Data Standardization¶
New in version 0.1.
This function standardizes (zscore) the series according to equation
\(\textbf{x}_s = \frac{\textbf{x}  a}{b}\)
where \(\textbf{x}\) is time series to standardize, \(a\) is offset to remove and \(b\) scale to remove
See also: Data Destandardization
Usage Explanation¶
As simple as
xs = pa.standardize(x, offset=a , scale=b)
If the key arguments offset
and scale
are not provided
(example below) the mean value and standard deviation of x is used.
xs = pa.standardize(x)
Minimal Working Example¶
An example how to standarize (zscore) data:
>>> import numpy as np
>>> import padasip as pa
>>> x = np.random.random(1000)
>>> x.mean()
0.49755420774866677
>>> x.std()
0.29015765297767376
>>> xs = pa.standardize(x)
>>> xs.mean()
1.4123424652012772e16
>>> xs.std()
0.99999999999999989
Code Explanation¶

padasip.preprocess.standardize.
standardize
(x, offset=None, scale=None)[source]¶ This is function for standarization of input series.
Args:
 x : series (1 dimensional array)
Kwargs:
 offset : offset to remove (float). If not given, the mean value of x is used.
 scale : scale (float). If not given, the standard deviation of x is used.
Returns:
 xs : standardized series