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Step 3 - Plotting the cluster centres

We will further improve the trajectory plot by indicating  the particle distribution along the mean track track. 

The macro to use is 'plot_tr_step3.mv' and is basically the same as the one in Step 2 but contains an additional plot layer. In this plot layer we draw circles around the mean trajectory waypoints using the RMS (root mean square) of the horizontal distances of the particles to this waypoint. The code goes like this:

#Get rms of the horizontal distances (in km) to the mean particle positions (i.e. waypoints)
mRms=values(tbl,"rmsHBefore")

#Draw an rms circle around every second waypoint
iStart=1
if mod(count(mRms),2)= 0 then
    iStart=2
end if   

pltRmsLst=nil
for i=iStart to count(mRms) by 2 do

   if mRms[i] > 0 then
        
        #input visualiser defining the circle
        iv_rms=mvl_geocircle(mLat[i],mLon[i],mRms[i],100)

        #circle line attributes
        graph_rms=mgraph(           
            graph_line_colour: "magenta",
            graph_line_thickness: "2",
            graph_line_style: "dot",
            graph_symbol: "off"
            ) 

        pltRmsLst=pltRmsLst & [iv_rms,graph_rms]

    end if
end for

Please note that we use mvl_geocircle() to construct the circle and plotted the circle around every second waypoint to avoid cluttering. The only other change with respect to Step 2 is that we need to extend the plot command with the new data layer (pltRmsLst):

plot(view,iv_track,graph_track,pltRmsLst,pltDateLst,title)

Having run the macro you will get a plot like this:

Image Added

Step 4 - Plotting the cluster centres

The trajectory output file also contains the coordinates of the cluster centres. In this step we will show a possible way to plot this extra bit of information together with the mean trajectory. Our approach is as follows:

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The macro to use is 'plot_tr_step3step4.mv'. This is a fairly long and advanced macro so we will not examine it here but try to encourage you to open it and study how it works.

Having run the macro you will get a plot like this:

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