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Update statistical inefficiency calculation with statistical_inefficiency in alchemlyb 2.1.0 #250

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Merged
orbeckst merged 3 commits into from
Jun 29, 2023
Merged

Update statistical inefficiency calculation with statistical_inefficiency in alchemlyb 2.1.0 #250

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135742d
replace pymbar SI functions with SI function in alchemlyb
VOD555 Jun 28, 2023
68113ac
increased required versions for alchemlyb and pymbar
orbeckst Jun 28, 2023
83e69bc
explicitly install mdanalysis during GROMACS installation
orbeckst Jun 29, 2023
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20 changes: 8 additions & 12 deletions mdpow/fep.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,11 +157,10 @@
from numkit.observables import QuantityWithError

from alchemlyb.parsing.gmx import extract_dHdl, extract_u_nk
from alchemlyb.estimators import TI, BAR
from alchemlyb.estimators import AutoMBAR as MBAR
from alchemlyb.estimators import TI, BAR, MBAR
from alchemlyb.parsing.gmx import _extract_dataframe
from pymbar.timeseries import (statisticalInefficiency,
subsampleCorrelatedData, )
from alchemlyb.preprocessing.subsampling import statistical_inefficiency

import gromacs
import gromacs.utilities
try:
Expand Down Expand Up @@ -1054,19 +1053,16 @@ def collect_alchemlyb(self, SI=True, start=0, stop=None, stride=None, autosave=T
for l in lambdas:
xvg_file = self.dgdl_xvg(self.wdir(component, l))
xvg_df = extract(xvg_file, T=self.Temperature).iloc[start:stop:stride]
full_len = len(xvg_df)
if SI:
logger.info("Performing statistical inefficiency analysis for window %s %04d" % (component, 1000 * l))
ts = _extract_dataframe(xvg_file).iloc[start:stop:stride]
ts = pd.DataFrame({'time': ts.iloc[:,0], 'dhdl': ts.iloc[:,1]})
ts = ts.set_index('time')
# calculate statistical inefficiency of series
statinef = statisticalInefficiency(ts, fast=False)
logger.info("The statistical inefficiency value is {:.4f}.".format(statinef))
logger.info("The data are subsampled every {:d} frames.".format(int(np.ceil(statinef))))
# use the subsampleCorrelatedData function to get the subsample index
indices = subsampleCorrelatedData(ts, g=statinef,
conservative=True)
xvg_df = xvg_df.iloc[indices]
# use the statistical_inefficiency function to subsample the data
xvg_df = statistical_inefficiency(xvg_df, ts, conservative=True)
logger.info("The statistical inefficiency value is {:.4f}.".format(full_len/len(xvg_df)/2))
logger.info("The data are subsampled every {:d} frames.".format(int(np.ceil(full_len/len(xvg_df)/2))))
val.append(xvg_df)
self.results.xvg[component] = (np.array(lambdas), pd.concat(val))

Expand Down