zeros = np.sum(cc_sums[0, :int(n_corr - moveouts.max() / step)] == 0.) if zeros > 10: print("{} correlation computations were skipped. Can be caused by" " zeros in data, or too low amplitudes (try to increase the " "gain).".format(zeros))
This snippet from fast_matched_filter.py will print a warning message if there are more than 10 zeroes in the first CC sum time series. I'm running into superfluous warnings when setting some templates (including the first one) to all 0 weights, so it's basically skipped.
Should I just deal with it, or is there a better way to do this?
zeros = np.sum(cc_sums[0, :int(n_corr - moveouts.max() / step)] == 0.) if zeros > 10: print("{} correlation computations were skipped. Can be caused by" " zeros in data, or too low amplitudes (try to increase the " "gain).".format(zeros))This snippet from fast_matched_filter.py will print a warning message if there are more than 10 zeroes in the first CC sum time series. I'm running into superfluous warnings when setting some templates (including the first one) to all 0 weights, so it's basically skipped.
Should I just deal with it, or is there a better way to do this?