molpx._bmutils.get_good_starting_point¶

molpx._bmutils.
get_good_starting_point
(cl, geom_samples, cl_order=None, strategy='smallest_Rgyr')¶ provided a pyemmacl object and a list of geometries, return the index of the clustercenter that’s most suited to start a minimally diffusing path.
Parameters:  cl (
pyemma.coordinates
clustering object) –  geom_samples (list of
mdtraj.Trajectory
objects corresponding to each clustercenter incl
) –  cl_order (None or iterable of integers) – The order of the list
geom_samples
may or may not correspond to the order ofcl
. Very often,geom_samples
is sorted in ascending order of a given coordinate while the clustercenters incl
are not.cl_order
represents this reordering, so thatgeom_samples[cl_order]
reproduces the order of the clusterscenters, so that finally:geom_samples[cl_order][i]
contains geometries sampled for thei
th clustercenter  strategy (str, default is 'smallest_Rgyr') –
 Which property gets optimized
 smallest_Rgyr:
look for the geometries with smallest radius of gyration(
mdtraj.compute_rg
), regardless of the population  most_pop: look for the clustercenter that’s most populated, regardless of the associated geometries
 most_pop_x_smallest_Rgyr: Mix both criteria. Weight Rgyr values with population to avoid highly compact but rarely populated structures
 bimodal_compact: assume the distribution of clustercenters is bimodal, then locate its centers and choose the one with smaller Rgyr
 bimodal_open: assume the distribution of clustercenters is bimodal, then locate its centers and choose the one with larger Rgyr
 smallest_Rgyr:
look for the geometries with smallest radius of gyration(
Returns: start_idx – The
mdtraj.Trajectory
ingeom_samples[start_idx]
satisfies best thestrategy
criterionReturn type: int, ndex of list
geom_samples
 cl (