def test_grompp_singulariy(self):
returncode = grompp(properties=self.properties, **self.paths)
assert fx.not_empty(self.paths['output_tpr_path'])
assert gmx_check(self.paths['output_tpr_path'],
self.paths['ref_output_tpr_path'],
gmx=self.properties.get('gmx_path', 'gmx'))
assert fx.exe_success(returncode)
def launch(self) -> int:
"""Execute the :class:`GromppMdrun <gromacs.grompp_mdrun.GromppMdrun>` object."""
fu.log(f'Calling Grompp class', self.out_log, self.global_log)
grompp_return_code = grompp(input_gro_path=self.input_gro_path,
input_top_zip_path=self.input_top_zip_path,
output_tpr_path=self.output_tpr_path,
input_cpt_path=self.input_cpt_path,
input_ndx_path=self.input_ndx_path,
input_mdp_path=self.input_mdp_path,
properties=self.properties_grompp)
fu.log(f'Grompp return code: {grompp_return_code}', self.out_log,
self.global_log)
if not grompp_return_code:
fu.log(f'Grompp return code is correct. Calling MDRun class',
self.out_log, self.global_log)
mdrun_return_code = mdrun(input_tpr_path=self.output_tpr_path,
output_trr_path=self.output_trr_path,
output_gro_path=self.output_gro_path,
output_edr_path=self.output_edr_path,
output_log_path=self.output_log_path,
output_xtc_path=self.output_xtc_path,
output_cpt_path=self.output_cpt_path,
output_dhdl_path=self.output_dhdl_path,
properties=self.properties_mdrun)
fu.log(f'MDRun return code: {mdrun_return_code}', self.out_log,
self.global_log)
else:
return 1
return mdrun_return_code
def via_gromacs(cls,
parmed_obj,
file_name,
file_path="./",
num_steps=5000 * 500,
write_out_freq=5000,
num_threads=1,
minimisation_mdp=None,
equilibration_mdp=None,
production_mdp=None,
tmp_dir=None,
report_equilibration=False,
report_production=False,
debug=False,
stages=["minimisation", "equilibration", "production"],
**kwargs): #TODO infer threads based on system size
"""
Simulation via GROMACS will be added in the future.
based on cell size, make recomendation on number of threads to use
Parameters
------------
parmed_obj : parmed.structure
Parmed object of the fully parameterised simulated system.
file_name : str
No file type postfix is necessary
file_path : str
Default to current directory
platform : str
The computing architecture to do the calculation, default to CUDA, CPU, OpenCL is also possible.
num_steps : int
Number of production simulation to run, default 2,500,000 steps, i.e. 5 ns.
write_out_freq : int
Write out every nth frame of simulated trajectory, default to every 5000 frame write out one, i.e. 10 ps per frame.
Returns
--------
path : str
The absolute path where the trajectory is written to.
"""
from biobb_md.gromacs.make_ndx import make_ndx, MakeNdx
# from biobb_md.gromacs.grompp_mdrun import grompp_mdrun
from biobb_md.gromacs.mdrun import mdrun
from biobb_md.gromacs.grompp import grompp
from biobb_common.tools.file_utils import zip_top
assert tmp_dir is None or os.path.realpath(
file_path) != os.path.realpath(
tmp_dir
), "Simulation results will not be stored in a temporary directory"
if debug:
import shutil
tmp_dir = "{}/.debug/".format(file_path)
if os.path.isdir(tmp_dir):
shutil.rmtree(tmp_dir)
os.mkdir(tmp_dir)
elif tmp_dir is None:
tmp_dir = tempfile.mkdtemp(
dir=file_path
) #FIXME just change this to debug dir in dubug mode
else:
try:
os.mkdir(tmp_dir)
except:
raise ValueError(
"Cannot create an empty temporary directory for storing intermediate files"
)
parmed_obj.residues[0].name = "LIG"
parmed_obj = parmed.gromacs.gromacstop.GromacsTopologyFile(
).from_structure(parmed_obj)
parmed_obj.defaults.fudgeLJ = 0.5
parmed_obj.defaults.fudgeQQ = 0.83333
parmed_obj.defaults.gen_pairs = "yes"
# Create prop dict and inputs/outputs
xtc_file = '{}/{}.xtc'.format(file_path, file_name)
gro_file = "{}/{}.gro".format(file_path, file_name)
top_file = "{}/{}.top".format(tmp_dir, "topol")
topzip_file = "{}/{}.zip".format(tmp_dir, "topol")
# parmed_obj.save("{}/{}.pdb".format(tmp_dir, stage), overwrite=True)
parmed_obj.save(gro_file, overwrite=True)
parmed_obj.save(top_file, overwrite=True)
index_file = "{}/{}.ndx".format(tmp_dir, "index")
prop = {
# "can_write_console_log" : False,
'selection': "! r LIG"
}
# Create and launch bb
# tmp = MakeNdx(input_structure_path = gro_file,
# output_ndx_path = index_file,
# properties = prop)
# import logging
# tmp.out_log = logging.Logger("x")
# tmp.err_log = logging.Logger("y")
# tmp.launch()
# print(getattr(tmp, "out_log"))
# print(tmp.err_log)
make_ndx(input_structure_path=gro_file,
output_ndx_path=index_file,
properties=prop)
####################################################
zip_top(topzip_file, top_file)
stage = "minimisation"
if stage in stages:
mdp_dict = mdp2dict(get_data_filename("{}.mdp".format(stage)))
if eval("{}_mdp".format(stage)) is not None:
mdp_dict.update(eval("{}_mdp".format(stage)))
next_gro_file = "{}/{}.gro".format(tmp_dir, stage)
# grompp_mdrun(input_gro_path = gro_file,
# input_ndx_path = index_file,
# input_top_zip_path= topzip_file,
# # input_mdp_path = "{}.mdp".format(stage),
# output_trr_path = "{}/{}.trr".format(tmp_dir, stage),
# output_gro_path = next_gro_file,
# output_edr_path = "{}/{}.edr".format(tmp_dir, stage),
# output_log_path = "{}/{}.log".format(tmp_dir, stage),
# output_xtc_path = "{}/{}.xtc".format(tmp_dir, stage),
# num_threads_omp = num_threads,
# properties = {
# "mdp" : mdp_dict
# }
# )
grompp(
input_gro_path=gro_file,
input_ndx_path=index_file,
input_top_zip_path=topzip_file,
output_tpr_path="{}/{}.tpr".format(tmp_dir, stage),
# input_mdp_path = "{}.mdp".format(stage),
properties={"mdp": mdp_dict})
mdrun(
input_tpr_path="{}/{}.tpr".format(tmp_dir, stage),
output_trr_path="{}/{}.trr".format(tmp_dir, stage),
output_gro_path=next_gro_file,
output_edr_path="{}/{}.edr".format(tmp_dir, stage),
output_log_path="{}/{}.log".format(tmp_dir, stage),
output_cpt_path="{}/{}.cpt".format(tmp_dir, stage),
output_xtc_path="{}/{}.xtc".format(tmp_dir, stage),
properties={
"num_threads": num_threads,
"num_threads_omp": num_threads,
"num_threads_omp_pme": num_threads,
}
# num_threads_omp = 1 #XXX seems for minimisation speed is very slow when multiple threads are used, especially on cluster. Maybe need better handle
)
gro_file = next_gro_file
###################################################333
stage = "equilibration"
if stage in stages:
mdp_dict = mdp2dict(get_data_filename("{}.mdp".format(stage)))
if eval("{}_mdp".format(stage)) is not None:
mdp_dict.update(eval("{}_mdp".format(stage)))
next_gro_file = "{}/{}.gro".format(tmp_dir, stage)
grompp(
input_gro_path=gro_file,
input_ndx_path=index_file,
input_top_zip_path=topzip_file,
output_tpr_path="{}/{}.tpr".format(tmp_dir, stage),
# input_mdp_path = "{}.mdp".format(stage),
properties={"mdp": mdp_dict})
mdrun(input_tpr_path="{}/{}.tpr".format(tmp_dir, stage),
output_trr_path="{}/{}.trr".format(tmp_dir, stage),
output_gro_path=next_gro_file,
output_edr_path="{}/{}.edr".format(tmp_dir, stage),
output_log_path="{}/{}.log".format(tmp_dir, stage),
output_cpt_path="{}/{}.cpt".format(tmp_dir, stage),
output_xtc_path="{}/{}.xtc".format(tmp_dir, stage),
properties={
"num_threads": num_threads,
"num_threads_omp": num_threads,
"num_threads_omp_pme": num_threads,
})
gro_file = next_gro_file
######################################################3
stage = "production"
if stage in stages:
mdp_dict = mdp2dict(get_data_filename("{}.mdp".format(stage)))
if eval("{}_mdp".format(stage)) is not None:
mdp_dict.update(eval("{}_mdp".format(stage)))
next_gro_file = "{}/{}.gro".format(tmp_dir, stage)
grompp(
input_gro_path=gro_file,
input_ndx_path=index_file,
input_top_zip_path=topzip_file,
output_tpr_path="{}/{}.tpr".format(tmp_dir, stage),
# input_mdp_path = "{}.mdp".format(stage),
properties={"mdp": mdp_dict})
mdrun(input_tpr_path="{}/{}.tpr".format(tmp_dir, stage),
output_trr_path="{}/{}.trr".format(tmp_dir, stage),
output_gro_path=next_gro_file,
output_edr_path="{}/{}.edr".format(tmp_dir, stage),
output_log_path="{}/{}.log".format(tmp_dir, stage),
output_cpt_path="{}/{}.cpt".format(tmp_dir, stage),
output_xtc_path=xtc_file,
properties={
"num_threads": num_threads,
"num_threads_omp": num_threads,
"num_threads_omp_pme": num_threads,
})
# gro_file = next_gro_file
######################################################3
if debug is not True and tmp_dir is not None:
import shutil
shutil.rmtree(tmp_dir)
return os.path.abspath(xtc_file)