add cogging torque harmonics analysis script

analysis/cogging_torque/cogging_harmonics.py

@@ -0,0 +1,52 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+encoder_cpr = 2400
+stator_slots = 12
+pole_pairs = 7
+
+N = data.size
+fft = np.fft.rfft(data)
+freq = np.fft.rfftfreq(N, d=1./encoder_cpr)
+
+harmonics = [0]
+harmonics += [(i+1)*stator_slots for i in range(pole_pairs)]
+harmonics += [pole_pairs]
+harmonics += [(i+1)*2*pole_pairs for i in range(int(stator_slots/4))]
+
+
+fft_sparse = fft.copy()
+indicies = np.arange(fft_sparse.size)
+mask = [i not in harmonics for i in indicies]
+fft_sparse[mask] = 0.0
+interp_data = np.fft.irfft(fft_sparse)
+
+#%%
+
+#plt.figure()
+plt.subplot(3, 1, 1)
+plt.plot(data, label='raw')
+plt.plot(interp_data, label='selected harmonics IFFT')
+plt.title('cogging map')
+plt.xlabel('counts')
+plt.ylabel('A')
+plt.legend(loc='best')
+
+#plt.figure()
+plt.subplot(3, 1, 2)
+plt.stem(freq, np.abs(fft)/N, label='raw')
+plt.stem(freq[harmonics], np.abs(fft_sparse[harmonics])/N, markerfmt='ro', label='selected harmonics')
+plt.title('cogging map spectrum')
+plt.xlabel('cycles/turn')
+plt.ylabel('A')
+plt.legend(loc='best')
+
+#plt.figure()
+plt.subplot(3, 1, 3)
+plt.stem(freq, np.abs(fft)/N, label='raw')
+plt.stem(freq[harmonics], np.abs(fft_sparse[harmonics])/N, markerfmt='ro', label='selected harmonics')
+plt.title('cogging map spectrum')
+plt.xlabel('cycles/turn')
+plt.ylabel('A')
+plt.legend(loc='best')