def process(self):
# Grab only the new samples into a NumPy array
x = np.array(self.__l1[-self.__new_samples:])
# Filter the signal (detrend, LP, MA, etc…)
# ...
# ...
# ...
# Store the filtered data
ma = filt.moving_average(x, 20) # Compute Moving Average
dt = filt.detrend(ma) # Detrend the Signal
lp = filt.filter(self.__b, self.__a, dt) # Low-pass Filter Signal
grad = filt.gradient(lp) # Compute the gradient
x = filt.moving_average(
grad, 20) # Compute the moving average of the gradient
self.__filtered.add(x.tolist())
# Count the number of peaks in the filtered data
count, peaks = filt.count_peaks(x, self.__thresh_low,
self.__thresh_high)
# Update the step count and reset the new sample count
self.__steps += count
self.__new_samples = 0
# Return the step count, peak locations, and filtered data
return self.__steps, peaks, np.array(self.__filtered)
data = load_data("./data/offline_data.csv")
t = data[:, 0]
t = (t - t[0]) / 1e3
ax = data[:, 1]
ay = data[:, 2]
az = data[:, 3]
l1 = filt.l1_norm(ax, ay, az) # Compute the L1-Norm
print(len(l1))
ma = filt.moving_average(l1, 20) # Compute Moving Average
dt = filt.detrend(ma) # Detrend the Signal
freqs, power = filt.psd(l1, len(l1), 50) # Power Spectral Density
bl, al = filt.create_filter(3, 1, "lowpass", fs) # Low-pass Filter Design
lp = filt.filter(bl, al, dt) # Low-pass Filter Signal
grad = filt.gradient(lp) # Compute the gradient
grad_avg = filt.moving_average(
grad, 20) # Compute the moving average of the gradient
count, peaks = filt.count_peaks(grad_avg, t_low,
t_high) # Find & Count the Peaks
# Plot the results
plt.plot(t, grad_avg)
plt.title("Detected Peaks = %d" % count)
plt.plot(t[peaks], grad_avg[peaks], 'rx')
plt.plot(t, [t_low] * len(grad_avg), "b--")
plt.plot(t, [t_high] * len(grad_avg), "b--")
plt.show()