Welcome to Nolds’ documentation!

The acronym Nolds stands for ‘NOnLinear measures for Dynamical Systems’. It is a small numpy-based library that provides an implementation and a learning resource for nonlinear measures for dynamical systems based on one-dimensional time series.

Nolds is hosted on GitHub. This documentation describes the latest version. A change log of the different versions can be found on GitHub.

For the impatient, here is a small example how you can calculate the lyapunov exponent of the logistic map with Nolds:

import nolds
import numpy as np
lm = nolds.logistic_map(0.1, 1000, r=4)
x = np.fromiter(lm, dtype="float32")
l = max(nolds.lyap_e(x))

Contents:

• nolds module
  • Algorithms
    • Lyapunov exponent (Rosenstein et al.)
    • Lyapunov exponent (Eckmann et al.)
    • Sample entropy
    • Hurst exponent
    • Correlation dimension
    • Detrended fluctuation analysis
    • Generalized Hurst Exponent (Barabási et al.)
    • Generalized Hurst Exponent (Di Matteo et al.)
  • Helper functions
    • binary_n()
    • logarithmic_n()
    • logarithmic_r()
    • expected_h()
    • expected_rs()
    • logmid_n()
    • lyap_r_len()
    • lyap_e_len()
  • Datasets
    • Benchmark dataset for hurst exponent
    • Tent map
    • Logistic map
    • Fractional brownian motion
    • Fractional gaussian noise
    • Quantum random numbers
    • Financial example datasets
    • Fractal data used by Barabasi et al. (1991)
• Nolds examples
  • Functions in nolds.examples
    • plot_lyap()
    • profiling()
    • weron_2002_figure2()
    • plot_hurst_hist()
    • hurst_compare_nvals()
• Nolds Unittests

Indices and tables

• Index

• Search Page