openpathsampling.analysis.ShootingPointAnalysis
- class openpathsampling.analysis.ShootingPointAnalysis(steps, states, error_if_no_state=True)[source]
Container and methods for shooting point analysis.
This is especially useful for analyzing committors, which is automatically done on a per-configuration basis, and can also be done as a histogram.
- Parameters
steps (iterable of
MCStep
or None) – input MC steps to analyze; if None, no analysis performedstates (list of
Volume
) – volumes to consider as states for the analysis. For pandas output, these volumes must be named.error_if_no_state (bool, default True) – boolean flag to error on steps that don’t end in one of the states
Methods
__init__
(steps, states[, error_if_no_state])analyze
(steps)Analyze a list of steps, adding to internal results.
analyze_single_step
(step)Analyzes final states from a path sampling step.
clear
()committor
(state[, label_function])Calculate the (point-by-point) committor.
committor_histogram
(new_hash, state[, bins])Calculate the histogrammed version of the committor.
from_individual_runs
(run_results[, states])Build shooting point analysis from pairs of shooting point to final state.
get
(k[,d])items
()keys
()pop
(k[,d])If key is not found, d is returned if given, otherwise KeyError is raised.
popitem
()as a 2-tuple; but raise KeyError if D is empty.
rehash
(new_hash)Create a new TransformedDict with this data and new hash.
setdefault
(k[,d])step_key
(step)Returns the key we use for hashing (the shooting snapshot).
to_pandas
([label_function])Pandas dataframe.
update
([E, ]**F)If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values
()Attributes
progress