how to draw a line through points?

Question

pruth 2016-2-21

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/269224-how-to-draw-a-line-through-points

评论： Star Strider 2016-3-1

hello... I want to plot a line through the points I have plotted.please see the attachment ..I have drawn a line manually just to give u an idea. please let me know if is it possible in Matlab?

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。

Answer 1

Star Strider 2016-2-21

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/269224-how-to-draw-a-line-through-points#answer_210651

You will need the Signal Processing Toolbox.

I would first do a fft on your data, to see if your signal can be separated from high-frequency noise. If so, an appropriately-designed lowpass discrete filter would probably work. (My filter design procedure is: How to design a lowpass filter for ocean wave data in Matlab?)

If you have broadband noise that cannot be effectively filtered, with your data, my first approach would be to use the sgolayfilt function to do Savitzky-Golay filtering on it.

All signal processing on actual data requires some experimentation, and the noisier the signal, the more experimentation will be necessary. Be prepared to spend some time processing your data.

8 个评论
显示 6更早的评论隐藏 6更早的评论

Star Strider 2016-2-29

在 MATLAB Online 中打开

You have so many NaN values in your data that I had to remove them and do a linear interpolation to fill them in. I normally avoid that, but doing any signal processing on your data was impossible otherwise.

I was able to do a reasonably decent approximation of the sort of trend curve you want with a low-pass filter. The code is relatively robust, so you can experiment with various values for ‘Wp’ in it without ‘breaking’ it. Please only vary the numerator in ‘Wp’. That is the frequency (in the inverse of whatever units your time vector is in). The denominator ‘Fn’ normalises it to the Nyquist frequency as the Signal Processing Toolbox requires, so please leave that as it is.

I plotted the interpolated data here, but I also plotted the original data, and the approximation looks good with it as well. It’s more difficult to see because of all the NaN values, however.

The code:

D = load('pruth vcdmean.mat');
t = D.vcdmean(:,1);
s = D.vcdmean(:,2);
sm  = mean(s, 'omitnan');
tn = t(~isnan(s));                                      % Remove ‘NaN’ Values
sn = s(~isnan(s));
si = interp1(tn, sn, t, 'linear','extrap');             % Linear Interpolation To Fill ‘NaN’ Values
figure(1)
plot(t, s)
hold on
plot(t, si)
% plot([min(t) max(t)], [1 1]*sm)
hold off
grid
legend('Original Data', 'Interpolated Data')
Ts = mean(diff(t));                                     % Sampling Interval
Fs = 1/Ts;                                              % Sampling Frequency
Fn = Fs/2;                                              % Nyquist Frequency
L = length(t);
fts = fft(si)/L;                                        % Normalised Fourier Transform Of Interpolated Data 
Fv = linspace(0, 1, fix(L/2)+1)*Fn;                     % Frequency Vector
Iv = 1:length(Fv);                                      % Index Vector
figure(2)
semilogy(Fv, abs(fts(Iv))*2)                            % Plot FFT
grid
axis([0  1    1E+8  1E+12])
Wp =  0.007/Fn;                                         % Passband Frequency
Ws =  2*Wp;                                             % Stopband Frequency
Rp =  1;                                                % Passband Ripple (dB)
Rs = 20;                                                % Stopband Ripple (dB)
[n,Wn] = buttord(Wp,Ws,Rp,Rs);                          % Optimal Filter Order Calculation
[b,a] = butter(n,Wn);                                   % Calculate Filter Transfer Function Coefficients
[sos,g] = tf2sos(b,a);                                  % Convert To Second-Order-Section For Stability
figure(3)
freqz(sos, 2048, Fs)                                    % Filter Bode Plot To Assess Filter Characteristics & Stability
sif = filtfilt(sos,g,si);                               % Filter The Signal
figure(4)
plot(t, si)                                             % Plot Original Data
hold on
plot(t, sif, '-r', 'LineWidth',1.5)                     % Plot Filtered Data
hold off
grid

pruth 2016-3-1

omg... hats off to u sir... I didn't know this would be that hard......thank you so much.....to learn this much programming I need to give at least 1 years...anyways answer accepted ..thank you.