MarkovAnalyzer 0.1.5

Automate Markov Process Characterization

MarkovAnalyzer: Simplifying Markov Analysis in Python

Authored by M. Sifft and D. Hägele We are excited to introduce MarkovAnalyzer, a high-speed Python toolkit designed for the analysis of hidden Markov models. This toolkit offers two distinct approaches: the well-established forward algorithm and our innovative polyspectra fitting method, both of which facilitate the study of complex Markov systems. The forward algorithm is a widely recognized technique, detailed on Wikipedia. In contrast, the polyspectra method is a novel contribution from our team at Ruhr University Bochum. This approach involves a comparative analysis between the experimental polyspectra—advanced extensions of the traditional power spectrum—and their theoretical models, which are derived from a given transition matrix and a measurement operator. This operator translates the state of the Markov system into measurable values. By matching the theoretical polyspectra with the experimental data, we can accurately estimate variable parameters within the hidden Markov model. The use of polyspectra in analyzing Markov processes comes with several significant benefits:

• Direct Input Utilization: Our method accepts the raw, experimentally measured Markov process data. This eliminates the need to categorize noisy measurements into distinct output levels, allowing for the analysis of data where noise is prevalent and distinct levels are obscured.

• Speed and Efficiency: Polyspectra-based analysis can outpace the forward algorithm, especially with large datasets. When dealing with terabyte-sized data, polyspectra need to be computed only once. These computations result in compact kilobyte-sized data, which are then used exclusively for the remainder of the analysis process.

MarkovAnalyzer is poised to revolutionize the way researchers approach the analysis of Markov systems, offering both efficiency and precision in one user-friendly package.

Installation

MarkovAnalyzer is available on pip and can be installed with

pip install markovanalyzer

Installation of Arrayfire

Besides running on CPU, MarkovAnalyzer offers GPU support for Nvidia and AMD cards. Depending on the hardware used, the usage of a GPU is highly recommended for Markov systems with more than about 100 states. A comprehensive installation guide for Linux + NVidia GPU can be found here. For GPU calculations the high performance library Arrayfire is used. The Python wrapper (see here) is automatically installed when installing SignalSnap, however, ArrayFire C/C++ libraries need to be installed separately. Instructions can be found can be found here and here.

Documentation

The documentation of the package can be found here. The package is divided into two parts: the polyspectra-calculator module, the fitting-tools module, and the forward module.

Polyspectra-Calculator Module

The Simulation Module allows for the calculation of the theoretical quantum polyspectra directly from the system's transition matrix.

Fitting-Tools Module

The Fitting-Tools Module enables a user-friendly characterization of a Markov system in the lab based on the polyspectra of a measurement of a Markov process. These polyspectra can be calculated via our SignalSnap package. After providing a model transition matrix with one or more variable parameters, these parameters are estimated by fitting the theoretical model prediction of the polyspectra to their measured counterparts.

Forward Module

Here, the Forward Algorithm for the estimation of Markov models is implemented.

Example: Characterization of a Two-State Markov Model via Polyspectra

We want to deduce a Markov model (i.e., it's transition matrix) from the observation of the Markov process. We are given data, that looks like this:

This is only a short excerpt of the full 6 min dataset. Using the SignalSnap library we are firstly calculating the polyspectra of that measurement. More details about the SignalSnap Code can be found on its GitHub page.

from signalsnap import SpectrumCalculator, SpectrumConfig, PlotConfig
import numpy as np
import h5py

Here is polyspectra are calculated and stored.

path = 'example_data/long_measurement.h5'
group_key = 'day1'
dataset = 'measurement1'

config = SpectrumConfig(dataset=dataset, group_key=group_key, path=path, f_unit='Hz', 
                        spectrum_size=150, f_max=2000, order_in=[1,2,3,4], 
                        backend='cpu')

spec = SpectrumCalculator(config)

f, s, serr = spec.calc_spec()

plot_config = PlotConfig(plot_orders=[2,3,4], arcsinh_plot=False, arcsinh_const=0.0002)
fig = spec.plot(plot_config)

path = 'example_data/two_state_example_spectra.pkl'
spec.save_spec(path, remove_S_stationarity=True)

Characterization is performed by fitting the theoretical polyspectra of a Markov model with variable parameters to their experimental counterparts calculated above. We are assuming a two-state model for the system that produced the data; hence, we begin with defining such a model with two variable transition rates.
The system undergoes transitions from the 0 to 1 state and from the 1 to the 0 state at rates gamma_01 and gamma_10, respectively. Each needs to be associated with a measurement value. From the histogram above we know that the value of the 0 state might be around 0, whereas the value of the 1 state might be around 26. Since we don't know for sure, we leave these measurement values also as variable parameters n_0 and n_1.

import markovanalyzer as ma

def set_system(params):
      
    rates = {'0->1': params['gamma_01'],
             '1->0': params['gamma_10']}
             
    m_op = np.array([params['n_0'],params['n_1']])
    
    markov_system = ma.System(rates, m_op)

    return markov_system

Now, we can set start values and bounds for the fit of the parameters. A parameter c is always part of the fit and acconts for constant white noise outset in the power spectrum.

system_fit = ma.FitSystem(set_system)

parameter = {'gamma_01': [2.0396975e+04, 0, 1e5, True],
             'gamma_10': [1.0345057e+04, 0, 1e5, True],
             'n_0': [0, 0, 1e8, True],
             'n_1': [20, 0, 1e8, True],
             'c': [-2.7651282e-01 , -1e14, 1e14, True]}

path = 'example_data/two_state_example_spectra.pkl'

result = system_fit.complete_fit(path, parameter, 
                        method='least_squares', xtol=1e-6, ftol=1e-6, show_plot=True, fit_modus='order_based',
                        fit_orders=(1,2,3,4), beta_offset=False)