larvaworld.lib.util.fitting
===========================

.. py:module:: larvaworld.lib.util.fitting


Functions
---------

.. autoapisummary::

   larvaworld.lib.util.fitting.simplex
   larvaworld.lib.util.fitting.beta0
   larvaworld.lib.util.fitting.critical_bout
   larvaworld.lib.util.fitting.exp_bout


Module Contents
---------------

.. py:function:: simplex(func: Callable[Ellipsis, float], x0: float | numpy.ndarray, args: tuple[Any, Ellipsis] = ()) -> float

   Nelder-Mead simplex optimization.

   Minimizes function using Nelder-Mead algorithm with tight tolerance.

   Args:
       func: Function to minimize.
       x0: Initial parameter value(s).
       args: Additional arguments passed to func.

   Returns:
       Optimized parameter value.

   Example:
       >>> def cost(x): return (x - 3)**2
       >>> result = simplex(cost, x0=0.0)


.. py:function:: beta0(x0: float, x1: float) -> float

   Compute beta function used in DEB textbook (p.58).

   Implements the beta function for Dynamic Energy Budget (DEB) modeling
   as defined in the DEB textbook, page 58.

   Args:
       x0: First parameter value
       x1: Second parameter value

   Returns:
       Real part of the computed beta function difference

   Example:
       >>> result = beta0(0.5, 1.0)
       >>> isinstance(result, float)
       True


.. py:function:: critical_bout(c: float = 0.9, sigma: float = 1, N: int = 1000, tmax: int = 1100, tmin: int = 1) -> int

   Stochastic bout duration with critical dynamics.

   Simulates behavioral bout durations using critical point dynamics
   with population size N and control parameters c, sigma.

   Args:
       c: Control parameter (default: 0.9).
       sigma: Noise parameter (default: 1).
       N: Population size (default: 1000).
       tmax: Maximum bout duration (default: 1100).
       tmin: Minimum bout duration (default: 1).

   Returns:
       Bout duration in timesteps.

   Example:
       >>> duration = critical_bout(c=0.9, sigma=1.0, N=1000)


.. py:function:: exp_bout(beta: float = 0.01, tmax: int = 1100, tmin: int = 1) -> int

   Stochastic bout duration with exponential dynamics.

   Simulates behavioral bout durations using exponential waiting time
   with rate parameter beta.

   Args:
       beta: Rate parameter for exponential process (default: 0.01).
       tmax: Maximum bout duration (default: 1100).
       tmin: Minimum bout duration (default: 1).

   Returns:
       Bout duration in timesteps.

   Example:
       >>> duration = exp_bout(beta=0.01, tmax=1000)