""" 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')
from signalUtility import reconstructor from signalUtility import signalType from scipy import signal as scipySignal from copy import copy import sys import numpy as np import scipy.fftpack as scipyFFT # create analog signals. """ time duration of each signal = 1 sec to maintain same data length for each signal in 1 sec time duration dataPerCycle has been changed, because for addition each should have same length """ f_10 = functionGenerator(dataPerCycle=1000) f_100= functionGenerator(dataPerCycle=100) f_1000= functionGenerator(dataPerCycle=10) 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()
