nomopy: Noise modeling in Python#
Statistical software for analyzing noise in the time domain, modeled as having been generated by a factorial hidden Markov process.
Features#
- Factorial Hidden Markov Model:
Exact E-step.
Mean Field Approximation E-step.
Gibbs Sampling E-Step.
Structured Variational Approximation E-step.
Viterbi algorithm.
- Uncertainty Quantification:
Hessian-based confidence intervals.
Bootstrapped confidence intervals.
- Model Selection:
Routines for cross validated model selection.
- Higher order statistics (HOS):
Second spectrum analysis.
Test for Gaussianity.
- Noise models:
Thermal two-level fluctuator model. Defined using physical properties of the fluctuators (energy barrier, energy bias, etc.).
- Optimized and Scalable to HPC:
Algorithms optimized using vectorization and Numba for just-in-time compilation.
Highly parallel workloads scale easily to HPC using Dask.