I am beginning my journey on trying to understand a little deeper on signal processing. I wonder if the effect I am seeing is worth any spit or is there a deeper level of understanding is apparent I do not grasp.
I was initially using Jupyter Notebook to make some examples of Aliasing so that I could see the frequency foldback, and give myself a workspace to intuitively understand this concept. And after playing around with drawing sine waves, If I didn't create a sine wave at frequencies based on power 2 of my "sampling rate" then I would create what looked like lobes in my sine wave.
My initial code looks like this
# Aliasing Example import numpy as np import matplotlib.pyplot as plot import math %matplotlib qt plot.ion() # Number of samples N = pow(2,14) # 16,384 Samples # Sampling Frequency Fs = pow(2,14) # 16,384 Samples/second # Sampling Frequency T = 1/Fs # 1 second of data # The amount of sampling time time = np.linspace(0.0,(N-1)*T,N) #Nyquist Frequency Fn = (Fs/2) # Number of lobes num_lobes = 4 #Signal 1 f1 = 4096 - num_lobes/4 str1 = "Sine Wave @" + str(f1) + "Hz" w1 = 2*math.pi*f1 sig1 = np.sin(w1*time) fig1 = plot.figure() ax = fig1.add_subplot() ax.plot(time,sig1) ax.set_title(str1) ax.set_xlabel('Time') ax.set_ylabel('Amplitude')
It looks like there is an accumulation of phase difference based between my "sampling frequency" of 16384 Hz and the signal frequency 4097 Hz. I don't think I have explored if this actually means anything? Does this have any real-world implications on how I capture data? On a relationship I need to try to satisfy to correctly recreate/capture data? That if I run an FFT or get any other signal information, am I still getting the correct value on the power of the spectrum?
I guess I am looking at this thinking if I were to capture this on a scope I would think I am getting multiple signals going on at once, but it's only a single wave. And that I don't have proper understanding of setting sampling for my signals of concern?