本文整理汇总了Python中msmbuilder.Trajectory.initialized_from_id方法的典型用法代码示例。如果您正苦于以下问题：Python Trajectory.initialized_from_id方法的具体用法？Python Trajectory.initialized_from_id怎么用？Python Trajectory.initialized_from_id使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类msmbuilder.Trajectory的用法示例。
在下文中一共展示了Trajectory.initialized_from_id方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: generate_job
# 需要导入模块: from msmbuilder import Trajectory [as 别名]
# 或者: from msmbuilder.Trajectory import initialized_from_id [as 别名]
def generate_job():
"""Generate a single job from the most recent MSMs
No parameters -- reads from the database and from the Project file to get
info.
Returns
-------
traj : models.Trajectory
An unsaved trajectory. Note that we "attach" the conformation that we want
to start from to the object as traj.init_pdb.
"""
# get the most recent build round
msmgroup = Session.query(MSMGroup).order_by(MSMGroup.id.desc()).first()
if msmgroup is None:
return _generate_equilibration_job()
# select the forcefield to shoot with
msm_i = multinomial([msm.model_selection_weight for msm in msmgroup.markov_models])
model_to_shoot_with = msmgroup.markov_models[msm_i]
forcefield = model_to_shoot_with.forcefield
# select the microstate to pull from
selected_microstate = multinomial(model_to_shoot_with.microstate_selection_weights)
# select the conformation to start from uniformly over all the confs
# in that microstate. Note that these confs count come from any of the
# forcefields. This is why its critical to have the joint clustering
confs_per_model = []
for msm in msmgroup.markov_models:
n_confs = 0
# it is possible that there is no inverse_assignments if this round
# was done before any trajs were run in this ff
# its also possible that this state was only populated by one of the ffs,
# and sense you can't pickle defaultdicts (they have lambda), msm.inv_assignments
# is a regular dict
if os.path.exists(msm.inverse_assignments_fn):
msm.inv_assignmnets = load_file(msm.inverse_assignments_fn)
if selected_microstate in msm.inv_assignmnets:
n_confs = len(msm.inv_assignmnets[selected_microstate])
confs_per_model.append(n_confs)
model_to_draw_from = msmgroup.markov_models[multinomial(confs_per_model)]
trjs, frames = model_to_draw_from.inv_assignmnets[selected_microstate]
r = np.random.randint(len(trjs))
#import IPython as ip; ip.embed()
trj_i, frame_i = trjs[r], frames[r]
conf = model_to_draw_from.trajectories[trj_i].extract_wet_frame(frame_i)
logger.info('Drew conformation from %s, shooting in %s', model_to_draw_from.forcefield.name, forcefield.name)
if forcefield == model_to_draw_from.forcefield:
# we're shooting in the same forcefield the conf was generated in
t = Trajectory(
forcefield=forcefield,
name='Started from round {}, microstate {} (ff {}, trj {}, frame {}) -- continuing in same ff'.format(msmgroup.id, selected_microstate, model_to_draw_from.forcefield.name, trj_i, frame_i),
mode='Production')
else:
# we're shooting in a new forcefield
# SHOULD WE DO EQUILIBRATION INSTEAD OF PRODUCTION?
# TJL sayz yes, we definitely should
# ======
t = Trajectory(
forcefield=forcefield,
name='Started from round {}, microstate {} (ff {}, trj {}, frame {}) -- switching forcfields'.format(msmgroup.id, selected_microstate, model_to_draw_from.forcefield.name, trj_i, frame_i),
mode='Production')
t.initialized_from_id = model_to_draw_from.trajectories[trj_i].id
t.initialized_from_frame = int(frame_i)
# this just "attaches" the conf object to t. the Trajectory model doesn't
# actually have an init_pdb field -- in the submit method of QMaster we'll
# save this conf to a file on disk
t.init_pdb = conf
return t