from simtk import unit
T = 298.15 * unit.kelvin
solute = 'phenol'
solvent = 'water'
kB = unit.BOLTZMANN_CONSTANT_kB * unit.AVOGADRO_CONSTANT_NA
states = pd.read_csv('alchemical_states.inp', sep='\s+', comment='#')
nstates = len(states.index)
states = states[states.weight != -np.inf].drop('weight', axis=1)
files = [f'{solute}-in-{solvent}_energy-{i:02d}.csv' for i in states.index]
renamer = lambda x: x.replace('Energy[', 'E').replace('] (kJ/mole)', '')
kT = (kB * T).value_in_unit(unit.kilojoules_per_mole)
mx.verbose = True
samples = mx.pooledsample()
for i, file in zip(states.index, files):
print(f"Reading file {file}")
df = pd.read_csv(file)
df.drop(index=range(15000), inplace=True)
df.rename(renamer, axis='columns', inplace=True)
prev = max(i - 1, 0)
next = min(i + 1, nstates - 1)
samples += mx.sample(df,
f'beta*(E{i}-E0)',
acfun=f'E{next}-E{prev}',
beta=1 / kT,
lambda_vdw=states.lambda_vdw[i],
lambda_coul=states.lambda_coul[i])
samples.histograms().plot(x='potential')
def __add__(self, other):
return mics.pooledsample([self]) + other
import pandas as pd
import pytest
import mics
def test_main():
assert mics # use your library here
mics.verbose = True
m = 4
beta = 1.6773985789
data = ["tests/data/log_%d.dat" % (i + 1) for i in range(m)]
samples = mics.pooledsample()
for i in range(m):
dataset = pd.read_csv(data[i], sep=" ")
potential = "beta*E%d" % (i + 1)
autocorr = "beta*(E%d - E%d)" % (min(i + 2, m), max(i, 1))
samples += mics.sample(dataset, potential, autocorr, beta=beta)
def test_read_samples():
for sample in samples:
assert sample.dataset.columns[5] == 'Press'
def test_pooledsample():
neff = [
100.829779921697, 76.82824014457174, 69.63811023389404,
def __radd__(self, other):
return mics.pooledsample(other) + self