# how to make smooth the vertcat of more vectors

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matteo bottoni on 19 Nov 2020
Commented: matteo bottoni on 19 Nov 2020
If I try to concatenate and plot all my vectros of different size (each one rapresents a big and small wave) the result is that in the image. What I want is that in the blue circles there is no a discontinuity. matteo bottoni on 19 Nov 2020
Hi Mathieu, thanks a lot for the help. I have to ask u another stuff: i am shame to say you that I didn't understand completly the use of this code. Where and how do i have to insert my data?
Mathieu NOE on 19 Nov 2020
here you are. Don't be ashamed, every body has to start some day
x = 1:numel(y);
N = 30; % nb of samples for averaging
% sliding avg method
out = myslidingavg(y, N);
figure(2),
plot(x,y,'b',x,out,'r');
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 matteo bottoni on 19 Nov 2020
very nice. Thanks you a lot

Star Strider on 19 Nov 2020
Edited: Star Strider on 19 Nov 2020
I am not certain what you want, since the posted file is not the same as the posted plot image.
Try this:
x = 1:numel(y);
Mxv = find(islocalmax(y, 'MinProminence',10, 'MinSeparation',200)); % Indices Of Maxima
for k = 1:numel(Mxv)-1
[minval(k),idx(k)] = min(y(Mxv(k):Mxv(k+1))); % Select Minimum Between Adjacent Maxima
idx(k) = idx(k) + Mxv(k);
end
figure
plot(x,y)
hold on
plot(x(idx), y(idx), 'vr')
plot(x(Mxv), y(Mxv), '^r')
hold off
grid
txtc = sprintfc('x = %4d\ny = %7.3f\n\\downarrow',[x(idx); y(idx)']');
text(x(idx), y(idx)+1, txtc, 'HorizontalAlignment','center', 'VerticalAlignment','bottom')
legend('Data', 'Desired Minima',' Maxima', 'Location','E')
EDIT — (19 Nov 202 at 13:54)
Added text call and plot image. Code otherwise unchanged. .

Star Strider on 19 Nov 2020
Filter it:
x = 1:numel(y);
Fs = 1;
Fn = Fs/2;
L = numel(y);
FTy = fft(y)/L;
Fv = linspace(0, 1, fix(L/2)+1)*Fn;
Iv = 1:numel(Fv);
figure
plot(Fv, abs(FTy(Iv))*2)
grid
xlim([0 0.1])
yfilt = lowpass(y, 0.005, Fs);
figure
subplot(2,1,1)
plot(x, y)
title('Original Signal')
grid
subplot(2,1,2)
plot(x, yfilt, '-r')
title('Lowpass-Filtered Signal')
grid
producing: .
matteo bottoni on 19 Nov 2020
thank you a lot
Star Strider on 19 Nov 2020
As always, my pleasure!