Source code for MDMC.MD.solvents.SPC_config

"""
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]), 'O': np.array([1.32, 11.06, 8.92])}, 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]), 'O': np.array([1.87, 4.72, 18.33])}, 132: {'H1': np.array([13.93, 18.47, 0.76]), 'H2': np.array([12.94, 17.26, 1.23]), 'O': np.array([13., 18.11, 0.71])}, 133: {'H1': np.array([12.68, 14.27, 1.11]), 'H2': np.array([13.97, 15.15, 0.64]), 'O': np.array([13.2, 15.1, 1.28])}, 134: {'H1': np.array([14.74, 14.61, 10.18]), 'H2': np.array([15.34, 14.81, 8.67]), 'O': np.array([14.92, 14.16, 9.3])}, 135: {'H1': np.array([2.12, 14.44, 8.64]), 'H2': np.array([3.5, 14.12, 7.83]), 'O': np.array([2.57, 13.81, 8.])}, 136: {'H1': np.array([2.79, 12.72, 6.76]), 'H2': np.array([3.07, 12.56, 5.16]), 'O': np.array([2.79, 12.08, 5.99])}, 137: {'H1': np.array([8.96, 4.86, 11.53]), 'H2': np.array([9.99, 3.66, 11.97]), 'O': np.array([9.89, 4.65, 11.84])}, 138: {'H1': np.array([10.27, 8.54, 18.84]), 'H2': np.array([11.63, 9.43, 18.77]), 'O': np.array([10.69, 9.4, 19.11])}, 139: {'H1': np.array([15.51, 7.81, 19.03]), 'H2': np.array([15.29, 6.28, 18.52]), 'O': np.array([14.85, 7.07, 18.94])}, 140: {'H1': np.array([1.62, 17.65, 4.46]), 'H2': np.array([0.25, 17.7, 3.57]), 'O': np.array([1.21, 17.97, 3.6])}, 141: {'H1': np.array([11.06, 15.12, 1.95]), 'H2': np.array([10.34, 15.6, 3.34]), 'O': np.array([10.21, 15.45, 2.36])}, 142: {'H1': np.array([12.29, 14.81, 7.74]), 'H2': np.array([11.12, 15.92, 8.03]), 'O': np.array([11.7, 15.21, 8.44])}, 143: {'H1': np.array([5.27, 1.78, 8.02]), 'H2': np.array([4.98, 0.69, 6.85]), 'O': np.array([4.88, 0.89, 7.82])}, 144: {'H1': np.array([18.03, 14.84, 12.63]), 'H2': np.array([17.02, 16.07, 13.01]), 'O': np.array([17.96, 15.73, 13.09])}, 145: {'H1': np.array([7.38, 15.36, 3.9]), 'H2': np.array([8.58, 15.69, 2.84]), 'O': np.array([7.76, 16.05, 3.28])}, 146: {'H1': np.array([12.03, 7.6, 9.42]), 'H2': np.array([12.26, 6.06, 8.92]), 'O': np.array([12.33, 7.03, 8.66])}, 147: {'H1': np.array([16.18, 13.76, 3.61]), 'H2': np.array([16.78, 15.19, 3.11]), 'O': np.array([16.52, 14.65, 3.91])}, 148: {'H1': np.array([18.87, 7.99, 4.92]), 'H2': np.array([19.41, 9.11, 5.97]), 'O': np.array([19.11, 8.17, 5.87])}, 149: {'H1': np.array([15.15, 15.81, 11.79]), 'H2': np.array([13.59, 15.32, 11.82]), 'O': np.array([14.54, 15.01, 11.75])}, 150: {'H1': np.array([11.62, 2.15, 6.64]), 'H2': np.array([11.5, 2.27, 5.02]), 'O': np.array([11.4, 2.76, 5.88])}, 151: {'H1': np.array([4.9, 1.35, 2.82]), 'H2': np.array([3.76, 2.38, 3.38]), 'O': np.array([4.66, 1.98, 3.56])}, 152: {'H1': np.array([3.35, 18.25, 13.35]), 'H2': np.array([4.17, 18.42, 11.94]), 'O': np.array([4.21, 18.56, 12.93])}, 153: {'H1': np.array([9.39, 9.16, 2.03]), 'H2': np.array([8.4, 7.97, 2.54]), 'O': np.array([8.78, 8.86, 2.76])}, 154: {'H1': np.array([4.55, 8.75, 1.96]), 'H2': np.array([3.48, 7.74, 2.68]), 'O': np.array([3.71, 8.7, 2.5])}, 155: {'H1': np.array([10.59, 3.96, 2.44]), 'H2': np.array([9.81, 3.56, 1.06]), 'O': np.array([10.64, 3.97, 1.44])}, 156: {'H1': np.array([16.13, 8.82, 8.47]), 'H2': np.array([17.21, 8.05, 7.52]), 'O': np.array([16.69, 8.01, 8.38])}, 157: {'H1': np.array([19.35, 10.88, 10.31]), 'H2': np.array([19.4, 10.45, 11.88]), 'O': np.array([18.84, 10.87, 11.17])}, 158: {'H1': np.array([9.18, 11.19, 14.4]), 'H2': np.array([8.14, 10.49, 15.45]), 'O': np.array([8.45, 11.36, 15.07])}, 159: {'H1': np.array([5.74, 7.24, 13.87]), 'H2': np.array([4.67, 8.01, 14.84]), 'O': np.array([5.07, 7.12, 14.61])}, 160: {'H1': np.array([10.7, 8.59, 10.26]), 'H2': np.array([11.75, 9.84, 10.31]), 'O': np.array([11.11, 9.33, 9.73])}, 161: {'H1': np.array([4.59, 18.47, 16.58]), 'H2': np.array([4.39, 19.97, 15.96]), 'O': np.array([3.99, 19.28, 16.56])}, 162: {'H1': np.array([18.06, 4.45, 6.39]), 'H2': np.array([17.25, 4.31, 4.98]), 'O': np.array([18.11, 4.12, 5.44])}, 163: {'H1': np.array([16.62, 7.65, 10.39]), 'H2': np.array([16.13, 6.66, 11.59]), 'O': np.array([16.53, 7.55, 11.38])}, 164: {'H1': np.array([8.22, 8.69, 4.32]), 'H2': np.array([7.42, 9.49, 5.5]), 'O': np.array([7.69, 8.59, 5.16])}, 165: {'H1': np.array([9.99, 10.91, 0.49]), 'H2': np.array([8.62, 11.78, 0.66]), 'O': np.array([9.6, 11.76, 0.85])}, 166: {'H1': np.array([11.65, 13.78, 16.7]), 'H2': np.array([12.35, 13.12, 18.02]), 'O': np.array([12.44, 13.27, 17.04])}, 167: {'H1': np.array([3.19, 6.58, 10.52]), 'H2': np.array([2.54, 7.51, 9.35]), 'O': np.array([3.13, 7.51, 10.15])}, 168: {'H1': np.array([18.43, 1.4, 4.02]), 'H2': np.array([18.13, 2.33, 5.34]), 'O': np.array([18.03, 1.41, 4.94])}, 169: {'H1': np.array([19.6, 19.49, 12.07]), 'H2': np.array([19.37, 18.28, 13.14]), 'O': np.array([18.97, 19.11, 12.75])}, 170: {'H1': np.array([5.31, 7.83, 10.35]), 'H2': np.array([6.61, 7.66, 9.38]), 'O': np.array([6.23, 7.46, 10.28])}, 171: {'H1': np.array([13.4, 5.2, 5.78]), 'H2': np.array([12.69, 3.75, 5.99]), 'O': np.array([13.53, 4.27, 6.12])}, 172: {'H1': np.array([7.93, 4.34, 4.91]), 'H2': np.array([6.58, 4.95, 4.24]), 'O': np.array([7.33, 4.31, 4.11])}, 173: {'H1': np.array([0.88, 1.9, 17.34]), 'H2': np.array([2.17, 1.69, 16.37]), 'O': np.array([1.58, 2.37, 16.8])}, 174: {'H1': np.array([1.25, 12.01, 0.]), 'H2': np.array([1.31, 11.61, 1.58]), 'O': np.array([1.33, 12.37, 0.93])}, 175: {'H1': np.array([16.13, 5.68, 14.05]), 'H2': np.array([15.27, 7.05, 14.31]), 'O': np.array([15.71, 6.26, 14.75])}, 176: {'H1': np.array([9.01, 14.55, 5.07]), 'H2': np.array([8.56, 13.11, 4.44]), 'O': np.array([8.28, 13.86, 5.03])}, 177: {'H1': np.array([11.4, 4.88, 12.47]), 'H2': np.array([12.48, 5.4, 13.58]), 'O': np.array([12.36, 4.87, 12.74])}, 178: {'H1': np.array([10.09, 1.19, 9.02]), 'H2': np.array([11.71, 1.22, 8.9]), 'O': np.array([10.86, 1.52, 8.48])}, 179: {'H1': np.array([17.43, 11.88, 5.71]), 'H2': np.array([16.61, 12.09, 7.1]), 'O': np.array([16.53, 12.04, 6.11])}, 180: {'H1': np.array([9.64, 13.18, 9.88]), 'H2': np.array([10.87, 14.23, 9.61]), 'O': np.array([10.56, 13.46, 10.17])}, 181: {'H1': np.array([5.02, 19.09, 4.57]), 'H2': np.array([6.55, 18.69, 4.96]), 'O': np.array([5.59, 18.57, 5.2])}, 182: {'H1': np.array([10.36, 12.5, 12.48]), 'H2': np.array([11.43, 11.3, 12.8]), 'O': np.array([10.53, 11.67, 13.02])}, 183: {'H1': np.array([2.03, 4.63, 11.05]), 'H2': np.array([3.36, 4.89, 11.96]), 'O': np.array([3.02, 4.74, 11.03])}, 184: {'H1': np.array([16.5, 19.24, 5.36]), 'H2': np.array([16.53, 17.63, 5.56]), 'O': np.array([15.94, 18.43, 5.53])}, 185: {'H1': np.array([6.45, 9.7, 0.12]), 'H2': np.array([6.78, 9., 1.56]), 'O': np.array([6.12, 9.06, 0.81])}, 186: {'H1': np.array([17.16, 16.31, 9.04]), 'H2': np.array([17.57, 14.86, 8.39]), 'O': np.array([16.97, 15.66, 8.3])}, 187: {'H1': np.array([5.6, 3.49, 8.54]), 'H2': np.array([6.65, 3.52, 7.29]), 'O': np.array([5.69, 3.59, 7.55])}, 188: {'H1': np.array([12.8, 9.21, 6.8]), 'H2': np.array([13.64, 7.86, 7.14]), 'O': np.array([13.71, 8.8, 6.82])}, 189: {'H1': np.array([7.66, 17.95, 3.39]), 'H2': np.array([7.94, 19.52, 3.03]), 'O': np.array([7.93, 18.84, 3.76])}, 190: {'H1': np.array([4.09, 12.52, 14.22]), 'H2': np.array([3.64, 13.76, 15.17]), 'O': np.array([3.44, 13.26, 14.33])}, 191: {'H1': np.array([16.25, 18.31, 16.17]), 'H2': np.array([14.87, 17.48, 15.88]), 'O': np.array([15.75, 17.46, 16.36])}, 192: {'H1': np.array([8.79, 13.74, 6.7]), 'H2': np.array([8.12, 13.06, 8.02]), 'O': np.array([8.96, 13.43, 7.64])}, 193: {'H1': np.array([8.87, 1.64, 6.54]), 'H2': np.array([8.41, 0.56, 5.4]), 'O': np.array([8.49, 0.73, 6.39])}, 194: {'H1': np.array([17.94, 5.28, 10.88]), 'H2': np.array([18.84, 5.98, 9.7]), 'O': np.array([18.4, 5.13, 10.])}, 195: {'H1': np.array([15.96, 10.17, 0.62]), 'H2': np.array([17.41, 9.41, 0.68]), 'O': np.array([16.42, 9.29, 0.75])}, 196: {'H1': np.array([4.31, 9.79, 8.15]), 'H2': np.array([5.83, 9.23, 7.94]), 'O': np.array([5.27, 9.9, 8.42])}, 197: {'H1': np.array([18.94, 0.26, 18.93]), 'H2': np.array([18.08, 1.64, 19.11]), 'O': np.array([18.4, 0.95, 18.45])}, 198: {'H1': np.array([1.79, 1.76, 9.89]), 'H2': np.array([2.83, 0.67, 10.52]), 'O': np.array([2.02, 1.23, 10.71])}, 199: {'H1': np.array([9.09, 6.96, 17.65]), 'H2': np.array([9.81, 6.29, 18.95]), 'O': np.array([9.8, 7.08, 18.35])}, 200: {'H1': np.array([11.37, 3.8, 9.45]), 'H2': np.array([12.06, 4.67, 10.64]), 'O': np.array([12.02, 4.53, 9.66])}, 201: {'H1': np.array([10.59, 3.32, 17.25]), 'H2': np.array([11.42, 3.51, 15.86]), 'O': np.array([10.6, 3.79, 16.37])}, 202: {'H1': np.array([15.75, 4.8, 7.84]), 'H2': np.array([16.5, 6.25, 7.77]), 'O': np.array([16.53, 5.3, 7.46])}, 203: {'H1': np.array([9.39, 11.03, 3.93]), 'H2': np.array([9.46, 11.82, 2.5]), 'O': np.array([9.43, 11.93, 3.49])}, 204: {'H1': np.array([14.39, 13.53, 14.42]), 'H2': np.array([13.7, 13.85, 15.87]), 'O': np.array([14.09, 14.25, 15.04])}, 205: {'H1': np.array([7.31, 6.01, 1.99]), 'H2': np.array([8.5, 5.76, 3.08]), 'O': np.array([8.24, 6.26, 2.26])}, 206: {'H1': np.array([13.36, 14.89, 4.74]), 'H2': np.array([13.38, 15.22, 3.14]), 'O': np.array([12.98, 15.48, 4.02])}, 207: {'H1': np.array([18.25, 7.35, 15.77]), 'H2': np.array([17.14, 6.63, 16.73]), 'O': np.array([17.93, 7.25, 16.71])}, 208: {'H1': np.array([5.95, 11.5, 2.89]), 'H2': np.array([4.94, 12.78, 2.95]), 'O': np.array([5.43, 12.15, 2.34])}, 209: {'H1': np.array([0.57, 9.63, 2.45]), 'H2': np.array([2.13, 9.99, 2.76]), 'O': np.array([1.2, 10.35, 2.76])}, 210: {'H1': np.array([17.05, 3.82, 13.02]), 'H2': np.array([18.42, 4.66, 13.26]), 'O': np.array([17.51, 4.69, 12.85])}, 211: {'H1': np.array([15.94, 7.88, 4.63]), 'H2': np.array([16.88, 7.03, 3.61]), 'O': np.array([15.99, 7.06, 4.06])}, 212: {'H1': np.array([5.06, 10.12, 10.68]), 'H2': np.array([3.81, 10.15, 11.72]), 'O': np.array([4.71, 10.53, 11.52])}, 213: {'H1': np.array([3.6, 15.34, 16.79]), 'H2': np.array([4.34, 14.19, 17.68]), 'O': np.array([4.19, 14.54, 16.75])}, 214: {'H1': np.array([6.28, 12.21, 19.2]), 'H2': np.array([7.14, 13.05, 18.1]), 'O': np.array([7.12, 12.22, 18.66])}, 215: {'H1': np.array([11.19, 2.05, 12.75]), 'H2': np.array([9.8, 1.3, 13.16]), 'O': np.array([10.2, 2.16, 12.84])}, 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])}}}