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State kinetics

Module Documentation: state_kinetics.py#

Overview#

The state_kinetics.py module provides functionality for simulating state transitions of fluorescent objects using a Markov Chain Monte Carlo (MCMC) approach. It includes classes and methods to calculate state transitions, track fluorescence history, and handle errors during simulation.

Classes#

ErnoMsg#

A data model representing the result of a state transition simulation, including success status, time, and end state.

Attributes:#
  • success (bool): Indicates whether the simulation was successful.
  • erno_time (Optional[float]): The time at which the error occurred (if any).
  • erno_end_state (Optional[State]): The state at which the error occurred (if any).

StateTransitionCalculator#

A class responsible for calculating state transitions of a fluorescent object over a given time duration.

Attributes:#
  • flurophoreobj (FluorescentObject): The fluorescent object for which state transitions are calculated.
  • time_duration (int | float): The duration of the simulation in seconds.
  • current_global_time (int): The current global time in milliseconds.
  • laser_intensity_generator (Callable): A function to generate laser intensities.
  • fluorescent_state_history (dict): A dictionary tracking fluorescence state history.
Methods:#

  • __init__(self, flurophoreobj: FluorescentObject, time_duration: int | float, current_global_time: int, laser_intensity_generator: Callable) -> None: Initializes the StateTransitionCalculator with the given parameters.

  • __call__(self) -> Tuple[dict, State, ErnoMsg]: Executes the state transition calculation and returns the fluorescence state history, final state, and error message.

  • _initialize_state_hist(self, time_pos: int, time_laser: float) -> dict: Initializes the state history dictionary with laser intensities.

  • MCMC(self) -> Tuple[State, ErnoMsg]: Performs the MCMC simulation to calculate state transitions and returns the final state and error message.

  • _find_transitions(self, statename: str) -> list: Cached method to find transitions from a given state.

Functions#

ssa_step(reaction_rates: Sequence[float | int]) -> tuple[float, int]#

Performs one step of the Stochastic Simulation Algorithm (SSA) to determine the next reaction and time step.

Parameters:#
  • reaction_rates (Sequence[float | int]): List of reaction rates.
Returns:#
  • dt (float): The time step to advance.
  • next_event (int): The index of the next reaction.