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Find the Threshold Threshold value in Matlab timelist data, then ignore subsequent crosses for 60 seconds before finding the next Threshold Cross

It's a little difficult to explain. I have a time series of data formatted like this: https://docs.google.com/spreadsheets/d/1B8mN0uD-t4kQr2U20gS713ZFHN6IgGB7OMR3-pqJjrw/edit?usp=sharing

This data represents voltage records at 0.01 s intervals. When built it looks like this:

http://i.imgur.com/yatlBLt.jpg.

Basically, I want to find the time at which the first peak occurs in each very narrow pair (i.e. at ~ .1, .75, 1.6, etc.).

The time values ​​are in a separate array, but the index values ​​(row numbers) match between the two sets.

Any ideas on how to do this?

My initial attempt was something like this from the MATLAB manual

function [edges2] = risingEdge2(time, data)
threshold = 0.4;
offsetData = [data(2:end); NaN];
edges2 = find(data < threshold & offsetData > threshold);
end

I couldn't find a good way to ignore for n seconds after the first peak ... I also get a lot more peaks than expected ... possibly due to noisy data.

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2 answers


For data data, the following approach seems to work.

%// Define parameters
window_size = 200;
stepsize = 0.4; %// to be used for quantizing data into three levels - 0, 0.4, 0.8

%// Perform a sliding max to get rid of the dips within the spikes
slmax_data = nlfilter(data,[window_size 1],@max);

enter image description here

%// Quantize sliding max data to three levels as the plot seem to suggest
quantized_slmax_data = round((slmax_data-min(slmax_data))./stepsize);

enter image description here

If you zoom in above, you will see ridges around the high peaks -



enter image description here

%// Get sliding mode to get rid of the short ledges around the high peaks
slmax_mode_data = nlfilter(quantized_slmax_data,[window_size 1],@mode);

enter image description here

%// Finally, find the indices where the mode data jump from 0 to 1 only, which
%// correspond to the start of spikes
index = find(diff(slmax_mode_data)==1)+window_size/2;

Output -

index =
         682
        8048
       16487
       24164
       31797
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Here - find all the growing edges, then find those that are very close to each other and take the first one.

rising_edges = find(diff(data > .3) > 0);
first_of_paired_edges = find(diff(time(rising_edges)) < 500);

first_rising_edge_times = time(rising_edges(first_of_paired_edges));


Then you can slide the edge to a peak.

first_peak_times = time(arrayfun( @(n) n+find(diff(data(n+[0:1000]) < 0, 1, 'first'), 
                        rising_edges(first_of_paired_edges));
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