fluopy.analysis

Analysis of a photophysical simulation.

Classes

Analysis

Container of simulation-derived statistical attributes and methods.

Module Contents

class fluopy.analysis.Analysis(simulation: fluopy.simulation.Simulation)

Container of simulation-derived statistical attributes and methods.

Variables:

• simulation (fluopy.simulation.Simulation) – Container for simulation-associated attributes.

• frequency_transitions (1-D array_like) – Simulated relative frequencies of each transition.

• frequency_states (dict) – Name of fluorophores as keys and their state’s simulated relative frequencies (array) as values.

• transition_time_distributions (Collection) – Contains 1-D array_like for each transition (time until the transition).

• lifetime_distributions (dict) – Name of fluorophores as keys and collections of their state’s simulated lifetimes (1-D array_like) as values.

• mean_transition_times (1-D array_like) – Simulated means of time until transition.

• mean_lifetimes (dict) – Name of fluorophores as keys and their state’s simulated lifetime means (array) as values.

• state_occupations (dict) – Name of fluorophores as keys and their state’s simulated probability of being occupied at any given point in time (array) as values.

simulation

frequency_transitions

frequency_states

mean_transition_times

is_absorbing() → bool

Check whether fluorophores reached Markovian absorbing states.

Returns:

is_abs – Whether at least one of the fluorophores has reached a Markovian absorbing state.

Return type:

bool

get_transition_occurrences() → numpy.typing.NDArray[numpy.float64]

Get the relative frequencies of transitions.

Returns:

frequency_transitions – Simulated relative frequencies of each transition.

Return type:

npt.NDArray[np.float64]

get_state_occurrences() → dict[str, numpy.typing.NDArray[numpy.float64]]

Get the relative frequencies of states.

Returns:

frequency_states – Name of fluorophores as keys and their state’s simulated relative frequencies (array) as values.

Return type:

dict[str, npt.NDArray[np.float64]]

get_lifetimes() → tuple[list[numpy.typing.NDArray[numpy.float64]], dict[str, numpy.typing.NDArray[numpy.float64]]]

Get the lifetime distributions of states and the time until occurrence distributions of transitions. Note: if transition of interest is energy transfer, the time to transition is only collected from the donor’s point of view.

Returns:

• transition_time_distributions (list[npt.NDArray[np.float64]]) – Contains 1-D array_like for each transition (time until the transition).

• lifetime_distributions (dict[str, npt.NDArray[np.float64]]) – Name of fluorophores as keys and collections of their state’s simulated lifetimes (1-D array_like) as values.

infer_stats() → tuple[dict[str, numpy.typing.NDArray[numpy.float64]], dict[str, numpy.typing.NDArray[numpy.float64]]]

Infers statistics of states based on lifetime distributions and frequencies.

Returns:

• mean_lifetimes (dict[str, npt.NDArray[np.float64]]) – Name of fluorophores as keys and their state’s simulated lifetime means (array) as values.

• state_occupations (dict[str, npt.NDArray[np.float64]]) – Name of fluorophores as keys and their state’s simulated probability of being occupied at any given point in time (array) as values.

get_fluorescence_lifetimes(fluorophore: str | None = None) → numpy.typing.NDArray[numpy.float64]

Get the fluorescence lifetime (i.e., S1 lifetime) of the specified fluorophore. Note that this does not consider whether the S1 state decays via photon emission.

Parameters:

fluorophore – The name of the fluorophore whose fluorescence lifetime is to be returned.

Returns:

fluorescence_lifetimes – The fluorescence lifetimes of the specified fluorophore.

Return type:

npt.NDArray[np.float64]

get_emitting_transition_lifetimes(fluorophore: str | None = None) → numpy.typing.NDArray[numpy.float64]

Get the lifetimes of the emitting transitions (i.e., S1 deexcitation via photon emission) of the specified fluorophore.

Parameters:

fluorophore – The name of the fluorophore whose fluorescence lifetime is to be returned.

Returns:

exp_fluorescence_lifetimes – The fluorescence lifetimes (photon emssion) of the specified fluorophore.

Return type:

npt.NDArray[np.float64]

plot_frequency_transitions(prediction: fluopy.prediction.Prediction | None = None, diff_dist: bool = True, **kwargs: Any) → numpy.typing.NDArray[matplotlib.axes.Axes]

Plot frequencies of transitions.

Parameters:

• prediction – Container of mathematically derived statistical attributes and methods.

• diff_dist – Whether to plot energy transfers distance-specific or not.

• kwargs – kwargs for fluopy.figure.universal_figure

Returns:

Contains matplotlib.axes._subplots.AxesSubplots.

Return type:

npt.NDArray[mplAxes]

plot_frequency_states(prediction: fluopy.prediction.Prediction | None = None, **kwargs: Any) → numpy.typing.NDArray[matplotlib.axes.Axes]

Plot frequencies of states.

Parameters:

• prediction – Container of mathematically derived statistical attributes and methods.

• kwargs – kwargs to universal_figure

Returns:

Contains matplotlib.axes._subplots.AxesSubplots.

Return type:

npt.NDArray[mplAxes]

plot_mean_transition_times(prediction: fluopy.prediction.Prediction | None = None, diff_dist: bool = True, **kwargs: Any) → numpy.typing.NDArray[matplotlib.axes.Axes]

Plot mean times until transitions occur.

Parameters:

• prediction – Container of mathematically derived statistical attributes and methods.

• diff_dist – Whether to plot energy transfers distance-specific or not.

• kwargs – kwargs to universal_figure

Returns:

Contains matplotlib.axes._subplots.AxesSubplots.

Return type:

npt.NDArray[mplAxes]

plot_mean_lifetimes(prediction: fluopy.prediction.Prediction | None = None, **kwargs: Any) → numpy.typing.NDArray[matplotlib.axes.Axes]

Plot mean lifetimes of states.

Parameters:

• prediction – Container of mathematically derived statistical attributes and methods.

• kwargs – kwargs to universal_figure

Returns:

Contains matplotlib.axes._subplots.AxesSubplots.

Return type:

npt.NDArray[mplAxes]

plot_state_occupations(prediction: fluopy.prediction.Prediction | None = None, **kwargs: Any) → numpy.typing.NDArray[matplotlib.axes.Axes]

Plot state occupation times (relative total time spent in state).

Parameters:

• prediction – Container of mathematically derived statistical attributes and methods.

• kwargs – kwargs to universal_figure

Returns:

Contains matplotlib.axes._subplots.AxesSubplots.

Return type:

npt.NDArray[mplAxes]

plot_lifetime_distributions(fluorophore: str, state_identity: int, prediction: fluopy.prediction.Prediction | None = None, **kwargs: Any) → numpy.typing.NDArray[matplotlib.axes.Axes]

Plot lifetime distributions of states.

Parameters:

• fluorophore – The name of the fluorophore whose state’s distribution is to be shown.

• state_identity – The identity of the state whose distribution is to be shown.

• prediction – Container of mathematically derived statistical attributes and methods.

• kwargs – kwargs to universal_figure

Returns:

Contains matplotlib.axes._subplots.AxesSubplots.

Return type:

npt.NDArray[mplAxes]

plot_transition_time_distributions(fluorophore: str, transition_id: int, prediction: fluopy.prediction.Prediction | None = None, **kwargs: Any) → numpy.typing.NDArray[matplotlib.axes.Axes]

Plot distributions of time until transition occurs.

Parameters:

• fluorophore – The name of the fluorophore whose transition’s distribution is to be shown.

• transition_id – The identity of the transition whose distribution is to be shown.

• prediction – Container of mathematically derived statistical attributes and methods.

• kwargs – kwargs to universal_figure

Returns:

Contains matplotlib.axes._subplots.AxesSubplots.

Return type:

npt.NDArray[mplAxes]