Linear prediction extrapolation of 1D or 2D data.
Parameters : | data : ndarray
pred : int
slice : slice object, optional
order : int
mode : {‘f’, ‘b’, ‘fb’ or ‘bf’}
extend : {‘before’, ‘after’}
bad_roots : {‘incr’, ‘decr’, None, ‘auto’}
fix_mode : {‘on’, ‘reflect’}
mirror : {None, ‘0’, ‘180’}
method : {‘svd’, ‘qr’, ‘choleskey’, ‘tls’}
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Returns : | ndata : ndarray
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Notes
When given 2D data a series of 1D linear predictions are made to each row in the array, extending each by pred points. To perform a 2D linear prediction using a 2D prediction matrix use lp2d().
In forward-backward or backward-forward mode root stabilizing is done on both sets of signal roots as calculated in the first mode direction. After averaging the coefficient the roots are again stabilized.
When the append parameter does not match the LP mode, for example if a backward linear prediction (mode=’b’) is used to predict points after the trace (append=’after’), any root fixing is done before reversing the filter.