watex.utils.smoothing#

watex.utils.smoothing(ar, /, drop_outliers=True, ma=True, absolute=False, interpolate=False, axis=0, view=False, fig_size=(7, 7), xlabel=None, ylabel=None, cmap='binary')[source]#

Smooth data along axis.

Parameters:

  • ar (ArrayLike 1d or 2d) – One dimensional or two dimensional array.

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

  • ma (bool, default=True,) – Use the moving average for smoothing array value along axis. This seems more realistic rather than using only the scaling method.

  • absolute (bool, default=False,) – keep positive 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.

  • axis (int, default=0) – Axis along with the data must be smoothed. The default is the along the row.

  • view (bool, default =False) – Visualize the two dimensional raw and smoothing grid.

  • xlabel (str, optional) – Label of x

  • ylabel (str, optional) – label of y

  • fig_size (tuple , default=(7, 5)) – Matplotlib figure size

  • cmap (str, default='binary') – Matplotlib.colormap to manage the view color

Returns:

arr0 – Smoothed array value.

Return type:

ArrayLike

Examples

>>> import numpy as np
>>> from watex.utils.exmath import smoothing
>>> # add Guassian Noises
>>> np.random.seed (42)
>>> ar = np.random.randn (20, 7 ) * 20 + np.random.normal ( 20, 7 )
>>> ar [:3, :3 ]
array([[ 31.5265026 ,  18.82693352,  34.5459903 ],
       [ 36.94091413,  12.20273182,  32.44342041],
       [-12.90613711,  10.34646896,   1.33559714]])
>>> arc = smoothing (ar, view =True , ma =False )
>>> arc [:3, :3 ]
array([[32.20356863, 17.18624398, 41.22258603],
       [33.46353806, 15.56839464, 19.20963317],
       [23.22466498, 13.8985316 ,  5.04748584]])
>>> arcma = smoothing (ar, view =True )# ma=True by default
>>> arcma [:3, :3 ]
array([[23.96547827,  8.48064226, 31.81490918],
       [26.21374675, 13.33233065, 12.29345026],
       [22.60143346, 16.77242118,  2.07931194]])
>>> arcma_1 = smoothing (ar, view =True, axis =1 )
>>> arcma_1 [:3, :3 ]
array([[18.74017857, 26.91532187, 32.02914421],
       [18.4056216 , 21.81293014, 21.98535213],
       [-1.44359989,  3.49228057,  7.51734762]])
On this page

This Page