antropy.num_zerocross

antropy.num_zerocross(x, normalize=False, axis=- 1)

Number of zero-crossings.

Parameters

xlist or np.array

1D or N-D data.

normalizebool

If True, divide by the number of samples to normalize the output between 0 and 1. Otherwise, return the absolute number of zero crossings.

axisint

The axis along which to perform the computation. Default is -1 (last).

Returns

nzcint or float

Number of zero-crossings.

Examples

Simple examples

>>> import numpy as np
>>> import antropy as ant
>>> ant.num_zerocross([-1, 0, 1, 2, 3])
1

>>> ant.num_zerocross([0, 0, 2, -1, 0, 1, 0, 2])
2

Number of zero crossings of a pure sine

>>> import numpy as np
>>> import antropy as ant
>>> sf, f, dur = 100, 1, 4
>>> N = sf * dur # Total number of discrete samples
>>> t = np.arange(N) / sf # Time vector
>>> x = np.sin(2 * np.pi * f * t)
>>> ant.num_zerocross(x)
7

Random 2D data

>>> np.random.seed(42)
>>> x = np.random.normal(size=(4, 3000))
>>> ant.num_zerocross(x)
array([1499, 1528, 1547, 1457])

Same but normalized by the number of samples

>>> np.round(ant.num_zerocross(x, normalize=True), 4)
array([0.4997, 0.5093, 0.5157, 0.4857])

Fractional Gaussian noise with H = 0.5

>>> import stochastic.processes.noise as sn
>>> rng = np.random.default_rng(seed=42)
>>> x = sn.FractionalGaussianNoise(hurst=0.5, rng=rng).sample(10000)
>>> print(f"{ant.num_zerocross(x, normalize=True):.4f}")
0.4973

Fractional Gaussian noise with H = 0.9

>>> rng = np.random.default_rng(seed=42)
>>> x = sn.FractionalGaussianNoise(hurst=0.9, rng=rng).sample(10000)
>>> print(f"{ant.num_zerocross(x, normalize=True):.4f}")
0.2615

Fractional Gaussian noise with H = 0.1

>>> rng = np.random.default_rng(seed=42)
>>> x = sn.FractionalGaussianNoise(hurst=0.1, rng=rng).sample(10000)
>>> print(f"{ant.num_zerocross(x, normalize=True):.4f}")
0.6451