nomopy 0.1.2

Noise Modeling in Python

Noise Modeling in Python

Nomopy is statistical software for modeling and analyzing noise signals created by discrete level fluctuators. For details see our paper, or look through the code documentation.

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 Scales to HPC:
  • Algorithms optimized using vectorization and Numba for just-in-time compilation.
  • Highly parallel workloads scale easily to HPC using Dask.

Installation

Consider using our environment.yml file to install the recommended dependencies:

$ conda env create -f environment.yml

Then install nomopy (we're working on the pip install):

Git clone the repository and either:

>>> import sys
>>> sys.path.append('/path/to/nomopy/')

or try

$ cd nomopy
$ pip install -e .

Testing and coverage:

$ pytest --cov=nomopy --cov-report html --ignore=tests/test_numba_utilities.py
$ pytest --cov=nomopy --cov-append --cov-report html tests/test_numba_utilities.py

Coverage report is in htmlcov/index.html.

Documentation

See full documentation here.

Some examples can be found in the examples directory.

Quick Example

(See the full Jupyter notebook.)

from nomopy.fhmm import FHMM

Generate some simulated data :

N = 1    # Number of time series
T = 200  # Number of samples per time series
d = 2    # Number of hidden fluctuators
k = 2    # Number of states for each fluctuator
o = 1    # Observable dimension

W, A, C, pi = FHMM.generate_random_model_params(T, d, k, o, seed=36)
C = np.array([[0.001]])  # Set low noise level

X, states = FHMM(T=T, d=d, o=o, k=k, W_fixed=W, A_fixed=A, C_fixed=C, pi_fixed=pi)\
                .generate(N, T, return_states=True)
# X has shape (N, T, o)
plt.plot(X[0, :, 0])

Fitting a FHMM using the exact method:

fhmm = FHMM(T=T, d=d, o=o, k=k, em_max_iter=100, method='exact', verbose=False)
fhmm.fit(X)
fhmm.plot_fit()

Calculate the most likely (Viterbi) hidden state trajectories:

viterbi = fhmm.viterbi(0)  # Sample 0

fig, axs = plt.subplots(2, 1, figsize=(20, 8))
axs[0].plot(viterbi[:, 0, 0])  # shape=(T, d, k)
axs[1].plot(states[0, :, 0, 0], c='r')  # shape=(N, T, d, k)
axs[0].set_ylabel('Viterbi', fontsize=15)
axs[1].set_ylabel('Actual', fontsize=15)

fig, axs = plt.subplots(2, 1, figsize=(20, 8))
axs[0].plot(viterbi[:, 1, 0])
axs[1].plot(states[0, :, 1, 0], c='r')
axs[0].set_ylabel('Viterbi', fontsize=15)
axs[1].set_ylabel('Actual', fontsize=15)