After importing data from excel and plotting them, I need to subtract the noise and extract only the five peaks, and finally to find the area under the peaks? Can anyone tell me what function to use?

2 次查看（过去 30 天）

Joswin Leslie 2018-7-20

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/411360-after-importing-data-from-excel-and-plotting-them-i-need-to-subtract-the-noise-and-extract-only-the

评论： Ryan Takatsuka 2018-8-10

I have attached a sample plot. The left plot is plotted in MATLAB and I need to convert this to the one similar to the plot in the right side by reducing the noise. I then need to find the area under the five peaks.

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

采纳的回答

Ryan Takatsuka 2018-7-20

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/411360-after-importing-data-from-excel-and-plotting-them-i-need-to-subtract-the-noise-and-extract-only-the#answer_329682

在 MATLAB Online 中打开

You can probably apply a highpass filter to the data to isolate the peaks. This should remove the low frequency/offset of the plot, while allowing the quickly changing peaks to pass through unchanged.

Alternatively, you can locate the peaks of the data with something like:

[pks, locs] = findpeaks(data);

Because the peaks seem to have a consistent width, you can divide the data into small "subsections" and plot each individual subsection.

To find the area under the curve, you can use a trapezoidal approximation using one of the following:

cumtrapz();
trapz();

19 个评论
显示 17更早的评论隐藏 17更早的评论

Ryan Takatsuka 2018-7-20

在 MATLAB Online 中打开

It's not super clean, but this seems to be a successful way at isolating the peaks in the data.

Essentially there are a few steps:

Identify the trend in the data and subtract it out
Find the peaks by detecting when the signal goes above a certain threshold. Assuming 'relatively' similar peak width, the peaks can be extracted. This should be an okay assumption anyway because the data is shifted to a y=0 mean anyway.
numerically integrate the data

Hopefully this helps:

%%Import the data
data = xlsread('P540.xlsx', 'Line1-5', 'B1:C9001');
x = data(:,1);
y = data(:,2);
%%Get the trend
% Find the derivative of the signal
dy_dx = diff(y) ./ diff(x); % derivative
% append a value to the end of the vector so the lengths remain the same
dy_dx = [dy_dx; dy_dx(end)].^2;
% Find the points where the derivative is high (These are points where the signal is changing
% significantly, and should not be used to calculate the trend
ind = find(dy_dx>2e9);
% Get new x and y variables without the high derivative points
x_new = x;
y_new = y;
x_new(ind) = [];
y_new(ind) = [];
% Fit a polynomial to the data (This defines the trend)
p = polyfit(x_new, y_new, 2);
y2 = polyval(p, x);
y2 = y-y2;
%%Find the peak
MORE_PEAKS = true;
peak_end_ind = 1;
k = 1;
while MORE_PEAKS
  peak_start_ind = find(y2(peak_end_ind:end)>500, 1) - 50 + peak_end_ind;
  peak_end_ind = peak_start_ind + 400;
  
  i_peak_start(k) = peak_start_ind;
  i_peak_end(k) = peak_end_ind;
  
  if isempty(find(y2(peak_end_ind:end)>500,1))
    MORE_PEAKS = false;
  end
  
  k = k+1;  
end
%%Create the clean version of the vectors
y_clean = zeros(size(y2));
for i=1:length(i_peak_start)
  y_clean(i_peak_start(i):i_peak_end(i)) = y2(i_peak_start(i):i_peak_end(i)) - y2(i_peak_start(i));
end
%%Calculate integral
y_int = cumtrapz(x, y_clean);
fprintf('The total area under the peaks: %0.5f \n', y_int(end))
%%PLOTS
figure
hold on
plot(x, y)
title('Original Data')
grid on
figure
plot(x, y_clean)
title('Peak-Only Data')
grid on
figure
plot(x, y_int)
title('Integrated-peak Data')
grid on

Ryan Takatsuka 2018-7-23

在 MATLAB Online 中打开

You can import the entire Excel spreadsheet, and then split the data up based on the value in column 1. The following should replace the first 4 lines of the original example code I provided. Essentially, it puts all of the data in a cell array, with each cell equal to one graph. By changing the value in graph_number different parts of the file will be plotted and analyzed. To fully automate this, the entire code can be placed in a for loop that iterates through each cell in the data variable.

%%Import the data
raw_data = xlsread('P540.xlsx', 'Line1-5');
% Find the row number where the value in column 1 changes
for i=1:9
  new_graph_index(i+1) = find(raw_data(:,1)==i,1);
end
% manually add the row number for the begining and end of the data
new_graph_index(1) = 1;
new_graph_index(11) = length(raw_data);
% Split the raw data into a cell array for each graph
for k = 1:length(new_graph_index)-1
  data{k} = raw_data(new_graph_index(k):new_graph_index(k+1)-1,2:3);
end
% Select which graph to analyze
graph_number = 5;
% Set the x and y variables from the cell array
x = data{graph_number}(:,1);
y = data{graph_number}(:,2);

Joswin Leslie 2018-8-10

They will. Is there any way possible to process it with these large numbers?

Ryan Takatsuka 2018-8-10

It'll be difficult to get any useful area information without a clean height vs. distance plot (it's hard to find the area under the curve without a reasonable looking curve).

请先登录，再进行评论。