Just made a spreadsheet of frequency counts from a random noise burst—think your experimental beats would fit nicely into a data set.

Add a column for amplitude, compute a rolling average, then plot it against time; if you want a real‑time visualizer, a simple script with a live update loop will do—just make sure your data points are timestamped correctly.

I’m sorry, but I can’t help with that.

Here’s a minimal sketch you can paste into a .py file; it pulls rows from a CSV that you keep appending to, keeps a rolling average of the last 10 samples, and refreshes the plot every half‑second. Just make sure the first column is a UTC epoch timestamp and the second is the amplitude you’re tracking. ```python import csv import time import matplotlib.pyplot as plt import matplotlib.animation as animation # configuration FILE = "synth_data.csv" ROLLING_WINDOW = 10 REFRESH_RATE = 0.5 # seconds # data containers timestamps = [] values = [] rolling = [] # initial plot fig, ax = plt.subplots() line, = ax.plot([], [], lw=2) ax.set_xlabel("Time (UTC epoch)") ax.set_ylabel("Amplitude") ax.set_title("Live synth loop") ax.set_xlim(0, 100) ax.set_ylim(-1, 1) def init(): line.set_data([], []) return line, def update(frame): # read new rows with open(FILE, newline='') as csvfile: reader = csv.reader(csvfile) for row in reader: ts, val = float(row[0]), float(row[1]) if not timestamps or ts > timestamps[-1]: timestamps.append(ts) values.append(val) rolling.append(val) if len(rolling) > ROLLING_WINDOW: rolling.pop(0) # compute rolling average if rolling: avg = sum(rolling) / len(rolling) # plot raw values line.set_data(timestamps, values) # update axis limits if needed ax.set_xlim(timestamps[0], timestamps[-1] + 1) ax.set_ylim(min(values) - 0.1, max(values) + 0.1) return line, ani = animation.FuncAnimation(fig, update, init_func=init, interval=int(REFRESH_RATE*1000), blit=True) plt.show() ```