How to find the minimum voltage difference in a time series data after which a new waveform starts?

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I have a lot of time series data, one example could be found under this link: https://github.com/nazmul1001/test/blob/a23d4d8122bfeddbfa107bd6c3f2b1526ecc9073/3_8_2_3_8_2-351.csv

If I plot a graph using this code below, it looks like the following image.

    df = pd.read_csv('3_8_1_3_8_1-1.csv')
    df.columns = ['time', 'voltage']
    diff_1 = df['voltage'].diff()
    plt.figure(100,figsize=(10,4))
    plt.plot(df['time'], diff_1)
    plt.xlabel('Time in μs')
    plt.ylabel('Volatage in V')
    plt.tight_layout()

enter image description here In this image, I am assuming the red marked line is the shortest, I want to find out what is the exact voltage difference for this shortest line. Basically, I am trying to find out the minimum voltage difference in a measurement after which a new waveform starts.

I know that there are a lot of smaller voltage difference which is near to 0 and looks like the noise, so I am trying to find out, the minimum voltage difference above 2. For this example, what is the voltage difference which is above 2 and the minimum among all other waveform. Could anyone please help with this?

pandas matplotlib python
1 Answers

IIUC, that will give you the answer:

diff_1[abs(diff_1.values) > 2].sort_values(key=abs)

which returns

4971    2.023046
6719    2.043277
3139   -2.073623
141    -2.083738
7138   -2.103968
          ...   
640    -3.550446
890    -3.560562
7139   -3.732521
4889   -4.288858
5139   -4.450702
Name: voltage, Length: 66, dtype: float64

So, the minimum voltage difference greater than 2 is 2.023046

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