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 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)[source]

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