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#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Author: A K Agrawal

Email: kumarabhishekbh8@gmail.com

"""

"""

Description:

In this tutorial we will a noisy signal using FFT technique

signal is noisy sin wave for which we have some idea of it's original frequency. similar to

chapter 2. the we will create a filter, multiply filter frequency response with signal response and take

inverse FFT of result, pass through DAC

"""

from signalUtility import functionGenerator

from signalUtility import oscilloscope

from signalUtility import sampler

from signalUtility import reconstructor

from scipy import signal as scipySignal

from copy import copy

import sys

import numpy as np

import scipy.fftpack as scipyFFT

from signalUtility import signalType

# Create three different types waveform

f1 = functionGenerator()

s1= copy(f1.sinWaveformGenerate(frequency = 100, noOfCycle=20, phase = 0))

# Add gaussian noise to signal 1

s2 = copy(f1.addNoise(s1,noiseType='gaussian', parameter1=0,parameter2=0.1))

# sample noisy signal, sampling frequency is 10 times of original signal about original signal

# this is s2 signal which we will be getting in real scenario, we assume that we have some idea

# (like it's frequency, if we don't have any previous information we can visualise the signal and try to extract some info) so we will be able to design filter.

adc = sampler()

Fs=10000

s3 = copy(adc.sampleSignal(s2,samplingFrequency=Fs))

o1=oscilloscope(title='Signal Visualisation')

o1.addWaveform(s1,plotColor='C0', plotLabel='Original Signal')

o1.addWaveform(s2,plotColor='C1', plotLabel='Noisy signal')

o1.addWaveform(s3,plotType='discrete',plotColor='C2', plotLabel='sampled discrete signal')

#FFT calculation for Signal

signalLen = len(s3.time)

fftLen=(int)(pow(2,np.ceil(np.log2(signalLen))))

fftPoints = copy(scipyFFT.fft(s3.value,n=fftLen))

freq = np.linspace(0,Fs,fftLen,endpoint=False)

o2 = oscilloscope(title="Frequency Response (FFT)",isFreqResponsePlotAlso='yes')

o2.addFrequencyResponse(freq,fftPoints,wc=Fs/2,freqView ='linear',ampView ='linear',label="signal FFT, analog freq vs amplitude")

# filters

lowPassFilterCutoff = 200

fc = lowPassFilterCutoff/Fs

wc = fc *(2*np.pi)

nyquistFrequency = np.pi

wc_normalized = wc/nyquistFrequency

b,a = scipySignal.butter(4,wc_normalized,btype='low',output='ba',analog=False)

# plot frequency response of filter

w,h = scipySignal.freqz(b,a,worN=fftLen,whole=True)

w = copy(w*(Fs/(2*np.pi)))

o2.addFrequencyResponse(w,h,wc=lowPassFilterCutoff,frequencyResponseColor='C1',freqView ='linear',ampView ='log',label="filter response, analog freq vs amplitude in db")

# filtering

filteredFFTAmplitude = np.multiply(fftPoints,h)

ifftSignal = copy(np.real(scipyFFT.ifft(filteredFFTAmplitude ,fftLen)))

ifftSignal = copy(ifftSignal[np.arange(0,len(s3.time))])

filteredDiscreteSignal = signalType(time =s3.time,value = ifftSignal)

#o1.addWaveform(filteredDiscreteSignal,plotType='discrete',plotColor='C2', plotLabel='Filtered discrete signal')

# reconstruction

dac = reconstructor()

reconstructedFilteredSignal = copy(dac.reconstructSignal(filteredDiscreteSignal,connectFilter='yes',lowPassCutoff=2000))

o1.addWaveform(reconstructedFilteredSignal,plotType='continious',plotColor='C3', plotLabel='filtered signal')