58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
# evaluating https://github.com/RafaelReyesCarmona/EMA
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import numpy as np
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import sys
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import matplotlib.pyplot as plt
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def map_trans(x, in_min, in_max, out_min, out_max):
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return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min
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with open(sys.argv[1], "r") as data:
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while True:
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line = data.readline()
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if not line.startswith('#'): break
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header = [e for e in line.strip().split(',') if e]
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print(header)
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log = np.genfromtxt(data, names=header, dtype=None, delimiter=',')
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print(log)
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dt = np.ediff1d(log['t'])
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dt = np.insert(dt, 0, 0)
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t = log['t']/1000
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fig, axs = plt.subplots(2, 1, layout='constrained', sharex=True)
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a_f = map_trans(log['a_raw_f'], 410, 3686, -180, +180)
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#axs[0].plot(t, map_trans(log['a_raw_r'], 410, 3686, -180, +180), 'g-')
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axs[0].plot(t, a_f, 'k.')
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axs[1].plot(t, log['sp_f']/10, 'b--')
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#axs[0].set_ylim(-270, 180)
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axs[0].set_xlabel('Raw Sensor Readings (Green: Rear, Blue: Front)')
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axs[0].grid(True)
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# simulate a Exponential Moving Average Filter
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amounts = [1,2,3,4,5,6]
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for amt in amounts:
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avg = [0]
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for i in range(len(a_f)-1):
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avg.append(avg[-1] + (a_f[i] - avg[-1])/(2**amt))
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axs[0].plot(t, np.array(avg))
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axs[0].legend(['Raw'] + ['K=%d'%amt for amt in amounts])
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plt.show()
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"""
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My thoughts as of 01JUN2024:
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K=1,2 too noisy (+/- 1.5 deg)
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K=3 smooths out pretty well and has low latency (150ms ish, +/- 1 deg)
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K=4 smooths out very well but has more latency (400ms ish, +/- 0.5 deg)
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K=5,6 seems too laggy (>1 second)
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""" |