watex.utils.exmath.smooth1d#

watex.utils.exmath.smooth1d(ar, /, drop_outliers=True, ma=True, absolute=False, view=False, x=None, xlabel=None, ylabel=None, fig_size=(10, 5))[source]#

Smooth one-dimensional array.

Parameters:

  • ar (ArrayLike 1d) – Array of one-dimensional

  • drop_outliers (bool, default=True) – Remove the outliers in the data before smoothing

  • ma (bool, default=True,) – Use the moving average for smoothing array value. This seems more realistic.

  • absolute (bool, default=False,) – keep postive the extrapolated scaled values. Indeed, when scaling data, negative value can be appear due to the polyfit function. to absolute this value, set absolute=True. Note that converting to values to positive must be considered as the last option when values in the array must be positive.

  • view (bool, default =False) – Display curves

  • x (ArrayLike, optional) – Abscissa array for visualization. If given, it must be consistent with the given array ar. Raises error otherwise.

  • xlabel (str, optional) – Label of x

  • ylabel (str, optional) – label of y

  • fig_size (tuple , default=(10, 5)) – Matplotlib figure size

Returns:

yc – Smoothed array value.

Return type:

ArrayLike

Examples

>>> import numpy as np
>>> from watex.utils.exmath import smooth1d
>>> # add Guassian Noise
>>> np.random.seed (42)
>>> ar = np.random.randn (20 ) * 20 + np.random.normal ( 20 )
>>> ar [:7 ]
array([6.42891445e+00, 3.75072493e-02, 1.82905357e+01, 2.92957265e+01,
       6.20589038e+01, 2.26399535e+01, 1.12596434e+01])
>>> arc = smooth1d (ar, view =True , ma =False )
>>> arc [:7 ]
array([12.08603102, 15.29819907, 18.017749  , 20.27968322, 22.11900412,
       23.5707141 , 24.66981557])
>>> arc = smooth1d (ar, view =True )# ma=True by default
array([ 5.0071604 ,  5.90839339,  9.6264018 , 13.94679804, 17.67369252,
       20.34922943, 22.00836725])