def test_mdrun(self, tmpdir):
"""Check if grompp can be run successfully at all"""
f = self.mdp
c = self.conf
processed = self.processed
with tmpdir.as_cwd():
tpr = grompp(f, c, processed, prefix="reference")
reference_trr = mdrun(tpr, prefix="reference")
df1 = rerun_energy(tpr, reference_trr, prefix="reference")
scaled = "scaled.top"
args = Namespace(banned_lines='', input=processed, output=scaled, scale_lipids=1.0, scale_protein=1.0)
partial_tempering(args)
assert os.path.exists(scaled), "failed to produce {0}".format(scaled)
tpr = grompp(f, c, scaled, prefix="scaled")
df2 = rerun_energy(tpr, reference_trr, prefix="scaled")
assert_frame_equal(df1.sort(axis=1), df2.sort(axis=1), check_names=True)
scaled = "scaled.top"
args = Namespace(banned_lines='', input=processed, output=scaled, scale_lipids=1.0, scale_protein=0.5)
partial_tempering(args)
tpr = grompp(f, c, scaled, prefix="scaled")
df3 = rerun_energy(tpr, reference_trr, prefix="scaled")
# print(df1, df1.columns)
# print(df3, df3.columns)
unscaled_terms = ['Time (ps)', 'Improper Dih.']
scaled_terms = ['Proper Dih.']
assert_frame_equal(df1[unscaled_terms].sort(axis=1), df3[unscaled_terms].sort(axis=1), check_names=True)
assert_frame_equal(df1[scaled_terms].sort(axis=1), 2*df3[scaled_terms].sort(axis=1), check_names=True)
def test_mdrun(self, tmpdir):
"""Check if grompp can be run successfully at all"""
f = self.mdp
c = self.conf
processed = self.processed
with tmpdir.as_cwd():
tpr = grompp(f, c, processed, prefix="reference")
reference_trr = mdrun(tpr, prefix="reference")
df1 = rerun_energy(tpr, reference_trr, prefix="reference")
scaled = "scaled.top"
args = Namespace(banned_lines='',
input=processed,
output=scaled,
scale_lipids=1.0,
scale_protein=1.0)
partial_tempering(args)
assert os.path.exists(scaled), "failed to produce {0}".format(
scaled)
tpr = grompp(f, c, scaled, prefix="scaled")
df2 = rerun_energy(tpr, reference_trr, prefix="scaled")
assert_frame_equal(df1.sort(axis=1),
df2.sort(axis=1),
check_names=True)
scaled = "scaled.top"
args = Namespace(banned_lines='',
input=processed,
output=scaled,
scale_lipids=1.0,
scale_protein=0.5)
partial_tempering(args)
tpr = grompp(f, c, scaled, prefix="scaled")
df3 = rerun_energy(tpr, reference_trr, prefix="scaled")
# print(df1, df1.columns)
# print(df3, df3.columns)
unscaled_terms = ['Time (ps)', 'Improper Dih.']
scaled_terms = ['Proper Dih.']
assert_frame_equal(df1[unscaled_terms].sort(axis=1),
df3[unscaled_terms].sort(axis=1),
check_names=True)
assert_frame_equal(df1[scaled_terms].sort(axis=1),
2 * df3[scaled_terms].sort(axis=1),
check_names=True)
# GromacsWrapper: gw-partial_tempering.py
# Copyright (c) 2009 Oliver Beckstein <*****@*****.**>
# Released under the GNU Public License 3 (or higher, your choice)
# See the file COPYING for details.
from gromacs.scaling import partial_tempering
import argparse
description="""
Modify gromacs processed topology (processed.top, generated by `grompp -pp` command)
for running with solute tempering replica exchange (REST2).
"""
def parse_args():
parser = argparse.ArgumentParser(description=description)
parser.add_argument("--scale_protein", type=float, help="scale protein interactions by this scaling factor (0-1)")
parser.add_argument("--scale_lipids", type=float, help="scale lipid interactions by this scaling factor (0-1)")
parser.add_argument("--scale_protein_lipids", type=float, help="scale lipid-lipid interactions by this scaling factor (0-1)")
parser.add_argument("input", help="input topology (processed.top)")
parser.add_argument("output", help="output topology")
parser.add_argument("--banned_lines", default="", help="line numbers of dihedrals/impropers that one wishes to exclude from scaling")
return parser.parse_args()
args = parse_args()
partial_tempering(args)
