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='log',label="signal FFT, analog freq vs amplitude") # filters
s_10 = copy(f_10.sinWaveformGenerate(frequency=10,noOfCycle=3)) s_100 = copy(f_100.sinWaveformGenerate(frequency=100,noOfCycle=30)) s_1000 = copy(f_1000.sinWaveformGenerate(frequency=1000,noOfCycle=300)) s_add = signalType() s_add.time = copy(s_10.time) s_add.value = copy(s_10.value + s_100.value +s_1000.value) s = copy(f_10.addNoise(s_add,noiseType='gaussian',parameter1=0, parameter2=0.3)) Fs = 10000 adc = sampler() s_sampled = adc.sampleSignal(s_add,samplingFrequency=Fs) o1 = oscilloscope(title="Fourier analysis ",isSubPlot='yes') o1.createSubplots(noOfRows=2,noOfColumns=2) o1.addToSubplot(s_add,addToRows=0,addToColumns=0,plotTitle='Signals', plotColor='C0') o1.addToSubplot(s_add,addToRows=0,addToColumns=1,plotTitle='Sampled Signals', plotType='discrete', plotColor='C0') #FFT calculation signalLen = len(s_sampled.time) fftLen=(int)(pow(2,np.ceil(np.log2(signalLen)))) fftPoints = copy(scipyFFT.fft(s_sampled.value,n=fftLen)) freq = np.linspace(0,Fs,fftLen,endpoint=True); fftSignal = signalType(freq[np.arange(0,fftLen/2,1,dtype=int)],20*np.log10(abs(fftPoints[np.arange(0,fftLen/2,1,dtype=int)]))) o1.addToSubplot(fftSignal,addToRows=1,addToColumns=0,plotTitle='signal FFT', plotColor='C0',xlabel="frequency(Hz)",ylabel="FFT values")
""" from signalUtility import functionGenerator from signalUtility import oscilloscope from signalUtility import sampler from signalUtility import reconstructor from copy import copy import sys # Task 1, create three different types waveform f1 = functionGenerator() s1= copy(f1.sinWaveformGenerate(frequency = 100, noOfCycle=10, phase = 0)) s2= copy(f1.pulseWaveformGenerate(frequency=50,phase=180,noOfCycle=5,dutyCycle=0.5, offset=0, amplitude=0.5)) s3= copy(f1.triangularWaveformGenerate(frequency=50,phase=90,noOfCycle=10,dutyCycle=0.5, offset=0, amplitude=0.5)) o1=oscilloscope(title='Signal Visualisation') o1.addWaveform(s1,plotColor='C0', plotLabel='S1') o1.addWaveform(s2,plotColor='C1', plotLabel='S2') o1.addWaveform(s3,plotColor='C2', plotLabel='S3') # task 2, add noise to signal 1 s4 = copy(f1.addNoise(s1,noiseType='gaussian', parameter1=0,parameter2=0.1)) s5 = copy(f1.addNoise(s1,noiseType='uniform', parameter1=-0.1,parameter2=0.1)) o2=oscilloscope(title='Noisy Signal Visualisation') o2.addWaveform(s1,plotColor='C0', plotLabel='S1') o2.addWaveform(s4,plotColor='C1', plotLabel='gaussian noise added with signal') o2.addWaveform(s5,plotColor='C2', plotLabel='uniform noise added with signal') # Task 3, sampling and reconstruction
s_10 = copy(f_10.sinWaveformGenerate(frequency=10, noOfCycle=3))
s_100 = copy(f_100.sinWaveformGenerate(frequency=100, noOfCycle=30))
s_1000 = copy(f_1000.sinWaveformGenerate(frequency=1000, noOfCycle=300))
s_add = signalType()
s_add.time = copy(s_10.time)
s_add.value = copy(s_10.value + s_100.value + s_1000.value)
s = copy(
f_10.addNoise(s_add, noiseType='gaussian', parameter1=0, parameter2=0.3))
Fs = 10000
adc = sampler()
s_sampled = adc.sampleSignal(s, samplingFrequency=Fs)
o1 = oscilloscope(title="plots", isSubPlot='yes')
o1.createSubplots(noOfRows=2, noOfColumns=2)
o1.addToSubplot(s,
addToRows=0,
addToColumns=0,
plotTitle="analog signal to be analysed")
#FFT calculation for Signal
signalLen = len(s_sampled.time)
fftLen = (int)(pow(2, np.ceil(np.log2(signalLen))))
fftPoints = copy(scipyFFT.fft(s_sampled.value, n=fftLen))
freq = np.linspace(0, Fs, fftLen, endpoint=False)
o2 = oscilloscope(title="Frequency Response (FFT)",
isFreqResponsePlotAlso='yes')
btype='bandpass')
w2, h2 = scipySignal.freqz(b2, a2)
# filter3
fc31 = 900
fc32 = 1100
wc31 = (fc31 / Fs) * (2 * np.pi)
wc32 = (fc32 / Fs) * (2 * np.pi)
wc31_normalised = wc31 / nyquistFrequency
wc32_normalised = wc32 / nyquistFrequency
b3, a3 = scipySignal.butter(4, [wc31_normalised, wc32_normalised],
btype='bandpass')
w3, h3 = scipySignal.freqz(b3, a3)
o3 = oscilloscope(title='filter response', isFreqResponsePlotAlso="yes")
o3.addFrequencyResponse(w1,
h1,
wc=wc11,
frequencyResponseColor='C0',
label='filter 1')
o3.addFrequencyResponse(w2,
h2,
wc=[wc21, wc22],
frequencyResponseColor='C1',
label='filter2')
o3.addFrequencyResponse(w3,
h3,
wc=[wc31, wc32],
frequencyResponseColor='C2',
label='filter3')
import sys
import numpy as np
# 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()
samplingFrequency = 1000
s3 = copy(adc.sampleSignal(s2, samplingFrequency=samplingFrequency))
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')
#******************************************************************************
"""
Digital filter design:
we will use 4th order butter worth low pass filter with cutoff at 200hz
"""
# filter design
lowPassFilterCutoff = 200