How to smooth plot without affecting the data. While I used smooth in plots, the data was reduced at some points.

For example P = [0.8449;1.9194;3.6072;4.8034;6.1154;6.8659;7.2927;6.8533;4.8565;2.9631;1.2971]

Now when I use smooth

Ps = smooth(P)

plot(Ps)

the resulting figure is

in the P there is value above 7, but in plot the curve is not even touching 7.

Kindly anyone help.

Thanks and regards

Answer 1

[Mathieu NOE]

hello

to have a smoothed curved that goes closer to your input points, you have to interpolate first to have more data points, then smooth

I prefered my own version of data smoothing vs the movmean function (better handling of start / end points)

here is my result : yellow is the improved smoothing after interpolation, orange is without interpolation

P = [0.8449;1.9194;3.6072;4.8034;6.1154;6.8659;7.2927;6.8533;4.8565;2.9631;1.2971]
 x = 1:length(P);
% Ps = movmean(P,5)
Ps = myslidingavg(P,5);

% increase points density and smoothing with myslidingavg
xx = linspace(1,length(P),200);
yy = interp1(x,P,xx,'pchip');
% yys = movmean(yy,25);
yys = myslidingavg(yy,25);

figure(1),plot(x,P,'*',x,Ps,'-',xx,yys,'-');


function out = myslidingavg(in, N)
%   OUTPUT_ARRAY = MYSLIDINGAVG(INPUT_ARRAY, N)
%
%  The function 'slidingavg' implements a one-dimensional filtering, applying a sliding window to a sequence. Such filtering replaces the center value in
%  the window with the average value of all the points within the window. When the sliding window is exceeding the lower or upper boundaries of the input
%  vector INPUT_ARRAY, the average is computed among the available points. Indicating with nx the length of the the input sequence, we note that for values
%  of N larger or equal to 2*(nx - 1), each value of the output data array are identical and equal to mean(in).
%
%  *  The input argument INPUT_ARRAY is the numerical data array to be processed.
%  *  The input argument N  is the number of neighboring data points to average over for each point of IN.
%
%  *  The output argument OUTPUT_ARRAY is the output data array.

if (isempty(in)) | (N<=0)                                              % If the input array is empty or N is non-positive,
 disp(sprintf('SlidingAvg: (Error) empty input data or N null.'));     % an error is reported to the standard output and the
 return;                                                               % execution of the routine is stopped.
end % if

if (N==1)                                                              % If the number of neighbouring points over which the sliding 
 out = in;                                                             % average will be performed is '1', then no average actually occur and
 return;                                                               % OUTPUT_ARRAY will be the copy of INPUT_ARRAY and the execution of the routine
end % if                                                               % is stopped.

nx   = length(in);             % The length of the input data structure is acquired to later evaluate the 'mean' over the appropriate boundaries.

if (N>=(2*(nx-1)))                % If the number of neighbouring points over which the sliding 
 out = mean(in)*ones(size(in));   % average will be performed is large enough, then the average actually covers all the points
 return;                          % of INPUT_ARRAY, for each index of OUTPUT_ARRAY and some CPU time can be gained by such an approach.
end % if                          % The execution of the routine is stopped.

out = zeros(size(in));         % In all the other situations, the initialization of the output data structure is performed.

if rem(N,2)~=1                 % When N is even, then we proceed in taking the half of it:
 m = N/2;                      % m = N     /  2.
else                           % Otherwise (N >= 3, N odd), N-1 is even ( N-1 >= 2) and we proceed taking the half of it:
 m = (N-1)/2;                  % m = (N-1) /  2.
end % if

for i=1:nx,                 % For each element (i-th) contained in the input numerical array, a check must be performed:
  dist2start = i-1;         %  index distance from current index to start index (1)
  dist2end = nx-i;          %  index distance from current index to end index (nx)
  if dist2start<m || dist2end<m        % if we are close to start / end of data, reduce the mean calculation on centered data vector reduced to available samples
      dd = min(dist2start,dist2end);    % min of the two distance (start or end)
  else
      dd = m;
  end % if
  out(i) = mean(in(i-dd:i+dd));     % mean of centered data , reduced to available samples at both ends of the data vector

end % for i

end

[hussain soomro]

Excellent sir. Thanks a lot