""" **Description**
A complex band pass filter produces a complex exponential incident
signal at a specified normalized frequency and adapts a forward complex
coefficient to produce a reference signal, which estimates a component
of interest in a primary signal. A normalized frequency and rate of
adaptation are specified.
.. math::
x_{n} = e^{\\ j\\ \\pi\\ \\phi_{n}}
.. math::
\\phi_{n+1} = \\phi_{n} + f_{n}
.. math::
y_{n} = b_{n} x_{n}
.. math::
e_{n} = d_{n} - y_{n}
.. math::
b_{n+1} = b_{n} + \\mu e_{n} x_{n}^{*}
**Example**
.. code-block:: python
from diamondback import ComplexBandPassFilter, ComplexExponentialFilter
import numpy
frequency = 0.1
x = numpy.linspace( -1.0e-4, 1.0e-4, 128 ) + frequency
# Create a primary signal.
d = ComplexExponentialFilter( phase = numpy.random.rand( 1 )[ 0 ] * 2.0 - 1.0 ).filter( x )
# Create an instance.
obj = ComplexBandPassFilter( frequency = frequency, rate = 5.0e-2 )
# Filter a primary signal.
obj.reset( d[ 0 ] )
y, e, b = obj.filter( d )
**License**
`BSD-3C. <https://github.com/larryturner/diamondback/blob/master/license>`_
© 2018 - 2024 Larry Turner, Schneider Electric Industries SAS. All rights reserved.
**Author**
Larry Turner, Schneider Electric, AI Hub, 2018-01-31.
"""
from diamondback.filters.ComplexExponentialFilter import ComplexExponentialFilter
from diamondback.filters.FirFilter import FirFilter
from typing import Tuple, Union
import numpy
import scipy
[docs]
class ComplexBandPassFilter( FirFilter ) :
""" Complex band pass filter.
"""
@property
def frequency( self ) :
return self._frequency
@frequency.setter
def frequency( self, frequency : float ) :
if ( ( frequency < -1.0 ) or ( frequency > 1.0 ) ) :
raise ValueError( f'Frequency = {frequency} Expected Frequency in [ -1.0, 1.0 ]' )
self._frequency = frequency
@property
def rate( self ) :
return self._rate
@rate.setter
def rate( self, rate : float ) :
if ( ( rate < 0.0 ) or ( rate > 1.0 ) ) :
raise ValueError( f'Rate = {rate} Expected Rate in [ 0.0, 1.0 ]' )
self._rate = rate
def __init__( self, frequency : float, rate : float ) -> None :
""" Initialize.
Arguments :
frequency : float - frequency normalized to Nyquist in [ -1.0, 1.0 ).
rate : float - in [ 0.0, 1.0 ].
"""
if ( ( frequency < -1.0 ) or ( frequency > 1.0 ) ) :
raise ValueError( f'Frequency = {frequency} Expected Frequency in [ -1.0, 1.0 ]' )
if ( ( rate < 0.0 ) or ( rate > 1.0 ) ) :
raise ValueError( f'Rate = {rate} Expected Rate in [ 0.0, 1.0 ]' )
super( ).__init__( b = numpy.array( [ numpy.finfo( float ).eps + 0j ] ), s = numpy.zeros( 1, complex ) )
self._filter = ComplexExponentialFilter( )
self._frequency = frequency
self._rate = rate
[docs]
def filter( self, d : Union[ list, numpy.ndarray ] ) -> Tuple[ numpy.ndarray, numpy.ndarray, numpy.ndarray ] : # type: ignore
""" Filters a primary signal and produces a reference signal.
Signals are Hilbert transformed to complex as necessary.
Arguments :
d : Union[ list, numpy.ndarray ] - primary signal.
Returns :
y : numpy.ndarray - reference signal.
e : numpy.ndarray - error signal.
b : numpy.ndarray - forward coefficient.
"""
if ( not isinstance( d, numpy.ndarray ) ) :
d = numpy.array( list( d ) )
if ( not len( d ) ) :
raise ValueError( f'D = {d}' )
if ( not numpy.iscomplex( d ).any( ) ) :
d = scipy.signal.hilbert( d )
x = self._filter.filter( numpy.ones( len( d ) ) * self.frequency )
y, e, b = numpy.zeros( len( x ), complex ), numpy.zeros( len( x ), complex ), numpy.zeros( len( x ), complex )
for ii in range( 0, len( x ) ) :
y[ ii ] = x[ ii ] * self.b[ 0 ]
e[ ii ] = d[ ii ] - y[ ii ]
b[ ii ] = self.b[ 0 ]
self.b[ 0 ] += self.rate * e[ ii ] * numpy.conjugate( x[ ii ] )
return y, e, b