1
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Hi I have a set of data electrical signal measurement in the time domain, may I seek some suggested on how can I filter this set of data.

The measurement was done over a period of 43 sec, at 200Hz per second, giving us around 8600-n data points.

I have tried using the moving average methods but it affects the accuracy of the reading.

Due to the data length, I have shorted the first cycle out of the 8 cycles.

Appreciate some suggestion in this. Thank you.

data = {0.867513, 0.855728, 0.853856, 0.86728, 0.888611, 0.899992,
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        1.26139, 1.26139, 1.26139, 1.26265, 1.26396, 1.26403, 1.26272, 
        1.26141, 1.2601, 1.25977, 1.23619, 1.20151, 1.20282, 1.20413, 
        1.24078, 1.26233, 1.26139, 1.26139, 1.26139, 1.20384, 1.17115, 
        1.16984, 1.17358, 1.24429, 1.25651, 1.25728, 1.25859, 1.25977, 
        1.25977, 1.26089, 1.26139, 1.26139, 1.25842, 1.23485, 1.23325, 
        1.23456, 1.24419, 1.26302, 1.26302, 1.26302, 1.26215, 1.26084, 
        1.25929, 1.25667, 1.25651, 1.25469, 1.25266, 1.25108, 1.24846, 
        1.24837, 1.24648, 1.24386, 1.24124, 1.23862, 1.2373, 1.04882, 
        0.922772, 0.917271, 0.920462, 1.16663, 1.2207, 1.2193, 1.21668, 
        1.21406, 1.21144, 1.20882, 1.2062, 1.20401, 1.20166, 1.18725, 
        1.18271, 1.17853, 1.17788, 1.18447, 1.18055, 1.17775, 1.17513, 
        1.17251, 1.17025, 1.17025, 1.16674, 1.16366, 1.16105, 1.15864, 
        1.15733, 1.15602, 1.15382, 1.15178, 1.14996, 1.14604, 1.15825, 
        1.14733, 1.13667, 1.13338, 1.11374, 1.1299, 1.13252, 1.1299, 1.12728, 
        1.12466, 1.12073, 1.11879, 1.11681, 1.11419, 1.11174, 1.11436, 
        1.11491, 1.11348, 1.10927, 1.10157, 1.09633, 1.09002, 1.08649, 
        1.08474, 1.0827, 1.08663, 1.08502, 1.0832, 1.08094, 1.07702, 1.07309, 
        1.06916, 1.06441, 1.06018, 1.05737, 1.05345, 1.05188, 1.05057, 
        1.04926, 1.0475, 1.04357, 1.03964, 1.03661, 1.03458, 1.03327, 
        1.03196, 1.02814, 1.02515, 1.02253, 1.02021, 1.0189, 1.01759, 
        1.01433, 1.01106, 1.00811, 1.00421, 1.0029, 1.00059, 0.997967, 
        0.994975, 0.990992, 0.985755, 0.981563, 0.977635, 0.974526, 0.973125, 
        0.970506, 0.967887, 0.964504, 0.960575, 0.956775, 0.954157, 0.953776, 
        0.952975, 0.948768, 0.940426, 0.937807, 0.936344, 0.940062, 0.943491, 
    0.942383, 0.942383, 0.94119, 0.939881, 0.938571, 0.9375, 0.9375, 
        0.9375, 0.936589, 0.935279, 0.922702, 0.867707, 0.87095, 0.871448, 
        0.895326, 0.932687, 0.92614, 0.926107, 0.927091, 0.927734, 0.927386, 
        0.926107, 0.926107, 0.925086, 0.924479, 0.92371, 0.921224, 0.921224, 
        0.921224, 0.923444, 0.926107, 0.926107, 0.926107, 0.925012, 0.925256, 
        0.926107, 0.925826, 0.923208, 0.920589, 0.919597, 0.920092, 0.921401, 
        0.922711, 0.922852, 0.922002, 0.924946, 0.932404, 0.931094, 0.932194, 
       0.868787, 0.815998, 0.816807, 0.815498, 0.896122, 0.920301, 0.919597, 
        0.919023, 0.916404, 0.917619, 0.917969, 0.917969, 0.917645, 0.916341, 
        0.916341, 0.917338, 0.917969, 0.855286, 0.634808, 0.636117, 0.63536, 
        0.759197, 0.917969, 0.917654, 0.912416, 0.911459, 0.910707, 0.911566, 
        0.916341, 0.916341, 0.916341, 0.91713, 0.917969, 0.917817, 0.916507, 
        0.916341, 0.916341, 0.916341, 0.916341, 0.916341, 0.916341, 0.916341, 
        0.916341};

enter image description here

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  • 1
    $\begingroup$ I cannot see a question in this post. $\endgroup$ – Szabolcs Oct 1 '19 at 10:46
  • $\begingroup$ What kind of filtering are you looking for? Having cleaner looking data? Removing the outliers and picking up the outliers? $\endgroup$ – Anton Antonov Oct 1 '19 at 11:25
  • 1
    $\begingroup$ Possible duplicate of Deleting noisy data from a plot (manually) and export the best remaining data $\endgroup$ – m_goldberg Oct 1 '19 at 15:13
  • $\begingroup$ Try a median filter: ListPlot[{data, MedianFilter[data, 15]}] looks pretty good. $\endgroup$ – bill s Oct 1 '19 at 23:54
1
$\begingroup$

Let's use this method. Play with parameters if it is necessary.

 xScale = 2.;
    xy = Transpose[{N[Range[Length[data]]]*(xScale/Length[data]), data}];

    start = {-xScale, Mean[data]};
    finish = {2*xScale, Mean[data]};
    graph = NearestNeighborGraph[Join[{start}, xy, {finish}], 10];
    graph = SetProperty[graph, 
       EdgeWeight -> 
        Apply[SquaredEuclideanDistance, EdgeList[graph], {1}]];

    path = FindShortestPath[graph, start, finish][[2 ;; -2]];
    ListLinePlot[path, PlotRange -> MinMax[data], 
     Prolog -> {Gray, Point[xy]}, PlotStyle -> Red, ImageSize -> 600]

enter image description here

|improve this answer|||||
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