# Statistical tests

## Whitenoise check

PyEMD.checks.whitenoise_check(IMFs: ndarray, test_name: str = 'aposteriori', rescaling_imf: int = 1, alpha: float = 0.95)[source]

Whitenoise statistical significance test.

Performs whitenoise test as described by Wu & Huang [Wu2004].

Parameters:
IMFs: np.ndarray

(Required) numpy array containing IMFs computed from a normalized signal

test_name: str

(Optional) Test type. Supported values: ‘apriori’, ‘aposteriori’. (default ‘aposteriori’)

rescaling_imf: int

(Optional) ith IMF of the signal used in rescaling for ‘a posteriori’ test. (default 1)

alpha: float

(Optional) The percentiles at which the test is to be performed; 0 < alpha < 1; (default 0.95)

Returns:
Optional dictionary

Returns dictionary with keys and values as IMFs’ number and test result, respetively, Test results can be either 0 (fail) or 1 (pass). In case of problems with the input imfs, e.g. NaN values or no imfs, we return None.

References

[Wu2004]

Zhaohua Wu, and Norden E. Huang. “A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method.” Proceedings: Mathematical, Physical and Engineering Sciences, vol. 460, no. 2046, The Royal Society, 2004, pp. 1597–611, http://www.jstor.org/stable/4143111.

Examples

```>>> import numpy as np
>>> from PyEMD import EMD
>>> from PyEMD.checks import whitenoise_check
>>> T = np.linspace(0, 1, 100)
>>> S = np.sin(2*2*np.pi*T)
>>> emd = EMD()
>>> imfs = emd(S)
>>> significant_imfs = whitenoise_check(imfs, test_name='apriori')
>>> significant_imfs
{1: 1, 2: 1}
>>> type(significant_imfs)
<class 'dict'>
```