pyPeriod 0.1.0

Sethares and Staley's Periodicity Transforms

pyPeriod

Sethares and Staley's Periodicity Transforms in Python. See the paper here.

There is still much work to be done on these in terms of efficiency but it is a faithful implementation of the ideas and algorithms found in the paper. This is an alpha version so expect breaking changes in the future.

Usage

import numpy as np
from pyPeriod import Periods
from random import uniform

# define a signal
sr = 1000 # samplerate
f1 = 10 # frequency
f2 = 17
noise = 0.2 # percent noise

# Make some signals with some noise. The second signal has a slightly offset phase.
a = [np.sin((x*np.pi*2*f1)/sr)+(uniform(-1*noise, noise)) for x in range(2000)]
b = [np.sin(((x*np.pi*2*f2)/sr) + (np.pi*1.1))+(uniform(-1*noise, noise)) for x in range(2000)]
c = np.array(a)+np.array(b) # combine the signals

# normalize, though the algorithms can handle any range of signals (best if DC is removed)
c /= np.max(np.abs(c),axis=0)

p = Periods(c) # make an instance of Period

"""
The output of each algorithm are three arrays consisting of:
  periods: the integer values of the periods found
  powers: the floating point values of the amount of "energy" removed by each period found
  bases: the arrays, equal in length to the input signal length, that contain the periodicities found
"""

# find periodicities using the small-to-large algorithm
periods, powers, bases = p.small_to_large(thresh=0.1)

# find periodicities using the M-best algorithm
periods, powers, bases = p.m_best(num=10)

# find periodicities using the M-best gamma algorithm
periods, powers, bases = p.m_best_gamma(num=10)

# find periodicities using the best correlation algorithm
periods, powers, bases = p.best_correlation(num=10)

# find periodicities using the best frequency algorithm
periods, powers, bases = p.best_frequency(sr=sr, win_size=None, num=10)
## note that for best frequency, we need a samplerate. The window size, if not provided,
## is the same length as the input signal. A larger window is zero-padded, a smaller
## window is truncated (not good).

Algorithms

• Small-to-large
• M-best
• M-best gamma
• Best correlation
• Best frequency

Documentation

Forthcoming. Between the source and the paper, it should be pretty easy to understand in the meantime.

Requirements

• numpy>=1.19.2

Contributors

• Jacob Sundstrom: University of California, San Diego