How Can I Find The Duration A List Element Exceeds A Certain Criteria?

I'm analysing EEG data for my honours project. Specifically, I'm analysing EEG bursts (i.e. the number of counts that it exceeds a criteria). The EEG values are recorded every seco

Solution 1:

You can get these regions using numpy. I leveraged part of an answer from another question to do this.

Results with index positions and number of points between them as well as screenshot of console/plot:

The code to do this (You had no data given, I used a sine-wave as base):

import numpy as np
import datetime
import matplotlib.pyplot as plot

base = datetime.datetime(2019, 1, 1,8,0,0)
tarr = np.array([base + datetime.timedelta(seconds=i) for i inrange(60*60)]) #  1 h

time = np.arange(0, 6*6, 0.01) *0.2;
amp = np.sin(time)

# get all indexes that interest me - yours would have the mean here# not used in rest of code: contains all indexes that are above 0.75
indx = np.nonzero(abs(amp) > 0.75)
# looks like: (array([ 425,  426,  427, ..., 3597, 3598, 3599], dtype=int64),)defcontiguous_regions(cond):
    """Credits: https://stackoverflow.com/a/4495197/7505395""""""Finds contiguous True regions of the boolean array "condition". Returns
    a 2D array where the first column is the start index of the region and the
    second column is the end index."""# Find the indicies of changes in "condition"
    d = np.diff(cond)
    idx, = d.nonzero() 

    # We need to start things after the change in "condition". Therefore, # we'll shift the index by 1 to the right.
    idx += 1if condition[0]:
        # If the start of condition is True prepend a 0
        idx = np.r_[0, idx]

    if condition[-1]:
        # If the end of condition is True, append the length of the array
        idx = np.r_[idx, cond.size] 

    # Reshape the result into two columns
    idx.shape = (-1,2)
    return idx


condition = np.abs(amp) > 0.75# create a plot visualization
fig, ax = plot.subplots()
ax.plot(tarr, amp)

# print values that fullfill our condition - also do some plottingfor start, stop in contiguous_regions(condition):
    segment = amp[start:stop]
    print (start, stop, f"Amount: {stop-start}")

    # plot some lines into the graph, off by 1 for stops in graph
    ax.vlines(x=tarr[start], ymin=amp[start]+(-0.1if amp[start]>0else0.1),
                             ymax=1.0if amp[start]>0else -1.0, color='r')
    ax.vlines(x=tarr[stop-1], ymin=amp[start]+(-0.1if amp[start]>0else0.1), 
                              ymax=1.0if amp[start]>0else -1.0, color='r')
    ax.hlines(y=amp[start]-0.08,xmin=tarr[start],xmax=tarr[stop-1])

# show plot    
plot.show()

The trick is to apply ndp.diff to the array with [False,True,...]'s in them that fullfill the condition. Then get the indexes from np.nonzero() result-tuple.