Filter periodical sharp spikes in experimental data











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I have experimental data with periodic sharp artefact spikes, which mask the real signal that could be seen as several negative peaks in the first third part on the curve:



enter image description here



Is it possible somehow to filter these spikes? or at least significantly reduce them? Here is data set:



data = {{5.`, 2.41204`}, {10.`, 2.40477`}, {15.`, 2.40154`}, {20.`, 
2.4094`}, {25.`, 2.41592`}, {30.`, 2.40706`}, {35.`,
2.41802`}, {40.`, 2.4171`}, {45.`, 2.40979`}, {50.`,
2.4127`}, {55.`, 2.40254`}, {60.`, 2.40753`}, {65.`,
2.41049`}, {70.`, 2.40521`}, {75.`, 2.40446`}, {80.`,
2.40692`}, {85.`, 2.40509`}, {90.`, 2.40604`}, {95.`,
2.40464`}, {100.`, 2.40448`}, {105.`, 2.41854`}, {110.`,
2.41627`}, {115.`, 2.40941`}, {120.`, 2.40443`}, {125.`,
2.39909`}, {130.`, 2.41194`}, {135.`, 2.40494`}, {140.`,
2.40801`}, {145.`, 2.41532`}, {150.`, 2.41512`}, {155.`,
2.41328`}, {160.`, 2.41383`}, {165.`, 2.40806`}, {170.`,
2.3958`}, {175.`, 2.41296`}, {180.`, 2.41556`}, {185.`,
2.40119`}, {190.`, 2.27056`}, {195.`, 2.51902`}, {200.`,
2.41532`}, {205.`, 2.36206`}, {210.`, 2.37837`}, {215.`,
2.38771`}, {220.`, 2.39959`}, {225.`, 2.40557`}, {230.`,
2.40373`}, {235.`, 2.4045`}, {240.`, 2.39692`}, {245.`,
2.38983`}, {250.`, 2.39646`}, {255.`, 2.40411`}, {260.`,
2.40749`}, {265.`, 2.41679`}, {270.`, 2.42152`}, {275.`,
2.40963`}, {280.`, 2.39891`}, {285.`, 2.39964`}, {290.`,
2.39939`}, {295.`, 2.40453`}, {300.`, 2.40663`}, {305.`,
2.41569`}, {310.`, 2.40495`}, {315.`, 2.40756`}, {320.`,
2.41375`}, {325.`, 2.41212`}, {330.`, 2.41838`}, {335.`,
2.41029`}, {340.`, 2.40475`}, {345.`, 2.40698`}, {350.`,
2.41066`}, {355.`, 2.40286`}, {360.`, 2.40045`}, {365.`,
2.38019`}, {370.`, 2.1133`}, {375.`, 2.74495`}, {380.`,
2.44314`}, {385.`, 2.23574`}, {390.`, 2.26553`}, {395.`,
2.28845`}, {400.`, 2.3039`}, {405.`, 2.32833`}, {410.`,
2.35769`}, {415.`, 2.36831`}, {420.`, 2.38712`}, {425.`,
2.39121`}, {430.`, 2.3947`}, {435.`, 2.39861`}, {440.`,
2.40759`}, {445.`, 2.41661`}, {450.`, 2.41583`}, {455.`,
2.41684`}, {460.`, 2.40925`}, {465.`, 2.40916`}, {470.`,
2.41589`}, {475.`, 2.41347`}, {480.`, 2.40624`}, {485.`,
2.40825`}, {490.`, 2.41021`}, {495.`, 2.40547`}, {500.`,
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2.40647`}, {520.`, 2.40802`}, {525.`, 2.41601`}, {530.`,
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2.38785`}, {550.`, 2.1242`}, {555.`, 2.74989`}, {560.`,
2.47186`}, {565.`, 2.22649`}, {570.`, 2.25904`}, {575.`,
2.27855`}, {580.`, 2.30019`}, {585.`, 2.31269`}, {590.`,
2.33149`}, {595.`, 2.35533`}, {600.`, 2.37581`}, {605.`,
2.38236`}, {610.`, 2.39662`}, {615.`, 2.38875`}, {620.`,
2.3945`}, {625.`, 2.40292`}, {630.`, 2.41223`}, {635.`,
2.41755`}, {640.`, 2.41581`}, {645.`, 2.42747`}, {650.`,
2.42243`}, {655.`, 2.40486`}, {660.`, 2.41097`}, {665.`,
2.41289`}, {670.`, 2.4112`}, {675.`, 2.41057`}, {680.`,
2.40854`}, {685.`, 2.40255`}, {690.`, 2.40663`}, {695.`,
2.41292`}, {700.`, 2.39958`}, {705.`, 2.40153`}, {710.`,
2.41089`}, {715.`, 2.41834`}, {720.`, 2.41014`}, {725.`,
2.38069`}, {730.`, 2.18214`}, {735.`, 2.78697`}, {740.`,
2.43799`}, {745.`, 2.2292`}, {750.`, 2.2685`}, {755.`,
2.27852`}, {760.`, 2.29429`}, {765.`, 2.31846`}, {770.`,
2.33485`}, {775.`, 2.34926`}, {780.`, 2.36237`}, {785.`,
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2.408`}, {820.`, 2.40672`}, {825.`, 2.4003`}, {830.`,
2.40356`}, {835.`, 2.40439`}, {840.`, 2.41256`}, {845.`,
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2.41221`}, {880.`, 2.41353`}, {885.`, 2.40919`}, {890.`,
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2.36319`}, {910.`, 2.232`}, {915.`, 2.83176`}, {920.`,
2.40338`}, {925.`, 2.24156`}, {930.`, 2.26923`}, {935.`,
2.27405`}, {940.`, 2.29832`}, {945.`, 2.32295`}, {950.`,
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2.39176`}, {1225.`, 2.40127`}, {1230.`, 2.41093`}, {1235.`,
2.41167`}, {1240.`, 2.41547`}, {1245.`, 2.41724`}, {1250.`,
2.41734`}, {1255.`, 2.41807`}, {1260.`, 2.40711`}, {1265.`,
2.32865`}, {1270.`, 2.33615`}, {1275.`, 2.83364`}, {1280.`,
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2.30539`}, {1300.`, 2.30546`}, {1305.`, 2.32279`}, {1310.`,
2.34521`}, {1315.`, 2.35093`}, {1320.`, 2.36876`}, {1325.`,
2.38492`}, {1330.`, 2.38044`}, {1335.`, 2.38815`}, {1340.`,
2.38348`}, {1345.`, 2.40065`}, {1350.`, 2.39982`}, {1355.`,
2.39992`}, {1360.`, 2.4054`}, {1365.`, 2.40309`}, {1370.`,
2.40594`}, {1375.`, 2.4014`}, {1380.`, 2.40763`}, {1385.`,
2.40742`}, {1390.`, 2.40345`}, {1395.`, 2.41129`}, {1400.`,
2.41493`}, {1405.`, 2.41004`}, {1410.`, 2.41289`}, {1415.`,
2.41294`}, {1420.`, 2.4068`}, {1425.`, 2.4041`}, {1430.`,
2.39989`}, {1435.`, 2.41382`}, {1440.`, 2.4151`}, {1445.`,
2.28722`}, {1450.`, 2.41435`}, {1455.`, 2.8229`}, {1460.`,
2.34602`}, {1465.`, 2.27485`}, {1470.`, 2.28696`}, {1475.`,
2.30587`}, {1480.`, 2.32392`}, {1485.`, 2.33769`}, {1490.`,
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2.35091`}, {2020.`, 2.35177`}, {2025.`, 2.368`}, {2030.`,
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share|improve this question






















  • You could try LowpassFilter. For example, with y = data[[1 ;; 400, 2]] (to see better) and Manipulate[LowpassFilter[y, t] // ListLinePlot, {t, 0, 3}], you can see that t=0.375 is not a too bad choice (I got rid of the $x$ values so of course you should compensate for that).
    – anderstood
    7 hours ago















up vote
2
down vote

favorite












I have experimental data with periodic sharp artefact spikes, which mask the real signal that could be seen as several negative peaks in the first third part on the curve:



enter image description here



Is it possible somehow to filter these spikes? or at least significantly reduce them? Here is data set:



data = {{5.`, 2.41204`}, {10.`, 2.40477`}, {15.`, 2.40154`}, {20.`, 
2.4094`}, {25.`, 2.41592`}, {30.`, 2.40706`}, {35.`,
2.41802`}, {40.`, 2.4171`}, {45.`, 2.40979`}, {50.`,
2.4127`}, {55.`, 2.40254`}, {60.`, 2.40753`}, {65.`,
2.41049`}, {70.`, 2.40521`}, {75.`, 2.40446`}, {80.`,
2.40692`}, {85.`, 2.40509`}, {90.`, 2.40604`}, {95.`,
2.40464`}, {100.`, 2.40448`}, {105.`, 2.41854`}, {110.`,
2.41627`}, {115.`, 2.40941`}, {120.`, 2.40443`}, {125.`,
2.39909`}, {130.`, 2.41194`}, {135.`, 2.40494`}, {140.`,
2.40801`}, {145.`, 2.41532`}, {150.`, 2.41512`}, {155.`,
2.41328`}, {160.`, 2.41383`}, {165.`, 2.40806`}, {170.`,
2.3958`}, {175.`, 2.41296`}, {180.`, 2.41556`}, {185.`,
2.40119`}, {190.`, 2.27056`}, {195.`, 2.51902`}, {200.`,
2.41532`}, {205.`, 2.36206`}, {210.`, 2.37837`}, {215.`,
2.38771`}, {220.`, 2.39959`}, {225.`, 2.40557`}, {230.`,
2.40373`}, {235.`, 2.4045`}, {240.`, 2.39692`}, {245.`,
2.38983`}, {250.`, 2.39646`}, {255.`, 2.40411`}, {260.`,
2.40749`}, {265.`, 2.41679`}, {270.`, 2.42152`}, {275.`,
2.40963`}, {280.`, 2.39891`}, {285.`, 2.39964`}, {290.`,
2.39939`}, {295.`, 2.40453`}, {300.`, 2.40663`}, {305.`,
2.41569`}, {310.`, 2.40495`}, {315.`, 2.40756`}, {320.`,
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2.41029`}, {340.`, 2.40475`}, {345.`, 2.40698`}, {350.`,
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2.44314`}, {385.`, 2.23574`}, {390.`, 2.26553`}, {395.`,
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share|improve this question






















  • You could try LowpassFilter. For example, with y = data[[1 ;; 400, 2]] (to see better) and Manipulate[LowpassFilter[y, t] // ListLinePlot, {t, 0, 3}], you can see that t=0.375 is not a too bad choice (I got rid of the $x$ values so of course you should compensate for that).
    – anderstood
    7 hours ago













up vote
2
down vote

favorite









up vote
2
down vote

favorite











I have experimental data with periodic sharp artefact spikes, which mask the real signal that could be seen as several negative peaks in the first third part on the curve:



enter image description here



Is it possible somehow to filter these spikes? or at least significantly reduce them? Here is data set:



data = {{5.`, 2.41204`}, {10.`, 2.40477`}, {15.`, 2.40154`}, {20.`, 
2.4094`}, {25.`, 2.41592`}, {30.`, 2.40706`}, {35.`,
2.41802`}, {40.`, 2.4171`}, {45.`, 2.40979`}, {50.`,
2.4127`}, {55.`, 2.40254`}, {60.`, 2.40753`}, {65.`,
2.41049`}, {70.`, 2.40521`}, {75.`, 2.40446`}, {80.`,
2.40692`}, {85.`, 2.40509`}, {90.`, 2.40604`}, {95.`,
2.40464`}, {100.`, 2.40448`}, {105.`, 2.41854`}, {110.`,
2.41627`}, {115.`, 2.40941`}, {120.`, 2.40443`}, {125.`,
2.39909`}, {130.`, 2.41194`}, {135.`, 2.40494`}, {140.`,
2.40801`}, {145.`, 2.41532`}, {150.`, 2.41512`}, {155.`,
2.41328`}, {160.`, 2.41383`}, {165.`, 2.40806`}, {170.`,
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2.40346`}, {6535.`, 2.40558`}};









share|improve this question













I have experimental data with periodic sharp artefact spikes, which mask the real signal that could be seen as several negative peaks in the first third part on the curve:



enter image description here



Is it possible somehow to filter these spikes? or at least significantly reduce them? Here is data set:



data = {{5.`, 2.41204`}, {10.`, 2.40477`}, {15.`, 2.40154`}, {20.`, 
2.4094`}, {25.`, 2.41592`}, {30.`, 2.40706`}, {35.`,
2.41802`}, {40.`, 2.4171`}, {45.`, 2.40979`}, {50.`,
2.4127`}, {55.`, 2.40254`}, {60.`, 2.40753`}, {65.`,
2.41049`}, {70.`, 2.40521`}, {75.`, 2.40446`}, {80.`,
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filtering






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked 8 hours ago









Филипп Цветков

1,384721




1,384721












  • You could try LowpassFilter. For example, with y = data[[1 ;; 400, 2]] (to see better) and Manipulate[LowpassFilter[y, t] // ListLinePlot, {t, 0, 3}], you can see that t=0.375 is not a too bad choice (I got rid of the $x$ values so of course you should compensate for that).
    – anderstood
    7 hours ago


















  • You could try LowpassFilter. For example, with y = data[[1 ;; 400, 2]] (to see better) and Manipulate[LowpassFilter[y, t] // ListLinePlot, {t, 0, 3}], you can see that t=0.375 is not a too bad choice (I got rid of the $x$ values so of course you should compensate for that).
    – anderstood
    7 hours ago
















You could try LowpassFilter. For example, with y = data[[1 ;; 400, 2]] (to see better) and Manipulate[LowpassFilter[y, t] // ListLinePlot, {t, 0, 3}], you can see that t=0.375 is not a too bad choice (I got rid of the $x$ values so of course you should compensate for that).
– anderstood
7 hours ago




You could try LowpassFilter. For example, with y = data[[1 ;; 400, 2]] (to see better) and Manipulate[LowpassFilter[y, t] // ListLinePlot, {t, 0, 3}], you can see that t=0.375 is not a too bad choice (I got rid of the $x$ values so of course you should compensate for that).
– anderstood
7 hours ago










2 Answers
2






active

oldest

votes

















up vote
2
down vote













How about a running median filter?



With[{
time = Median /@ Partition[data[[All, 1]], 5, 1],
ampl = Median /@ Partition[data[[All, 2]], 5, 1]},
ListPlot[Transpose[{time, ampl}], Joined -> True, PlotRange -> All]]


enter image description here






share|improve this answer





















  • +1 Beat me by 30 seconds!
    – JimB
    7 hours ago


















up vote
1
down vote













You might try a "moving median". Unfortunately the decision for an optimal window width is "I'll know it when I see it" kinda thing. In other words, it takes subject matter expertise and restraint.



Manipulate[
ListPlot[Table[{data[[n, 1]], Median[data[[n - ww ;; n + ww, 2]]]},
{n, ww + 1, Length[data] - ww}], Joined -> True, PlotRange -> All],
{{ww, 2, "Window width"}, 2, 200, 2, Appearance -> "Labeled"}]


Moving median and window width 2



Moving median and window width 130






share|improve this answer





















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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes








    up vote
    2
    down vote













    How about a running median filter?



    With[{
    time = Median /@ Partition[data[[All, 1]], 5, 1],
    ampl = Median /@ Partition[data[[All, 2]], 5, 1]},
    ListPlot[Transpose[{time, ampl}], Joined -> True, PlotRange -> All]]


    enter image description here






    share|improve this answer





















    • +1 Beat me by 30 seconds!
      – JimB
      7 hours ago















    up vote
    2
    down vote













    How about a running median filter?



    With[{
    time = Median /@ Partition[data[[All, 1]], 5, 1],
    ampl = Median /@ Partition[data[[All, 2]], 5, 1]},
    ListPlot[Transpose[{time, ampl}], Joined -> True, PlotRange -> All]]


    enter image description here






    share|improve this answer





















    • +1 Beat me by 30 seconds!
      – JimB
      7 hours ago













    up vote
    2
    down vote










    up vote
    2
    down vote









    How about a running median filter?



    With[{
    time = Median /@ Partition[data[[All, 1]], 5, 1],
    ampl = Median /@ Partition[data[[All, 2]], 5, 1]},
    ListPlot[Transpose[{time, ampl}], Joined -> True, PlotRange -> All]]


    enter image description here






    share|improve this answer












    How about a running median filter?



    With[{
    time = Median /@ Partition[data[[All, 1]], 5, 1],
    ampl = Median /@ Partition[data[[All, 2]], 5, 1]},
    ListPlot[Transpose[{time, ampl}], Joined -> True, PlotRange -> All]]


    enter image description here







    share|improve this answer












    share|improve this answer



    share|improve this answer










    answered 7 hours ago









    John Doty

    6,5541924




    6,5541924












    • +1 Beat me by 30 seconds!
      – JimB
      7 hours ago


















    • +1 Beat me by 30 seconds!
      – JimB
      7 hours ago
















    +1 Beat me by 30 seconds!
    – JimB
    7 hours ago




    +1 Beat me by 30 seconds!
    – JimB
    7 hours ago










    up vote
    1
    down vote













    You might try a "moving median". Unfortunately the decision for an optimal window width is "I'll know it when I see it" kinda thing. In other words, it takes subject matter expertise and restraint.



    Manipulate[
    ListPlot[Table[{data[[n, 1]], Median[data[[n - ww ;; n + ww, 2]]]},
    {n, ww + 1, Length[data] - ww}], Joined -> True, PlotRange -> All],
    {{ww, 2, "Window width"}, 2, 200, 2, Appearance -> "Labeled"}]


    Moving median and window width 2



    Moving median and window width 130






    share|improve this answer

























      up vote
      1
      down vote













      You might try a "moving median". Unfortunately the decision for an optimal window width is "I'll know it when I see it" kinda thing. In other words, it takes subject matter expertise and restraint.



      Manipulate[
      ListPlot[Table[{data[[n, 1]], Median[data[[n - ww ;; n + ww, 2]]]},
      {n, ww + 1, Length[data] - ww}], Joined -> True, PlotRange -> All],
      {{ww, 2, "Window width"}, 2, 200, 2, Appearance -> "Labeled"}]


      Moving median and window width 2



      Moving median and window width 130






      share|improve this answer























        up vote
        1
        down vote










        up vote
        1
        down vote









        You might try a "moving median". Unfortunately the decision for an optimal window width is "I'll know it when I see it" kinda thing. In other words, it takes subject matter expertise and restraint.



        Manipulate[
        ListPlot[Table[{data[[n, 1]], Median[data[[n - ww ;; n + ww, 2]]]},
        {n, ww + 1, Length[data] - ww}], Joined -> True, PlotRange -> All],
        {{ww, 2, "Window width"}, 2, 200, 2, Appearance -> "Labeled"}]


        Moving median and window width 2



        Moving median and window width 130






        share|improve this answer












        You might try a "moving median". Unfortunately the decision for an optimal window width is "I'll know it when I see it" kinda thing. In other words, it takes subject matter expertise and restraint.



        Manipulate[
        ListPlot[Table[{data[[n, 1]], Median[data[[n - ww ;; n + ww, 2]]]},
        {n, ww + 1, Length[data] - ww}], Joined -> True, PlotRange -> All],
        {{ww, 2, "Window width"}, 2, 200, 2, Appearance -> "Labeled"}]


        Moving median and window width 2



        Moving median and window width 130







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 7 hours ago









        JimB

        16.6k12662




        16.6k12662






























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