I am trying to understand Wavelet transform. So far I have understood the basic theory of it. But I am not able to get my head around how to interpret both coefficients.
I am using PyWavelets package of python, I have a time-series data for 1 year equally divided into 15-min time interval. I am trying to get high-frequency components for each day. Fourier transform gives the information about high-frequency. But I would like to get time-information also i.e. at what part during the day there occurs a spike(high-frequency). If someone could please help me figure out this and how to plot time-frequency. I have searched a lot but couldn't find a perfect answer. I am not looking for a free solution without trying; Here is everything I have tried
tdayde=d2012_2['Date'].iloc endde=d2012_2['Date'].iloc[-1] skiphr=datetime.timedelta(hours=24) blockde=tdayde+skiphr
I have timestamps attached to data, so to plot for each day I am using
healtharrayde=d2012_2 healtharrayde=healtharrayde[(healtharrayde['Date'] >= tdayde) & (healtharrayde['Date'] < blockde)] sig=list(healtharrayde['MCP']) cA, cD = pywt.dwt(sig, 'db2') plt.plot(cD) plt.plot(cA)
So if there are 96 values in array, both cD and cA are 48 values each. I would like to understand and plot cD(detail components) with respect to time, So how should I do? because there are 96 timestamps(MCP is column name for values)
Sorry if it's a silly question but I really need help. Thank you