def gomc_benzene_u_nk():
dataset = alchemtest.gomc.load_benzene()
u_nk = alchemlyb.concat(
[gomc.extract_u_nk(filename, T=298) for filename in dataset['data']])
return u_nk
def test_u_nk():
"""Test that u_nk has the correct form when extracted from files.
"""
dataset = load_benzene()
for filename in dataset['data']:
u_nk = extract_u_nk(filename, T=298)
assert u_nk.index.names == ['time', 'Coulomb-lambda', 'VDW-lambda']
assert u_nk.shape == (1000, 23)
freeEn_file = fname + str(i) + ext
file_path = data_loc + str(i) + "/" + freeEn_file
print("%4s: Reading File: %s " % (com, file_path))
files.append(file_path)
#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)
for nr in range(NREP):
#read the free energy files
data_loc = bd + "/" + com + "/TI_" + str(nr + 1) + "/state_"
files = []
for i in range(numFile + 1):
freeEn_file = fname + str(i) + ext
file_path = data_loc + str(i) + "/" + freeEn_file
print("%4s: Reading File: %s " % (com, freeEn_file))
files.append(file_path)
# Read the data for TI estimator and BAR or MBAR estimators.
list_data_TI = []
list_data_BAR = []
for f in files:
dHdl = extract_dHdl(f, T=temprature)
u_nkr = extract_u_nk(f, T=temprature)
#Detect uncorrelated samples using VDW+Coulomb term in derivative
# of energy time series (calculated for TI)
srs = dHdl['VDW'] + dHdl['Coulomb']
list_data_TI.append(
ss.statistical_inefficiency(dHdl,
series=srs,
conservative=False))
list_data_BAR.append(
ss.statistical_inefficiency(u_nkr,
series=srs,
conservative=False))
#for TI estimator
#print("Working on TI method ...")
dhdl = pd.concat([ld for ld in list_data_TI])