def __init__(self, num_samples, fs, data=None):
self.__steps = 0
self.__num_samples = num_samples
self.__fs = fs
self.__l1 = CircularList(data, num_samples)
self.__filtered = CircularList([], num_samples)
self.__b, self.__a = filt.create_filter(3, 1.2, "lowpass", fs)
self.__peak_arr = []
# Load the data as a 500x4 ndarray
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--")