How to remove constant part of a signal in python?

Question

I have a dataset where it is constant sometimes with small noise. It can be on the top, it can be in the middle. I want to remove all constant part of the signal so that it compress into a smaller length.

Lets say if 200 consicutive value is same (or in a threshold limit to be considered as constant) then consider that part as constant .

Any solution code or logical suggestion will be appriciated. Thanks for your time.

I have attached a sample data google drive link in .npy format.

In the below sample image I have indicated (red) which part I want to get rid of as an example:

Answer 1

I came up with the following solution. The answer from @rsenne helped me to start but that was not the solution I have wanted. Please let me know if there anything I should add in my solution to improve the performance.

import numpy as np

def remove_flat_segments(data, threshold, min_length):
       
       """
       Remove flat segments from data within a threshold and with a minimum length.
    
       Parameters:
        data (ndarray): Input data array.
        threshold (float): Threshold for flatness.
        min_length (int): Minimum length of flat segment to be removed.
    
       Returns:
        ndarray: Data with flat segments removed.
       """
    flat_segments = []
    start_idx = None
    
    for i in range(len(data)):
        if start_idx is None:
            start_idx = i
        elif abs(data[i] - data[i-1]) > threshold:
            if i - start_idx >= min_length:
                flat_segments.append((start_idx, i-1))
            start_idx = None
    
    if start_idx is not None and len(data) - start_idx >= min_length:
        flat_segments.append((start_idx, len(data)-1))
    
    if len(flat_segments) > 0:
        flat_indices = np.concatenate([np.arange(start, end+1) for start, end in flat_segments])
        return np.delete(data, flat_indices)
    else:
        return data


threshold = 0.01
min_length = 1000

filtered_data = remove_flat_segments(data, threshold, min_length)

The resulted image looks like: