def load_metadata(traj_dir, top):
"""
Loads metadata of features and saves them.
:param traj_dir: directory containing trajectories
:param top: topology file name
:return: metadata data frame
"""
re_pattern = '(\w+)-([0-9]{3})k-([0-9])atm-prod([0-9]+\.[0-9]+).*BT([0-9]+)*'
captured_group_names = [
'PDB', 'Temp', 'Pressure', 'Prod_Round', 'Act_Site'
]
captured_group_transforms = [identity, float, float, identity, int]
time_step = 1 # in picoseconds
file_type = 'dcd'
parser = GenericParser(re_pattern,
group_names=captured_group_names,
group_transforms=captured_group_transforms,
top_fn=top,
step_ps=time_step)
meta = gather_metadata(os.path.join(traj_dir, "*.{}".format(file_type)),
parser)
save_meta(meta)
return meta
"""Find trajectories and associated metadata
msmbuilder autogenerated template version 2
created 2017-05-30T15:16:59.066163
please cite msmbuilder in any publications
"""
from msmbuilder.io import gather_metadata, save_meta, NumberedRunsParser
## Construct and save the dataframe
parser = NumberedRunsParser(
traj_fmt="trajectory-{run}.xtc",
top_fn="top.pdb",
step_ps=50,
)
meta = gather_metadata("trajs/*.xtc", parser)
save_meta(meta)
"""Find trajectories and associated metadata
{{header}}
Meta
----
depends:
- trajs
- top.pdb
"""
from msmbuilder.io import gather_metadata, save_meta, NumberedRunsParser
## Construct and save the dataframe
parser = NumberedRunsParser(
traj_fmt="trajectory-{run}.xtc",
top_fn="top.pdb",
step_ps=50,
)
meta = gather_metadata("trajs/*.xtc", parser)
save_meta(meta)