pykappa.analysis

Various model and simulation analysis tools.

class pykappa.analysis.Monitor(system)

Records the history of the values of observables in a system.

Parameters:

system (System)

history: dict[str, list[float | None]]

Maps observable names to their history

property system: System

The system being monitored.

property dataframe: DataFrame

The history of observable values as a pandas DataFrame.

update()

Record current time and observable values.

Return type:

None

measure(observable_name, time=None)

Get the value of an observable at a specific time.

Raises:

AssertionError – If simulation hasn’t reached the specified time.

Parameters:

• observable_name (str)

• time (float | None)

equilibration_start(observable_name, **kwargs)

Return the index of the history at which equilibration is detected, or None.

Parameters:

• observable_name (str) – Name of the observable to check. If None, checks all observables.

• **kwargs – Arguments passed to the equilibration detection function.

Return type:

int | None

plot(observables=None, combined=False, figsize=None)

Make a plot of all observables over time.

Parameters:

• combined (bool) – Whether to plot all observables on the same axes.

• figsize (tuple[float, float] | None) – The figure size (width, height) in inches.

• observables (list[str] | None) – Specific observables to plot. If None, plots all observables.

Return type:

Figure

pykappa.analysis.equilibration_start(values, times=None, tail_fraction=0.1, tolerance=0.01)

Checks whether the magnitude of the slope of the tail of the series relative to the mean is sufficiently small (below tolerance), and if so returns the first index of the stable tail. Time can be provided to account for non-uniform sampling intervals. The tail_fraction argument specifies the fraction of the time series to consider as the tail.

Parameters:

• values (list[float])

• times (list[float] | None)

• tail_fraction (float)

• tolerance (float)

Return type:

int | None