def free_energy_calculation(dHdl, u_nk):
logger("Fitting TI on dHdl ...")
ti = TI().fit(dHdl)
logger("Fitting BAR on u_nk ...")
bar = BAR().fit(u_nk)
logger("Fitting MBAR on u_nk ...\n")
try:
mbar_stop = False
mbar = MBAR().fit(u_nk)
except pymbar.utils.ParameterError:
mbar_stop = True
logger(
"\sum_n W_nk is not equal to 1, probably due to insufficient overlap between states."
)
logger("Stop using MBAR ...")
logger("------ Results based on the whole dataset ------")
if hasattr(dHdl, 'statineff') is True:
logger(f'Statistical inefficiency of dHdl: {dHdl.statineff}')
if hasattr(u_nk, 'statineff') is True:
logger(f'Statistical inefficiency of u_nk: {u_nk.statineff}\n')
logger(f"TI: {ti.delta_f_.iloc[0, -1]} +/- {ti.d_delta_f_.iloc[0, -1]} kT")
logger(f"BAR: {bar.delta_f_.iloc[0, -1]} +/- unknown kT")
if mbar_stop is False:
logger(
f"MBAR: {mbar.delta_f_.iloc[0, -1]} +/- {mbar.d_delta_f_.iloc[0, -1]} kT"
)
logger("------------------------------------------------")
return ti, bar, mbar
else:
logger("------------------------------------------------")
return ti, bar
def test_plot_dF_state():
'''Just test if the plot runs'''
bz = load_benzene().data
u_nk_coul = pd.concat([extract_u_nk(xvg, T=300) for xvg in bz['Coulomb']])
dHdl_coul = pd.concat([extract_dHdl(xvg, T=300) for xvg in bz['Coulomb']])
u_nk_vdw = pd.concat([extract_u_nk(xvg, T=300) for xvg in bz['VDW']])
dHdl_vdw = pd.concat([extract_dHdl(xvg, T=300) for xvg in bz['VDW']])
ti_coul = TI().fit(dHdl_coul)
ti_vdw = TI().fit(dHdl_vdw)
bar_coul = BAR().fit(u_nk_coul)
bar_vdw = BAR().fit(u_nk_vdw)
mbar_coul = MBAR().fit(u_nk_coul)
mbar_vdw = MBAR().fit(u_nk_vdw)
dhdl_data = [
(ti_coul, ti_vdw),
(bar_coul, bar_vdw),
(mbar_coul, mbar_vdw),
]
fig = plot_dF_state(dhdl_data, orientation='portrait')
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state(dhdl_data, orientation='landscape')
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state(dhdl_data, labels=['MBAR', 'TI', 'BAR'])
assert isinstance(fig, matplotlib.figure.Figure)
with pytest.raises(ValueError):
fig = plot_dF_state(dhdl_data, labels=[
'MBAR',
'TI',
])
fig = plot_dF_state(dhdl_data, colors=['#C45AEC', '#33CC33', '#F87431'])
assert isinstance(fig, matplotlib.figure.Figure)
with pytest.raises(ValueError):
fig = plot_dF_state(dhdl_data, colors=['#C45AEC', '#33CC33'])
with pytest.raises(NameError):
fig = plot_dF_state(dhdl_data, orientation='xxx')
fig = plot_dF_state(ti_coul, orientation='landscape')
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state(ti_coul, orientation='portrait')
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state([ti_coul, bar_coul])
assert isinstance(fig, matplotlib.figure.Figure)
fig = plot_dF_state([(ti_coul, ti_vdw)])
assert isinstance(fig, matplotlib.figure.Figure)
def test_bar_namd():
"""Test BAR calculation on NAMD data.
"""
from alchemlyb.estimators import BAR
import numpy as np
# load data
dataset = load_tyr2ala()
u_nk1 = extract_u_nk(dataset['data']['forward'][0])
u_nk2 = extract_u_nk(dataset['data']['backward'][0])
# combine dataframes of fwd and rev directions
u_nk1.replace(0, np.nan, inplace=True)
u_nk1[u_nk1.isnull()] = u_nk2
u_nk1.replace(np.nan, 0, inplace=True)
u_nk = u_nk1.sort_index(level=u_nk1.index.names[1:])
# after loading BOTH fwd and rev data, do BAR calculation
bar = BAR()
bar.fit(u_nk)
dg = (bar.delta_f_.iloc[0].iloc[-1])
assert_almost_equal(dg, 6.03126982925, decimal=4)
def test_bar(self, u_nk):
bar = BAR().fit(u_nk)
assert bar.delta_f_.attrs['temperature'] == 300
assert bar.delta_f_.attrs['energy_unit'] == 'kT'
assert bar.d_delta_f_.attrs['temperature'] == 300
assert bar.d_delta_f_.attrs['energy_unit'] == 'kT'
#for TI estimator
#print("Working on TI method ...")
dHdl = pd.concat([extract_dHdl(f, T=temprature) for f in files])
ti = TI().fit(dHdl)
sum_ti, sum_ds_ti = get_delta(ti)
tis.append(sum_ti)
#for MBAR estimator
#print("Working on MBAR method ...")
u_nk = pd.concat([extract_u_nk(f, T=temprature) for f in files])
mbar = MBAR().fit(u_nk)
sum_mbar, sum_ds_mbar = get_delta(mbar)
mbars.append(sum_mbar)
#for BAR estimator
#print("Working on BAR method ...")
bar = BAR().fit(u_nk)
sum_bar, sum_ds_bar = get_delta2(bar)
bars.append(sum_bar)
print("Replica-%d, %4s, %7.4f, %7.4f, %7.4f, %7.4f, %7.4f, %7.4f" % (nr, com, sum_ti, sum_ds_ti, sum_mbar, sum_ds_mbar, sum_bar, sum_ds_bar))
tis = np.array(tis)
mbars = np.array(mbars)
bars = np.array(bars)
print("Average, %4s, %7.4f, %7.4f, %7.4f, %7.4f, %7.4f, %7.4f" % (com, np.average(tis), np.std(tis), np.average(mbars), np.std(mbars), np.average(bars), np.std(bars)))