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Update analyse_freenrg to use pymbar 4 #91

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merged 3 commits into from
Aug 7, 2023
Merged

Update analyse_freenrg to use pymbar 4 #91

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ec4943f
Update analyse_freenrg to use pymbar 4
fjclark Jul 28, 2023
f0952dc
Update FreeEnergyAnalysis to be compatible with pymbar 3 or 4[ci skip]
fjclark Aug 6, 2023
fd7fe22
Merge branch 'devel' into feature_pymbar4
chryswoods Aug 7, 2023
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2 changes: 2 additions & 0 deletions doc/source/changelog.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,8 @@ organisation on `GitHub <https://github.com/openbiosim/sire>`__.
* Make sure atom serial number in PDB files are capped when renumbering when
TER records are present.

* Updated ``FreeEnergyAnalysis.py`` to use the new pymbar 4 API.

* Please add the changelog entry for your PR here. We will add the link to your PR
during the code review :-)

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2 changes: 1 addition & 1 deletion requirements_bss.txt
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Expand Up @@ -34,7 +34,7 @@ rdkit>=2023.0.0
# The below are packages that aren't available on all
# platforms/OSs and so need to be conditionally included

alchemlyb<2 ; platform_machine != "aarch64" # Needs pymbar, not on Linux/aarch64
alchemlyb ; platform_machine != "aarch64" # Needs pymbar, not on Linux/aarch64

mdtraj ; platform_machine != "aarch64" # not on Linux/aarch64

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29 changes: 12 additions & 17 deletions wrapper/Tools/FreeEnergyAnalysis.py
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Expand Up @@ -26,7 +26,7 @@
try:
# This should force the installation of pymbar if it isn't
# installed already
_pymbar = Sire.try_import("pymbar", version="<4")
_pymbar = Sire.try_import("pymbar", version=">=4")

from pymbar import MBAR
from pymbar import timeseries
Expand Down Expand Up @@ -105,21 +105,16 @@ def run_mbar(self, test_overlap=True):
try:
MBAR_obj = MBAR(self._u_kln, self._N_k, verbose=True)
self._f_k = MBAR_obj.f_k
(
deltaF_ij,
dDeltaF_ij,
theta_ij,
) = MBAR_obj.getFreeEnergyDifferences(return_theta=True)
results = MBAR_obj.compute_free_energy_differences()
deltaF_ij = results["Delta_f"]
dDeltaF_ij = results["dDelta_f"]
except:
solver_options = {"maximum_iterations": 10000, "verbose": True}
solver_protocol = {"method": "BFGS", "options": solver_options}
MBAR_obj = MBAR(self._u_kln, self._N_k, solver_protocol=(solver_protocol,))
self._f_k = MBAR_obj.f_k
(
deltaF_ij,
dDeltaF_ij,
theta_ij,
) = MBAR_obj.getFreeEnergyDifferences(return_theta=True)
results = MBAR_obj.compute_free_energy_differences()
deltaF_ij = results["Delta_f"]
dDeltaF_ij = results["dDelta_f"]
self._deltaF_mbar = deltaF_ij[0, self._lambda_array.shape[0] - 1]
self._dDeltaF_mbar = dDeltaF_ij[0, self._lambda_array.shape[0] - 1]
self._pmf_mbar = numpy.full(
Expand All @@ -138,7 +133,7 @@ def run_mbar(self, test_overlap=True):

##testing data overlap:
if test_overlap:
overlap_matrix = MBAR_obj.computeOverlap()
overlap_matrix = MBAR_obj.compute_overlap()
self._overlap_matrix = overlap_matrix["matrix"]

@property
Expand Down Expand Up @@ -255,13 +250,13 @@ def subsample_gradients(self):
shape=(self._gradients_kn.shape[0]), fill_value=numpy.nan
)
for i in range(g_k.shape[0]):
g_k[i] = timeseries.statisticalInefficiency(self._gradients_kn[i, :])
g_k[i] = timeseries.statistical_inefficiency(self._gradients_kn[i, :])
g = int(numpy.max(g_k))
# now we need to figure out what the indices in the data are for subsampling
indices_k = []
for i in range(g_k.shape[0]):
indices_k.append(
timeseries.subsampleCorrelatedData(self._gradients_kn[i, :], g=g)
timeseries.subsample_correlated_data(self._gradients_kn[i, :], g=g)
)
self._subsampled_N_k_gradients[i] = len(indices_k[i])
N_max = int(numpy.max(self._subsampled_N_k_gradients))
Expand Down Expand Up @@ -319,7 +314,7 @@ def subsample_energies(self):
# first we compute statistical inefficiency
g_k = numpy.full(shape=(self._energies_kn.shape[0]), fill_value=numpy.nan)
for i in range(g_k.shape[0]):
g_k[i] = timeseries.statisticalInefficiency(
g_k[i] = timeseries.statistical_inefficiency(
self._energies_kn[i, percentage_removal[i] :]
)
g = numpy.max(g_k)
Expand All @@ -330,7 +325,7 @@ def subsample_energies(self):
)
for i in range(g_k.shape[0]):
indices_k.append(
timeseries.subsampleCorrelatedData(self._energies_kn[i, :], g=g)
timeseries.subsample_correlated_data(self._energies_kn[i, :], g=g)
)
self._subsampled_N_k_energies[i] = len(indices_k[i])
# self._subsampled_N_k_energies = (numpy.ceil(self._N_k / g)).astype(int)
Expand Down