how to make smooth the vertcat of more vectors

Question

matteo bottoni 2020-11-19

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/653248-how-to-make-smooth-the-vertcat-of-more-vectors

评论： matteo bottoni 2020-11-19

columntrue.txt

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.

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Mathieu NOE 2020-11-19

在 MATLAB Online 中打开

hi again

you can use this sliding window averaging code on the section of the signal you need to smooth out

all the best

samples = 1000;
VectorTime = 1 + sin(2*pi*(1:samples)/samples)+ 0.25*rand(1,samples);
buffer = 25;    % nb of samples for averaging 
% zero overlap mean averaging
for ci=1:floor(length(VectorTime)/ buffer)
    start_index = 1+(ci-1)*buffer;
    stop_index = min(start_index+ buffer,length(VectorTime));
    time_index(ci) = round((start_index+stop_index)/2);
    frr(ci) =mean(VectorTime(start_index:stop_index));  % 
end
figure(1),
plot((1:samples),VectorTime,'b',time_index,frr,'or');
% sliding avg method
out = myslidingavg(VectorTime, buffer);
figure(2),
plot((1:samples),VectorTime,'b',(1:samples),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<N || dist2end<N        % 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

Mathieu NOE 2020-11-19

在 MATLAB Online 中打开

here you are. Don't be ashamed, every body has to start some day

y = load('columntrue.txt');
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 2020-11-19

very nice. Thanks you a lot

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Answer 1

Star Strider 2020-11-19

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/653248-how-to-make-smooth-the-vertcat-of-more-vectors#answer_549728

编辑：Star Strider 2020-11-19

在 MATLAB Online 中打开

I am not certain what you want, since the posted file is not the same as the posted plot image.

Try this:

y = readmatrix('column.txt');
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')