Why does the convolution/cross correlation truncate to 0?
Clash Royale CLAN TAG #URR8PPP Why does the convolution/cross correlation truncate to 0? Cross-correlation for uniformly sampled signals is defined as [1] Convolution for uniformly sampled signals is defined as [2] I am trying to cross-correlate or convolve two arrays of similar input data. These arrays are two similar time series voltage events that I am trying to align. However, the correlation and convolution only give back arrays full of 0. This seems strange to me, because the input arrays are observations of the same phenomena and should have some sort of overlap. I can clearly plot the input data: Whole Time Series: Zoomed in: This is the code I am running: def get_cross_corr(files): datalist = # open required files for correlation try: for f in files: fp = open(f, "rb") data = np.fromfile(fp,dtype=np.int16) datalist.append(data[0:int(len(data)/4)]) fp.close() except: print("could not ...