# Interpolate the noisy data

I have some noisy data

data = Uncompress[FromCharacterCode[
Flatten[ImageData[Import["https://i.sstatic.net/RZcpj.png"],"Byte"]]]]


Plot looks like this

There is noise in certain region, it occured because of the overflow and underflow of the mathemtica.

I used GuassianFilter such as

Table[Show[ListPlot[
Select[
GaussianFilter[
Select[data, 0.00005 > # > 10^-8 &], l],
0.00005 > # >= 10^-8 &], Frame -> True, Axes -> False,
FrameTicks -> None, Joined -> True, PlotStyle -> Red,
ImageSize -> 300], ListPlot[data]]
, {l, 0, 30, 5}]


Above figure shows, filtering the data by increasing the filtering size. As I increase the value of the filter, it change data that I do not want to change the shape of the plot

Any advice for smoothing the data?

Here is another example data2,

data2=Uncompress[FromCharacterCode[
Flatten[ImageData[Import["https://i.sstatic.net/WYcxd.png"],"Byte"]]]]

• How about 2nd order polynomial fit? Commented May 23, 2017 at 15:49

For impulsive noises, you are probably better off with a Median filter than with a Gaussian Filter, since it is better able to remove the effect of outliers.

data = Uncompress[FromCharacterCode[
Flatten[ImageData[Import["https://i.sstatic.net/RZcpj.png"],"Byte"]]]];
smoothed = MedianFilter[data, 5];
Show[ListPlot[data], ListPlot[smoothed, PlotStyle -> Green]]


And here is the same filtering applied to your second data set:

data2 = Uncompress[FromCharacterCode[
Flatten[ImageData[Import["https://i.sstatic.net/WYcxd.png"], "Byte"]]]];
Show[ListPlot[data2], ListPlot[MedianFilter[data2, 5], PlotStyle -> Green]]


• Thank you! it works much better! Commented May 24, 2017 at 19:57

This gets the data:

data1 = Get["https://pastebin.com/raw/Z3GGhH2b"];


There are outliers in the data. This can be seen with this command:

qs = Range[0, 0.95, 0.25]~Join~Range[0.95, 1, 0.01];
TableForm[Transpose[{qs, Quantile[Abs[Log10@data1], qs]}]]


Or with this plot:

ListPlot[data1, PlotRange -> All]


This finds the outlier positions:

Import["https://raw.githubusercontent.com/antononcube/\
MathematicaForPrediction/master/OutlierIdentifiers.m"]
olPos = OutlierPosition[data1, SPLUSQuartileIdentifierParameters]

(* {92, 93, 95, 99, 105, 109, 112, 114} *)


Here is the plot of the data without the outliers:

 ListPlot[Delete[data1, List /@ olPos], PlotRange -> All]


We have identified that the outlier presence causes the problems in question. There are three ways to deal with that situation:

1. ignore the outliers (this answer),

2. replace the outlier values with average from neighbors (george2079 answer),

3. use a more robust filter, MedianFilter, (bill s answer).

Below is the modified GaussianFilter plots code in the question over data with ignored outliers. Note that ignoring the outliers is not a simple removal from the original 1D data array. We remove the outliers of the corresponding time series (2D array) and then do the filtering. Also, we have to use TimeSeries in order to make GaussianFilter work over the 2D array.

Block[{data = Transpose[{Range[Length[data1]], data1}]},
data = Delete[data, List /@ olPos];
Table[Show[{
ListPlot[data, PlotTheme -> "Detailed"],
ListLinePlot[GaussianFilter[TimeSeries[data], l], PlotStyle -> Red]
}, PlotLabel -> Row[{"l=", l}], ImageSize -> 300], {l, 0, 30,
10}]]


• Get["https://pastebin.com/raw/Z3GGhH2b"] will get nothing here
– yode
Commented May 23, 2017 at 16:19
• @yode I just verified it (still) works for me... Commented May 23, 2017 at 16:30

One thing to beware of, if you simply delete your outliers you are effectively shifting all of the following data. This may or may not be important obviously depending on what you want to do with the result, and on how many outliers you need to drop.

Here is an approach where we keep the outlier positions and replace the bad values with local averages:

outliers = Flatten[Position[data , x_ /; x >  1 || x < 0]]
(data[[#]] = Mean[Select[data[[# - 3 ;; # + 3]], 0 < # < 1 &]] )  & /@
outliers;
Show[{
ListPlot[GaussianFilter[data, 3], Joined -> True] ,
ListPlot[data, PlotStyle -> Red]}]


If you want you can go back and replace the outlier values with the filter data.

data[[outliers]] = GaussianFilter[data, 3][[outliers]]


Now look at the data we "made up"

Show[{
ListPlot[GaussianFilter[data, 3], Joined -> True] ,
ListPlot[data, PlotStyle -> Red]},
Epilog -> {PointSize[0.015],
Point[Transpose[{outliers, data[[outliers]]}]]},
PlotRange -> {{60, 140}, {0, 10^-5}}, AxesOrigin -> {60, 0}]


• I changed the code in my answer so the shifting of the data does not happen. I remove the outliers of the corresponding time series (2D array) -- not the original 1D array -- and then I do the filtering. Commented May 24, 2017 at 15:08