keras_lstm_multi_generate_fake_data_normal.py

# make some noisy but smooth looking data
data = np.sqrt(np.random.rand(N_DATA_ORIG,N_FEATURES))
df_data = pd.DataFrame(data)
df_data = df_data.rolling(window=N_ROLLING).mean()
df_data = df_data.dropna()
df_data = df_data.head(N_DATA)
print(df_data.shape)
data = df_data.values
# plot the normal healthy data
fig, ax = plt.subplots(num=None, figsize=(14, 6), dpi=80, facecolor='w', edgecolor='k')
size = len(data)
for x in range(data.shape[1]):
    ax.plot(range(0,size), data[:,x], '-', linewidth=1)