how to find out peak rise and decay - scipy

Question

I have doing some siganl processing and I am new to it. I am using scipy.signal to do the calculations.
I am able to find the peak height, width, but I was wondering if I can also find the rise of peak time and decay time. That will be the distance from the left width point to the tallest peak point and then tallest peak point to right width point.
So, far I have this, which is from tutorial
import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks
x = electrocardiogram()[2000:4000]
peaks, _ = find_peaks(x, height=0)
plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.plot(np.zeros_like(x), "--", color="gray")
plt.show()
esults_full = peak_widths(x, peaks, rel_height=1)
I think I am looking for the first moment or derivative

Answer 1

This is a thing that depends on the type of the signal, for this signal in particular an approach that worked is to find all peaks then filter the peaks by a prominence threshold defined by the the midpoint in the prominence ranges.
Once I have the peaks of interest I used the positions of the previous and next peaks.
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks, peak_prominences
x = electrocardiogram()[2000:3500]
#b, a = butter(4, 0.001, 'high')
#x = lfilter(b, a, x)
peaks, _ = find_peaks(x)
prominences, _, _ = peak_prominences(x, peaks)
selected = prominences > 0.5 * (np.min(prominences) + np.max(prominences))
left = peaks[:-1][selected[1:]]
right = peaks[1:][selected[:-1]]
top = peaks[selected]
plt.figure(figsize=(14, 4))
plt.plot(x)
plt.plot(top, x[top], "x")
plt.plot(left, x[left], ".", markersize=20)
plt.plot(right, x[right], ".", markersize=20)
plt.show()
If you want to use height threshold it is interesting to remove frequencies lower than the signal frequency.
from scipy.signal import butter, lfilter
x = electrocardiogram()
plt.figure(figsize=(14, 4))
b, a = butter(4, 0.01, 'high')
plt.plot(x[2000:10000])
x = lfilter(b, a, x)
plt.plot(x[2000:10000])
plt.legend(['original', 'highpass filtered'])
About coding style preference, if you coming from MATLAB you may be used to everything in the global scope, but I always say that modules are your friends :). I would simply import scipy.signal instead of importing their member functions as global variables, you can use some alias for the modules like import matplotlib.pyplot as plt, and you can find what alias is commong to be used for each module, but this is more for programmer interoperability, not mandatory, so it is that I wrote the code in your style.
The derivatives
You can use rise = (peaks[top] - peaks[left]) / (top - left), and fall = (peaks[top] - peaks[right]) / (top - right), this is not the actual value of the derivatives, but are related featueres features.
Also if you want to find the max de