- class openpathsampling.analysis.ShootingPointAnalysis(steps, states, error_if_no_state=True)
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.
steps (iterable of
MCStepor None) – input MC steps to analyze; if None, no analysis performed
states (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
- __init__(steps, states, error_if_no_state=True)
__init__(steps, states[, error_if_no_state])
Analyze a list of steps, adding to internal results.
Analyzes final states from a path sampling step.
Calculate the (point-by-point) committor.
committor_histogram(new_hash, state[, bins])
Calculate the histogrammed version of the committor.
Build shooting point analysis from pairs of shooting point to final state.
If key is not found, d is returned if given, otherwise KeyError is raised.
as a 2-tuple; but raise KeyError if D is empty.
Create a new TransformedDict with this data and new hash.
Returns the key we use for hashing (the shooting snapshot).
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