Flatiron.py

def filtering(x, alpha=50, beta=1):
    x_new = np.zeros_like(x)
    zero = x[0]
    for i in range(1, len(x)):
        zero = zero*(alpha-beta)/alpha + beta*x[i]/alpha
        x_new[i] =  x[i] - zero
    return x_new
  
  #Flattening a Normal signal
normal_signal_filter =  [None] * 3
normal_signal_filter[0] = filtering(df_signal_train.loc[0].values)
normal_signal_filter[1] = filtering(df_signal_train.loc[1].values)
normal_signal_filter[2] = filtering(df_signal_train.loc[2].values)

#Code to plot normal signal with flattened faulty signal
f, ax = plt.subplots(1, 2, figsize=(24, 8))

ax[0].plot((df_signal_train.loc[0].values), alpha=0.7);
ax[0].plot((df_signal_train.loc[1].values), alpha=0.7);
ax[0].plot((df_signal_train.loc[2].values), alpha=0.7);
ax[0].set_title('Normal signal')
ax[0].set_ylim([-100, 100])

ax[1].plot((normal_signal_filter)[0], alpha=0.7);
ax[1].plot((normal_signal_filter)[1], alpha=0.7);
ax[1].plot((normal_signal_filter)[2], alpha=0.7);
ax[1].set_title('filtered Normal signal')
ax[1].set_ylim([-100, 100])

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