Limits constants

Introduced H2MM_C.optimization_limits package variable to let setting of num_cores, converged_min, max_iter and max_time limits on optimizations universally in addition to through kwargs.

Simulated Models

This latest release provides new functions that generate random state and photon trajectories having probabilities based on input models.

H2MM_C

Code is fully implemented in C, with cython wrappers for access via Python in formats such as Jupyter Notebooks.
This version is dramatically faster than previous versions, taking full advantage of the efficiency of C, as well as multi threading. For linux this is implemented using POSIX pthreads, while in Windows using the functions available in the windows.h header file.

Photon by photon hidden Markov modelling

This release contains a streamlined and faster jupyter notebook, and adds a short bit of code to calculate the modified BIC of the H2MM code

H2MMpy

Python implementation of H2MM originally published in Pirchi et. al. JPC 2016, 120, 13065
This code analyzes sequences of photon arrival times and colors and fits them to a hidden Markov model in an iterative process. A Viterbi path function also finds the most likely state-path given a H2MM model and a photon sequence.