, reaction probability, error
AIMD_LO_CHD3_migliorini_2017 = np.array(
[[81.7, 0.023, 0.005],
[89.3, 0.030, 0.005],
[97.4, 0.060, 0.011],
[102.5, 0.086, 0.013],
[111.9, 0.094, 0.013],
[120.1, 0.140, 0.016]]
)

RPH_LO_500_CHD3 = np.array(
[[81.7, 0.219E-01],
[89.3, 0.385E-01],
[97.4, 0.629E-01],
[102.5, 0.826E-01],
[111.9, 0.125E-00],
[120.1, 0.168E-00]]
)

QCT_LO_CHD3_migliorini_2017 = np.array(
[[81.7, 0.022800, 0.002111],
[89.3, 0.034000, 0.002563],
[97.4, 0.054200, 0.003202],
[102.5, 0.066800, 0.003531],
[111.9, 0.108000, 0.004389],
[120.1, 0.146029, 0.004995]]
)

TRPMD_beta1000K_CHD3_migliorini_2017 = np.array(
[[81.7, 0.012183, 0.001563],
[89.3, 0.026202, 0.002548],
[97.4, 0.040961, 0.003647],
[102.5, 0.070040, 0.005135],
[111.9, 0.092213, 0.006549],
[120.1, 0.120381, 0.011234]]
)

Exp_LO_CHD3_migliorini_2017 = np.array(
[[81.7, 0.023, 0.005],
[89.3, 0.036, 0.005],
[97.4, 0.054, 0.005],
[102.5, 0.071, 0.006],
[111.9, 0.100, 0.008],
[120.1, 0.130, 0.010]]
)

AIMD_v1_CHD3_migliorini_2017 = np.array(
[[ 60.7140, 0.025, 0.005],
[ 71.4091, 0.048, 0.010],
[ 81.9073, 0.066, 0.011],
[ 92.2492, 0.084, 0.012],
[104.5798, 0.138, 0.015]]
)

# NOTE THAT THESE VALUES STILL NEED TO BE OBTAINED WITH THE HDNNP; THEY ARE A COPY OF THE AIMD RESULTS
QCT_v1_CHD3_migliorini_2017 = np.array(
[[ 60.7140, 0.025, 0.005],
[ 71.4091, 0.048, 0.010],
[ 81.9073, 0.066, 0.011],
[ 92.2492, 0.084, 0.012],
[104.5798, 0.138, 0.015]]
)

Exp_v1_CHD3_migliorini_2017 = np.array(
[[60.714, 0.032, 0.007],
[71.4091, 0.047, 0.008],
[81.9073, 0.069, 0.016],
[92.2492, 0.078, 0.016],
[104.5798, 0.113, 0.029]]
)

######### CH4

QCT_LO_CH4_bisson_2007 = np.array(
#[[9.684210526315791, , ],
#[22.175438596491226, , ],
[[32.9122807017544, 0.001100, 0.000331],
[43.47368421052634, 0.003600, 0.000599],
[54.14035087719297, 0.011200, 0.001052],
[63.68421052631582, 0.020500, 0.001417]]
)

TRPMD_0323K_MS_beta1000K_CH4_bisson_2007 = np.array(
#[[9.7, , ],
#[22., , ],
[[32.5, 0.000211, 0.000149],
[44.5, 0.000701, 0.000265],
[53.2, 0.002137, 0.000329],
[63.4, 0.006018, 0.002449]]
)

TRPMD_0323K_MS_beta500K_CH4_bisson_2007 = np.array(
#[[9.7, , ],
#[22., , ],
[[32.5, 0.000128, 0.000091],
[44.5, 0.001334, 0.000370],
[53.2, 0.001224, 0.000707]]
#[[63.4, , ]]
)

QCT_2v3_CH4_bisson_2007 = np.array(
#[[9.684210526315791, , ],
#[22.175438596491226, , ],
[[32.9122807017544, 0.025903, 0.001589],
[43.47368421052634, 0.045536, 0.002086],
[54.14035087719297, 0.075598, 0.002645],
[63.68421052631582, 0.109085, 0.003120]]
)

#  LO results including GS, first, second and third polyad
# Ei(eV)  Ei(kJ/m)   Tn    Z(T)        S0
RPH_GS_5_CH4 = np.array(
 [[0.1005,     9.70,  323.00,  1.0104,  0.16021E-11],
 [0.2277,    21.97,  323.00,  1.0104,  0.28302E-09],
 [0.3363,    32.45,  323.00,  1.0104,  0.12805E-07],
 [0.4606,    44.44,  323.00,  1.0104,  0.13825E-05],
 [0.5506,    53.12,  323.00,  1.0104,  0.34742E-04],
 [0.6563,    63.32,  373.00,  1.0240,  0.13358E-02]]
)

RPH_GS_500_CH4 = np.array(
 [[0.1005,     9.70,  323.00,  1.0104,  0.29310E-07],
 [0.2277,    21.97,  323.00,  1.0104,  0.18104E-05],
 [0.3363,    32.45,  323.00,  1.0104,  0.34338E-04],
 [0.4606,    44.44,  323.00,  1.0104,  0.52056E-03],
 [0.5506,    53.12,  323.00,  1.0104,  0.23539E-02],
 [0.6563,    63.32,  373.00,  1.0240,  0.93918E-02]]
)

RPH_GS_600_CH4 = np.array(
 [[0.1005,     9.70,  323.00,  1.0104,  0.12687E-06], 
 [0.2277,    21.97,  323.00,  1.0104,  0.54730E-05],
 [0.3363,    32.45,  323.00,  1.0104,  0.75801E-04],
 [0.4606,    44.44,  323.00,  1.0104,  0.84386E-03],
 [0.5506,    53.12,  323.00,  1.0104,  0.32379E-02],
 [0.6563,    63.32,  373.00,  1.0240,  0.11225E-01]]
)

# 2v3, RPH with SRP, velocity averaged
# E(kj)        S0
RPH_2v3_5K_CH4 = np.array(
[[9.70,   0.81064E-05],
[21.97,   0.39582E-03],
[32.45,   0.24731E-02],
[44.44,   0.14242E-01],
[53.12,   0.32921E-01],
[63.32,   0.58114E-01]]
)

RPH_2v3_300K_CH4 = np.array(
[[9.70,   0.13243E-03],
[21.97,   0.12610E-02],
[32.45,   0.56581E-02],
[44.44,   0.20098E-01],
[53.12,   0.38128E-01],
[63.32,   0.67286E-01]]
)

RPH_2v3_500K_CH4 = np.array(
[[9.70,   0.33358E-03],
[21.97,   0.22735E-02],
[32.45,   0.81028E-02],
[44.44,   0.23961E-01],
[53.12,   0.42914E-01],
[63.32,   0.73168E-01]]
)

RPH_2v3_600K_CH4 = np.array(
[[9.70,   0.47868E-03],
[21.97,   0.28576E-02],
[32.45,   0.93281E-02],
[44.44,   0.25909E-01],
[53.12,   0.45342E-01],
[63.32,   0.75922E-01]]
)

# En	S0	sigma_E	sigma_S0
Exp_LO_CH4_bisson_2007 = np.array(
[[9.7,	7.05E-07,	1.61E-01,	1.68E-07],
[22.,	3.98E-06,	2.42E-01,	9.71E-07],
[32.5,	9.66E-05,	3.88E-01,	2.32E-05],
[44.5,	4.18E-04,	8.34E-01,	1.00E-04],
[53.2,	1.65E-03,	9.59E-01,	3.46E-04],
[63.4,	9.40E-03,	5.49E-01,	2.26E-03]]
)

Exp_2v3_CH4_bisson_2007 = np.array(
[[9.7,	1.59E-04,	8.78E-02,	2.34E-05],
[22.,	1.04E-03,	1.83E-01,	1.56E-04],
[32.5,	5.64E-03,	2.78E-01,	8.20E-04],
[44.5,	9.18E-03,	2.56E-01,	1.35E-03],
[53.2,	3.14E-02,	4.68E-01,	5.05E-03],
[63.4,	3.36E-02,	5.42E-01,	6.63E-03]]
)