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# -*- coding: utf-8 -*-
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"""
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Created on Tue Nov 13 15:53:23 2012
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@author: pedroluizcoelhorodrigues
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"""
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import numpy as np
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from bin16 import *
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class Filter_Coefficients():
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"""List of coefficients of a IIR filter of order 2"""
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def __init__(self,a0=0,a1=0,a2=0,b0=0,b1=0,b2=0):
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self.b0 = b0
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self.b1 = b1
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self.b2 = b2
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self.a0 = a0
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self.a1 = a1
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self.a2 = a2
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class Filter(object):
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"""Basic Filter of Order 2"""
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def __init__(self,Coefficients,x1=0,x2=0,y1=0,y2=0):
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self.Coefficients = Coefficients
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self.x1 = x1
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self.x2 = x2
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self.y1 = y1
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self.y2 = y2
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def print_coefficients(self):
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print 'a0 = ', self.Coefficients.a0
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print 'a1 = ', self.Coefficients.a1
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print 'a2 = ', self.Coefficients.a2
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print 'b0 = ', self.Coefficients.b0
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print 'b1 = ', self.Coefficients.b1
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print 'b2 = ', self.Coefficients.b2, "\n"
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def convolve(self,input):
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B = np.array([self.Coefficients.b0,self.Coefficients.b1,self.Coefficients.b2])
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A = np.array([self.Coefficients.a1,self.Coefficients.a2])
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y = np.zeros(len(input))
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y[0] = round(1.0/(self.Coefficients.a0) * (np.dot(B,np.array([input[0],self.x1,self.x2])) - np.dot(A,np.array([self.y1,self.y2]))))
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y[0] = trunc16(y[0])
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y[1] = round(1.0/(self.Coefficients.a0) * (np.dot(B,np.array([input[1],input[0],self.x1])) - np.dot(A,np.array([y[0],self.y1]))))
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y[1] = trunc16(y[1])
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for j in range(2,len(input)):
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y[j] = trunc16(round(1.0/(self.Coefficients.a0) * (np.dot(B,np.array([input[j],input[j-1],input[j-2]])) - np.dot(A,np.array([y[j-1],y[j-2]])))))
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self.x1 = input[j]
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self.x2 = input[j-1]
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self.y1 = y[j]
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self.y2 = y[j-1]
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return y
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def reset(self):
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self.x1 = 0
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self.x2 = 0
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self.y1 = 0
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self.y2 = 0
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def impulsive_response(self, N):
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delta = np.zeros(N)
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delta[0] = 1
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return self.convolve(delta)
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class FilterGlobal(object):
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"""Global filter with other basic filters (of order 2) in cascade"""
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def __init__(self,Filters):
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self.Filter = Filters
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def convolve(self,x):
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y = np.zeros(len(x))
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y = self.Filter[0].convolve(x)
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if len(self.Filter) > 1:
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for j in range(1,len(self.Filter)):
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y = self.Filter[j].convolve(y)
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return y
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def impulsive_response(self, N):
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delta = np.zeros(N)
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delta[0] = 1
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return self.convolve(delta)
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def reset(self):
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for j in range(len(self.Filter)):
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self.Filter[j].reset()
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