"""
The solvent_config for SPC water with 216 SPC molecules. This is the template
for all 3 body water models.
"""
# Module name is acronym
#pylint: disable=invalid-name
import numpy as np
from MDMC.MD.solvents._solvent_config import SolventConfig
[docs]
class SPCConfig(SolventConfig):
"""
The SPC ``SolventConfig``, which uses the SPC216 dict
"""
def __str__(self):
return 'SPC'
@property
def _solvent_config_dict(self):
return SPC216
# SPC water
# 216H2O,WATJP01,SPC216,SPC-MODEL,300K,BOX(M)=1.86206NM,WFVG,MAR. 1984
# Raw data from: https://raw.githubusercontent.com/gromacs/gromacs/
# 4fa824292a67e2adeba57d528a6f5f19c1f1bd22/share/top/spc216.gro
# Coordinates translated to an origin of [0, 0, 0] so all coordinates are
# positive, and scaled by a factor of 10 to convert to Ang.
SPC216 = {'description': 'SPC water coordinates, 216 molecules, 300K, 18.6206'
' Ang box',
'box_dimensions': np.array([18.6206, 18.6206, 18.6206]),
'atom_types': {'H': 1,
'O': 2},
'bonded_interactions': [['Bond', ('H1', 'O'), ('H2', 'O')],
['BondAngle', ('H1', 'O', 'H2')]],
'nonbonded_interactions': [['Coulombic', 1],
['Coulombic', 2],
['Dispersion', (2, 2)]],
'constrained': True,
'molecule_name': 'water',
'molecules': {1: {'H1': np.array([11.18, 16.27, 11.34]),
'H2': np.array([12.12, 15.9, 10.05]),
'O': np.array([12.11, 16.29, 10.97])},
2: {'H1': np.array([12.41, 12.59, 2.1]),
'H2': np.array([11.18, 12.31, 1.06]),
'O': np.array([12.06, 12.76, 1.18])},
3: {'H1': np.array([9.18, 14.12, 16.7]),
'H2': np.array([9.72, 12.96, 15.68]),
'O': np.array([10., 13.69, 16.31])},
4: {'H1': np.array([14.57, 4.07, 2.5]),
'H2': np.array([15.61, 5.03, 3.31]),
'O': np.array([15.5, 4.14, 2.87])},
5: {'H1': np.array([6.17, 6.34, 17.68]),
'H2': np.array([6.15, 6.6, 16.07]),
'O': np.array([6.74, 6.5, 16.87])},
6: {'H1': np.array([8.95, 17.13, 18.4]),
'H2': np.array([9.13, 15.65, 19.06]),
'O': np.array([8.62, 16.19, 18.4])},
7: {'H1': np.array([3.11, 17.82, 16.76]),
'H2': np.array([1.94, 17.3, 17.77]),
'O': np.array([2.54, 17.04, 17.01])},
8: {'H1': np.array([8.62, 15.95, 11.16]),
'H2': np.array([8.44, 15.27, 12.64]),
'O': np.array([8.74, 16.08, 12.15])},
9: {'H1': np.array([16.71, 3., 2.05]),
'H2': np.array([17.83, 3.7, 1.09]),
'O': np.array([17.49, 2.83, 1.45])},
10: {'H1': np.array([18.27, 18.75, 10.1]),
'H2': np.array([18.53, 18.35, 8.54]),
'O': np.array([18.31, 17.99, 9.45])},
11: {'H1': np.array([17.35, 1.35, 17.19]),
'H2': np.array([15.93, 2.08, 16.87]),
'O': np.array([16.66, 1.51, 16.49])},
12: {'H1': np.array([17.27, 3.79, 9.39]),
'H2': np.array([15.81, 3.31, 8.84]),
'O': np.array([16.67, 3., 9.25])},
13: {'H1': np.array([12.38, 5.43, 1.3]),
'H2': np.array([13.74, 6.32, 1.26]),
'O': np.array([13.16, 5.74, 1.83])},
14: {'H1': np.array([6.03, 7.38, 4.87]),
'H2': np.array([5.63, 5.9, 5.43]),
'O': np.array([5.79, 6.44, 4.61])},
15: {'H1': np.array([15.01, 13.37, 6.3]),
'H2': np.array([13.38, 13.35, 6.25]),
'O': np.array([14.19, 13.93, 6.21])},
16: {'H1': np.array([6.48, 14.94, 16.71]),
'H2': np.array([7.73, 15.16, 17.74]),
'O': np.array([7.22, 14.48, 17.21])},
17: {'H1': np.array([12.46, 9.29, 15.21]),
'H2': np.array([12.56, 8.52, 13.77]),
'O': np.array([12.12, 8.52, 14.67])},
18: {'H1': np.array([2.93, 4.8, 9.]),
'H2': np.array([1.98, 3.93, 8.01]),
'O': np.array([2.46, 4.8, 8.12])},
19: {'H1': np.array([11.85, 6.69, 15.22]),
'H2': np.array([11.4, 5.19, 15.67]),
'O': np.array([12.11, 5.73, 15.22])},
20: {'H1': np.array([12.35, 1.46, 19.22]),
'H2': np.array([11.66, 2.94, 19.25]),
'O': np.array([12.21, 2.3, 18.7])},
21: {'H1': np.array([15.09, 9.08, 5.96]),
'H2': np.array([16.29, 10.17, 5.87]),
'O': np.array([16.01, 9.25, 5.61])},
22: {'H1': np.array([15.94, 1.87, 10.53]),
'H2': np.array([16.33, 1.16, 11.95]),
'O': np.array([15.87, 1.03, 11.07])},
23: {'H1': np.array([6.95, 11.82, 5.3]),
'H2': np.array([7.1, 11.61, 6.91]),
'O': np.array([7.13, 11.15, 6.02])},
24: {'H1': np.array([10.58, 15.56, 15.64]),
'H2': np.array([11.02, 16.98, 16.31]),
'O': np.array([11.03, 16.44, 15.47])},
25: {'H1': np.array([10.15, 8.77, 14.23]),
'H2': np.array([9.91, 9.96, 13.14]),
'O': np.array([9.61, 9.06, 13.43])},
26: {'H1': np.array([9.89, 6.39, 11.22]),
'H2': np.array([9.75, 7.93, 11.76]),
'O': np.array([10.08, 7.35, 11.01])},
27: {'H1': np.array([9.03, 18.94, 16.04]),
'H2': np.array([8., 19.88, 15.21]),
'O': np.array([8.08, 19.23, 15.96])},
28: {'H1': np.array([8.46, 2.49, 13.64]),
'H2': np.array([7.74, 2.94, 15.04]),
'O': np.array([7.6, 2.47, 14.16])},
29: {'H1': np.array([11.82, 17.25, 7.64]),
'H2': np.array([10.81, 18.35, 6.97]),
'O': np.array([10.94, 17.38, 7.19])},
30: {'H1': np.array([15.45, 4.17, 17.19]),
'H2': np.array([15.71, 5.47, 16.23]),
'O': np.array([15.94, 5.04, 17.1])},
31: {'H1': np.array([4.49, 2.94, 4.87]),
'H2': np.array([4.64, 3.72, 6.3]),
'O': np.array([4.12, 3.67, 5.45])},
32: {'H1': np.array([18.3, 10.96, 14.77]),
'H2': np.array([16.9, 10.13, 14.92]),
'O': np.array([17.9, 10.05, 14.86])},
33: {'H1': np.array([12.67, 0.7, 3.72]),
'H2': np.array([11.05, 0.5, 3.67]),
'O': np.array([11.78, 1.15, 3.86])},
34: {'H1': np.array([5.81, 0.62, 11.87]),
'H2': np.array([6.92, 1.56, 12.6]),
'O': np.array([6.44, 1.38, 11.74])},
35: {'H1': np.array([3.3, 9.91, 6.62]),
'H2': np.array([3.13, 8.36, 7.08]),
'O': np.array([3.06, 9.31, 7.38])},
36: {'H1': np.array([13.69, 13.23, 9.29]),
'H2': np.array([12.1, 12.91, 9.51]),
'O': np.array([12.98, 12.52, 9.23])},
37: {'H1': np.array([5.26, 5.62, 1.55]),
'H2': np.array([5.7, 4.68, 0.29]),
'O': np.array([5.85, 5.56, 0.75])},
38: {'H1': np.array([7.45, 8.3, 14.68]),
'H2': np.array([7.68, 7.79, 16.21]),
'O': np.array([7.86, 8.53, 15.56])},
39: {'H1': np.array([16.03, 17.99, 11.86]),
'H2': np.array([15.01, 17.63, 13.08]),
'O': np.array([15.79, 17.3, 12.54])},
40: {'H1': np.array([3.14, 12.96, 0.53]),
'H2': np.array([4.62, 12.92, 1.22]),
'O': np.array([4., 13.46, 0.66])},
41: {'H1': np.array([7.84, 8.47, 12.94]),
'H2': np.array([6.74, 7.77, 11.96]),
'O': np.array([6.95, 8.01, 12.91])},
42: {'H1': np.array([17.41, 16.03, 6.76]),
'H2': np.array([17.37, 15.51, 5.21]),
'O': np.array([17.88, 16.06, 5.87])},
43: {'H1': np.array([4.93, 15.13, 8.84]),
'H2': np.array([5.91, 14.08, 8.05]),
'O': np.array([5.13, 14.7, 7.96])},
44: {'H1': np.array([1.38, 18.07, 6.65]),
'H2': np.array([0.36, 19.25, 6.19]),
'O': np.array([0.92, 18.91, 6.94])},
45: {'H1': np.array([0.33, 14.45, 4.18]),
'H2': np.array([0.76, 13.6, 2.85]),
'O': np.array([1.1, 14.11, 3.64])},
46: {'H1': np.array([1.86, 16.98, 15.09]),
'H2': np.array([0.75, 16.51, 13.99]),
'O': np.array([1.6, 17.02, 14.13])},
47: {'H1': np.array([11.56, 18., 17.82]),
'H2': np.array([10.33, 19.07, 17.94]),
'O': np.array([10.57, 18.12, 17.73])},
48: {'H1': np.array([11.01, 9.45, 7.92]),
'H2': np.array([10.25, 9.96, 6.57]),
'O': np.array([11.11, 9.6, 6.93])},
49: {'H1': np.array([19.05, 14.12, 11.3]),
'H2': np.array([18.65, 12.55, 11.5]),
'O': np.array([18.46, 13.49, 11.79])},
50: {'H1': np.array([8.12, 16.75, 9.17]),
'H2': np.array([8.36, 15.18, 8.8]),
'O': np.array([8.38, 15.86, 9.53])},
51: {'H1': np.array([5.64, 2.54, 14.81]),
'H2': np.array([4.32, 3.5, 14.73]),
'O': np.array([4.81, 2.83, 15.29])},
52: {'H1': np.array([15.26, 11.92, 19.69]),
'H2': np.array([15.33, 12.93, 18.4]),
'O': np.array([15.31, 11.97, 18.69])},
53: {'H1': np.array([0.81, 6.67, 14.09]),
'H2': np.array([1.23, 6.15, 15.59]),
'O': np.array([1.27, 5.95, 14.61])},
54: {'H1': np.array([13.82, 8.54, 18.43]),
'H2': np.array([13.97, 10.17, 18.34]),
'O': np.array([13.32, 9.4, 18.37])},
55: {'H1': np.array([8.52, 1.9, 17.81]),
'H2': np.array([8.62, 2.16, 19.42]),
'O': np.array([9.14, 2.05, 18.57])},
56: {'H1': np.array([3.52, 6.12, 6.92]),
'H2': np.array([2.94, 6.63, 5.48]),
'O': np.array([3.46, 6.89, 6.28])},
57: {'H1': np.array([13.84, 1.21, 11.84]),
'H2': np.array([12.75, 0., 11.77]),
'O': np.array([13.02, 0.82, 12.26])},
58: {'H1': np.array([5.72, 16.71, 17.87]),
'H2': np.array([6.57, 17.95, 17.25]),
'O': np.array([5.77, 17.36, 17.12])},
59: {'H1': np.array([13.92, 4.06, 7.63]),
'H2': np.array([13.79, 3.87, 9.25]),
'O': np.array([14.42, 4.05, 8.49])},
60: {'H1': np.array([1.7, 8.53, 12.71]),
'H2': np.array([2.61, 8.71, 11.36]),
'O': np.array([2.3, 9.15, 12.21])},
61: {'H1': np.array([11.03, 13.46, 6.07]),
'H2': np.array([11.73, 12.37, 7.06]),
'O': np.array([11.83, 12.86, 6.2])},
62: {'H1': np.array([7.19, 6.1, 10.55]),
'H2': np.array([6.75, 4.53, 10.53]),
'O': np.array([7.51, 5.16, 10.65])},
63: {'H1': np.array([14.78, 9.21, 13.93]),
'H2': np.array([15.21, 8.75, 12.42]),
'O': np.array([14.45, 8.82, 13.07])},
64: {'H1': np.array([4.95, 10.71, 13.2]),
'H2': np.array([6.18, 11.24, 14.14]),
'O': np.array([5.19, 11.08, 14.1])},
65: {'H1': np.array([12.87, 8.59, 4.13]),
'H2': np.array([12.14, 8.91, 2.7]),
'O': np.array([12.3, 9.24, 3.63])},
66: {'H1': np.array([1.39, 7.8, 19.09]),
'H2': np.array([0.1, 7.97, 18.11]),
'O': np.array([0.59, 8.37, 18.88])},
67: {'H1': np.array([14.08, 16.11, 14.61]),
'H2': np.array([12.69, 16.9, 14.97]),
'O': np.array([13.63, 17.01, 14.64])},
68: {'H1': np.array([6.61, 12.6, 9.43]),
'H2': np.array([5.94, 11.54, 8.39]),
'O': np.array([6.66, 12.23, 8.51])},
69: {'H1': np.array([16.93, 11.01, 11.08]),
'H2': np.array([15.64, 11.06, 10.08]),
'O': np.array([15.95, 11.23, 11.01])},
70: {'H1': np.array([18.29, 12.04, 9.14]),
'H2': np.array([16.89, 12.84, 9.36]),
'O': np.array([17.62, 12.65, 8.71])},
71: {'H1': np.array([18.46, 6.28, 2.23]),
'H2': np.array([19.3, 5.84, 3.56]),
'O': np.array([18.69, 6.53, 3.17])},
72: {'H1': np.array([4.98, 15.48, 6.4]),
'H2': np.array([4.95, 16.87, 5.56]),
'O': np.array([4.7, 15.91, 5.55])},
73: {'H1': np.array([18.74, 5.55, 18.66]),
'H2': np.array([17.13, 5.43, 18.37]),
'O': np.array([17.84, 5.41, 19.08])},
74: {'H1': np.array([18.78, 14.95, 17.87]),
'H2': np.array([19.51, 14.22, 19.14]),
'O': np.array([19.03, 15.04, 18.83])},
75: {'H1': np.array([14.39, 10.66, 15.54]),
'H2': np.array([15.23, 11.48, 14.41]),
'O': np.array([15.2, 10.65, 14.96])},
76: {'H1': np.array([5.85, 2.51, 10.82]),
'H2': np.array([4.61, 3.54, 10.55]),
'O': np.array([5.53, 3.27, 10.25])},
77: {'H1': np.array([13.27, 11.2, 11.34]),
'H2': np.array([13.4, 9.71, 12.]),
'O': np.array([12.78, 10.36, 11.55])},
78: {'H1': np.array([0.06, 12.78, 13.86]),
'H2': np.array([1.53, 12.35, 14.45]),
'O': np.array([0.54, 12.37, 14.64])},
79: {'H1': np.array([1.15, 16.23, 5.89]),
'H2': np.array([2.76, 16.31, 5.62]),
'O': np.array([1.95, 16.84, 5.86])},
80: {'H1': np.array([3.67, 7.83, 17.12]),
'H2': np.array([4.14, 7.09, 18.5]),
'O': np.array([3.46, 7.09, 17.77])},
81: {'H1': np.array([13.69, 2.64, 17.9]),
'H2': np.array([14.14, 2.63, 16.32]),
'O': np.array([14.4, 2.91, 17.25])},
82: {'H1': np.array([4.34, 10., 15.11]),
'H2': np.array([3.4, 9.88, 16.45]),
'O': np.array([3.9, 9.36, 15.75])},
83: {'H1': np.array([1.1, 16.32, 9.61]),
'H2': np.array([2.15, 15.76, 10.73]),
'O': np.array([1.51, 15.5, 10.])},
84: {'H1': np.array([11.51, 15.56, 4.67]),
'H2': np.array([10.53, 16.31, 5.75]),
'O': np.array([10.59, 15.57, 5.08])},
85: {'H1': np.array([15.8, 11.39, 3.06]),
'H2': np.array([14.54, 12.42, 3.13]),
'O': np.array([15.42, 12.23, 2.69])},
86: {'H1': np.array([18.15, 15.27, 15.64]),
'H2': np.array([18.71, 13.74, 15.73]),
'O': np.array([18.47, 14.55, 16.26])},
87: {'H1': np.array([0.64, 10.45, 16.68]),
'H2': np.array([1.12, 9.71, 18.06]),
'O': np.array([1.36, 10.4, 17.37])},
88: {'H1': np.array([4.93, 2.28, 18.44]),
'H2': np.array([5.74, 3.41, 17.59]),
'O': np.array([5.48, 3.12, 18.51])},
89: {'H1': np.array([5.55, 14.96, 1.21]),
'H2': np.array([6.58, 16.07, 1.8]),
'O': np.array([5.85, 15.91, 1.14])},
90: {'H1': np.array([10.18, 17.7, 14.25]),
'H2': np.array([9.38, 17.83, 12.83]),
'O': np.array([9.76, 18.34, 13.61])},
91: {'H1': np.array([13.82, 5.09, 12.05]),
'H2': np.array([14.52, 5.5, 10.63]),
'O': np.array([14.69, 5.24, 11.58])},
92: {'H1': np.array([8.82, 4.27, 16.55]),
'H2': np.array([7.38, 5.03, 16.72]),
'O': np.array([7.83, 4.19, 16.41])},
93: {'H1': np.array([4.55, 15.55, 11.43]),
'H2': np.array([6., 15.35, 10.71]),
'O': np.array([5.09, 15.76, 10.62])},
94: {'H1': np.array([15.35, 11.98, 12.37]),
'H2': np.array([15.08, 13.52, 12.81]),
'O': np.array([15.08, 12.57, 13.12])},
95: {'H1': np.array([9.64, 3.23, 6.97]),
'H2': np.array([8.81, 4.58, 7.34]),
'O': np.array([8.73, 3.62, 7.1])},
96: {'H1': np.array([1.03, 4.63, 5.17]),
'H2': np.array([2.66, 4.6, 5.11]),
'O': np.array([1.83, 4.86, 4.62])},
97: {'H1': np.array([7.38, 8.02, 6.57]),
'H2': np.array([7.9, 7.3, 7.93]),
'O': np.array([7.11, 7.68, 7.47])},
98: {'H1': np.array([1.9, 3.78, 3.03]),
'H2': np.array([1.89, 3.71, 1.39]),
'O': np.array([2.3, 3.34, 2.22])},
99: {'H1': np.array([7.62, 3.19, 2.6]),
'H2': np.array([6.71, 2.4, 1.5]),
'O': np.array([7.57, 2.38, 2.01])},
100: {'H1': np.array([19.21, 10.7, 4.29]),
'H2': np.array([19.68, 11.46, 5.66]),
'O': np.array([18.96, 10.9, 5.24])},
101: {'H1': np.array([0., 2.61, 8.51]),
'H2': np.array([1.22, 1.75, 7.85]),
'O': np.array([0.99, 2.55, 8.41])},
102: {'H1': np.array([16.72, 1.96, 14.51]),
'H2': np.array([17.7, 1.38, 13.34]),
'O': np.array([16.86, 1.89, 13.52])},
103: {'H1': np.array([14.77, 18.26, 4.23]),
'H2': np.array([13.49, 17.27, 4.]),
'O': np.array([13.91, 18.14, 3.73])},
104: {'H1': np.array([4.14, 14.61, 4.48]),
'H2': np.array([3.04, 14.04, 3.41]),
'O': np.array([3.93, 13.87, 3.84])},
105: {'H1': np.array([9.99, 7.85, 4.19]),
'H2': np.array([11.43, 7.22, 4.62]),
'O': np.array([10.45, 7.03, 4.53])},
106: {'H1': np.array([13.41, 3.22, 14.29]),
'H2': np.array([13.52, 1.59, 14.25]),
'O': np.array([13.48, 2.39, 14.85])},
107: {'H1': np.array([15.59, 16.04, 17.75]),
'H2': np.array([15.93, 15.72, 19.32]),
'O': np.array([15.47, 15.38, 18.49])},
108: {'H1': np.array([4.21, 5.64, 14.12]),
'H2': np.array([2.76, 4.89, 14.04]),
'O': np.array([3.71, 4.87, 13.72])},
109: {'H1': np.array([4.38, 5.97, 3.51]),
'H2': np.array([3.25, 5.1, 2.73]),
'O': np.array([3.91, 5.84, 2.64])},
110: {'H1': np.array([6.7, 17.01, 15.29]),
'H2': np.array([7.51, 16.92, 13.87]),
'O': np.array([7.01, 16.4, 14.56])},
111: {'H1': np.array([13.14, 6.05, 3.64]),
'H2': np.array([14.32, 6.75, 4.54]),
'O': np.array([13.35, 6.49, 4.51])},
112: {'H1': np.array([14.51, 18.07, 6.73]),
'H2': np.array([14.23, 16.64, 7.47]),
'O': np.array([13.83, 17.52, 7.2])},
113: {'H1': np.array([6.14, 18.18, 7.87]),
'H2': np.array([7.66, 18.27, 7.27]),
'O': np.array([7.06, 17.8, 7.92])},
114: {'H1': np.array([1.38, 11.91, 8.4]),
'H2': np.array([1.64, 10.3, 8.35]),
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115: {'H1': np.array([14.43, 9.94, 7.92]),
'H2': np.array([14.19, 11.2, 8.94]),
'O': np.array([14.85, 10.51, 8.62])},
116: {'H1': np.array([15.98, 19.6, 1.42]),
'H2': np.array([14.91, 18.71, 2.28]),
'O': np.array([15.54, 18.71, 1.51])},
117: {'H1': np.array([4.04, 18.63, 1.01]),
'H2': np.array([5.16, 17.71, 1.76]),
'O': np.array([4.79, 18.63, 1.67])},
118: {'H1': np.array([3.41, 14.42, 13.13]),
'H2': np.array([2.99, 16., 13.19]),
'O': np.array([3.28, 15.26, 12.59])},
119: {'H1': np.array([12.65, 12.51, 4.43]),
'H2': np.array([12.58, 11.19, 3.47]),
'O': np.array([12.88, 12.14, 3.53])},
120: {'H1': np.array([10.71, 4., 4.72]),
'H2': np.array([10.4, 5.47, 4.09]),
'O': np.array([10.18, 4.49, 4.04])},
121: {'H1': np.array([17.72, 16.09, 1.1]),
'H2': np.array([16.85, 17.31, 1.75]),
'O': np.array([17.13, 16.35, 1.86])},
122: {'H1': np.array([8.01, 0.67, 8.87]),
'H2': np.array([7.85, 1.18, 10.42]),
'O': np.array([8.47, 0.74, 9.76])},
123: {'H1': np.array([12.77, 11.58, 16.35]),
'H2': np.array([12.83, 10.01, 16.79]),
'O': np.array([12.88, 10.64, 16.02])},
124: {'H1': np.array([7.43, 12.92, 14.22]),
'H2': np.array([6.93, 14.45, 13.98]),
'O': np.array([7.41, 13.68, 13.58])},
125: {'H1': np.array([1.57, 17.88, 1.84]),
'H2': np.array([1.12, 16.72, 0.79]),
'O': np.array([1.42, 17.67, 0.88])},
126: {'H1': np.array([0.79, 7.56, 7.34]),
'H2': np.array([1.38, 6.21, 8.06]),
'O': np.array([0.99, 7.12, 8.22])},
127: {'H1': np.array([9.92, 6.84, 6.32]),
'H2': np.array([10.61, 6.79, 7.8]),
'O': np.array([9.78, 6.57, 7.27])},
128: {'H1': np.array([14.07, 19.43, 9.74]),
'H2': np.array([13.66, 18.52, 8.44]),
'O': np.array([13.31, 18.99, 9.26])},
129: {'H1': np.array([5.98, 12., 11.32]),
'H2': np.array([6.81, 13.27, 11.92]),
'O': np.array([6.59, 12.75, 11.09])},
130: {'H1': np.array([4.56, 17.46, 10.41]),
'H2': np.array([4.4, 18.66, 9.31]),
'O': np.array([4.22, 18.39, 10.26])},
131: {'H1': np.array([1.94, 3.88, 17.78]),
'H2': np.array([2.49, 5.41, 17.97]),
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132: {'H1': np.array([13.93, 18.47, 0.76]),
'H2': np.array([12.94, 17.26, 1.23]),
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133: {'H1': np.array([12.68, 14.27, 1.11]),
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134: {'H1': np.array([14.74, 14.61, 10.18]),
'H2': np.array([15.34, 14.81, 8.67]),
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135: {'H1': np.array([2.12, 14.44, 8.64]),
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136: {'H1': np.array([2.79, 12.72, 6.76]),
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137: {'H1': np.array([8.96, 4.86, 11.53]),
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138: {'H1': np.array([10.27, 8.54, 18.84]),
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139: {'H1': np.array([15.51, 7.81, 19.03]),
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140: {'H1': np.array([1.62, 17.65, 4.46]),
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141: {'H1': np.array([11.06, 15.12, 1.95]),
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142: {'H1': np.array([12.29, 14.81, 7.74]),
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143: {'H1': np.array([5.27, 1.78, 8.02]),
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144: {'H1': np.array([18.03, 14.84, 12.63]),
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145: {'H1': np.array([7.38, 15.36, 3.9]),
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146: {'H1': np.array([12.03, 7.6, 9.42]),
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147: {'H1': np.array([16.18, 13.76, 3.61]),
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148: {'H1': np.array([18.87, 7.99, 4.92]),
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149: {'H1': np.array([15.15, 15.81, 11.79]),
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150: {'H1': np.array([11.62, 2.15, 6.64]),
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151: {'H1': np.array([4.9, 1.35, 2.82]),
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152: {'H1': np.array([3.35, 18.25, 13.35]),
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153: {'H1': np.array([9.39, 9.16, 2.03]),
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154: {'H1': np.array([4.55, 8.75, 1.96]),
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155: {'H1': np.array([10.59, 3.96, 2.44]),
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156: {'H1': np.array([16.13, 8.82, 8.47]),
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157: {'H1': np.array([19.35, 10.88, 10.31]),
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158: {'H1': np.array([9.18, 11.19, 14.4]),
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159: {'H1': np.array([5.74, 7.24, 13.87]),
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160: {'H1': np.array([10.7, 8.59, 10.26]),
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161: {'H1': np.array([4.59, 18.47, 16.58]),
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162: {'H1': np.array([18.06, 4.45, 6.39]),
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163: {'H1': np.array([16.62, 7.65, 10.39]),
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164: {'H1': np.array([8.22, 8.69, 4.32]),
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165: {'H1': np.array([9.99, 10.91, 0.49]),
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166: {'H1': np.array([11.65, 13.78, 16.7]),
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167: {'H1': np.array([3.19, 6.58, 10.52]),
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168: {'H1': np.array([18.43, 1.4, 4.02]),
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169: {'H1': np.array([19.6, 19.49, 12.07]),
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170: {'H1': np.array([5.31, 7.83, 10.35]),
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171: {'H1': np.array([13.4, 5.2, 5.78]),
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172: {'H1': np.array([7.93, 4.34, 4.91]),
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173: {'H1': np.array([0.88, 1.9, 17.34]),
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174: {'H1': np.array([1.25, 12.01, 0.]),
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175: {'H1': np.array([16.13, 5.68, 14.05]),
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176: {'H1': np.array([9.01, 14.55, 5.07]),
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177: {'H1': np.array([11.4, 4.88, 12.47]),
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178: {'H1': np.array([10.09, 1.19, 9.02]),
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179: {'H1': np.array([17.43, 11.88, 5.71]),
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180: {'H1': np.array([9.64, 13.18, 9.88]),
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181: {'H1': np.array([5.02, 19.09, 4.57]),
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182: {'H1': np.array([10.36, 12.5, 12.48]),
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183: {'H1': np.array([2.03, 4.63, 11.05]),
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184: {'H1': np.array([16.5, 19.24, 5.36]),
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185: {'H1': np.array([6.45, 9.7, 0.12]),
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186: {'H1': np.array([17.16, 16.31, 9.04]),
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187: {'H1': np.array([5.6, 3.49, 8.54]),
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188: {'H1': np.array([12.8, 9.21, 6.8]),
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189: {'H1': np.array([7.66, 17.95, 3.39]),
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190: {'H1': np.array([4.09, 12.52, 14.22]),
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191: {'H1': np.array([16.25, 18.31, 16.17]),
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192: {'H1': np.array([8.79, 13.74, 6.7]),
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193: {'H1': np.array([8.87, 1.64, 6.54]),
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194: {'H1': np.array([17.94, 5.28, 10.88]),
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195: {'H1': np.array([15.96, 10.17, 0.62]),
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196: {'H1': np.array([4.31, 9.79, 8.15]),
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197: {'H1': np.array([18.94, 0.26, 18.93]),
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198: {'H1': np.array([1.79, 1.76, 9.89]),
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199: {'H1': np.array([9.09, 6.96, 17.65]),
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200: {'H1': np.array([11.37, 3.8, 9.45]),
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201: {'H1': np.array([10.59, 3.32, 17.25]),
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202: {'H1': np.array([15.75, 4.8, 7.84]),
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203: {'H1': np.array([9.39, 11.03, 3.93]),
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204: {'H1': np.array([14.39, 13.53, 14.42]),
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205: {'H1': np.array([7.31, 6.01, 1.99]),
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206: {'H1': np.array([13.36, 14.89, 4.74]),
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207: {'H1': np.array([18.25, 7.35, 15.77]),
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208: {'H1': np.array([5.95, 11.5, 2.89]),
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209: {'H1': np.array([0.57, 9.63, 2.45]),
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210: {'H1': np.array([17.05, 3.82, 13.02]),
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211: {'H1': np.array([15.94, 7.88, 4.63]),
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212: {'H1': np.array([5.06, 10.12, 10.68]),
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213: {'H1': np.array([3.6, 15.34, 16.79]),
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214: {'H1': np.array([6.28, 12.21, 19.2]),
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215: {'H1': np.array([11.19, 2.05, 12.75]),
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216: {'H1': np.array([17.79, 7.5, 12.67]),
'H2': np.array([18.24, 8.56, 13.83]),
'O': np.array([18.56, 7.85, 13.21])}}}