MATLAB: Scatter plot with narrowed color range

color rangeMATLABplottingscatter

Hi all,

below scatter plot has two variables plotted against each other and a third variable provides the color. For the third variables most values lie between 1 and 10 and only one value is at 30 (hence the colorbar range from 0-30).

Is there a way that I can shift the color range so I get more variations in color between 0 and 10?

Thanks in advance!

Best Answer

Here's a colorbar for you to give you an idea of how you can build your own. The colorscale is not very intuitive.

part1 = jet(200);
part2 = [linspace(part1(end,1),0,200)',zeros(200,2)];
cmap=[part1;part2];
colormap(cmap)
scatter(rand(1,100),rand(1,100),[],rand(1,100))
colorbar

EDIT: slight improvement

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MATLAB: How to make Narrow Bandpass filter

hello

seems I have already seen similar post somewhere... a buddy of yours ?

you did not mentionned the sampling frequency , but 24 kHz seemed correct

here my little contribution :


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%





% options 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% spectrogram dB scale
spectrogram_dB_scale = 60;  % dB range scale (means , the lowest displayed level is XX dB below the max level)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% test data
Fs = 24000;
% t = 0:1/Fs:10-1/Fs;
fid = fopen('G4_12.dat','r')
[signal,samples] = fscanf(fid, '%12f');
fclose(fid);
% decimate
decim = 12;
signal = decimate(signal,decim);
Fs = Fs/decim;
samples = length(signal);
time =(1:samples)/Fs;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
n_max = fix(log2(samples));
NFFT = 2^(n_max-1);    % 2^n with n chosen so that NFFT is below length of signal (even after decimation) by factor > 2 so some averaging is doable
% NFFT = 2048/4;    % 2^n with n chosen so that NFFT is below length of signal (even after decimation) by factor > 2 so some averaging is doable
NOVERLAP = round(0.95*NFFT);
w = hanning(NFFT);     % Hanning window / Use the HANN function to get a Hanning window which has the first and last zero-weighted samples.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



% display : averaged FFT spectrum before filter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[sensor_spectrum, freq] = pwelch(signal,w,NOVERLAP,NFFT,Fs,'power');
sensor_spectrum_dB = 10*log10(sensor_spectrum);% convert to dB scale (ref = 1)

% sensor_spectrum_dB = sensor_spectrum_dB-min(sensor_spectrum_dB); % y axis shift so dB values are positive (min value = 0)
%% findpeaks
% df = Fs/NFFT;
% MAXW = 10*df;
% MPD = 20;
% MPP = 20;
% [peaks,loc,W,P] = findpeaks(sensor_spectrum_dB,'MinPeakDistance',MPD,'MaxPeakWidth',MAXW,'MinPeakProminence',MPP);
% freq_peaks = freq(loc);
freq_peaks = [159.0574, 359.3519, 459.4992, 709.8674];
peaks = interp1(freq,sensor_spectrum_dB,freq_peaks,'linear');
figure(1),plot(freq,sensor_spectrum_dB,'b',freq_peaks,peaks,'*r');grid
title(['Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);
xlabel('Frequency (Hz)');ylabel(' dB')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% display 2 : time / frequency analysis : spectrogram demo
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[sg,fsg,tsg] = specgram(signal,NFFT,Fs,hanning(NFFT),NOVERLAP);  
% FFT normalisation and conversion amplitude from linear to dB (peak)
sg_dBpeak = 20*log10(abs(sg))+20*log10(2/length(fsg));     % NB : X=fft(x.*hanning(N))*4/N; % hanning only
% scalingf the dB range : 
min_disp_dB = round(max(max(sg_dBpeak))) - spectrogram_dB_scale;
sg_dBpeak(sg_dBpeak<min_disp_dB) = min_disp_dB;
% plots spectrogram
figure(2);
imagesc(tsg,fsg,sg_dBpeak);colormap('jet');
axis('xy');colorbar('vert');grid
title(['Spectrogram / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(fsg(2)-fsg(1)) ' Hz ']);
xlabel('Time (s)');ylabel('Frequency (Hz)');
% keep only signal content extracted by bandpass filters 
fc = freq_peaks;      % Hz

bandwith = 8;      % Hz
f_low = fc-0.5*bandwith;
f_high = fc+0.5*bandwith;
N = 4;
signal_filtered = zeros(size(signal));
for ci = 1:length(fc)
    [b,a] = butter(N,2/Fs*[f_low(ci) f_high(ci)]);
    % add filtered signal to total final signal
    tmp = filtfilt(b,a,signal);
    signal_filtered = signal_filtered + tmp;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display : averaged FFT spectrum before / after notch filter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[sensor_spectrum_filtered, freq] = pwelch(signal_filtered,w,NOVERLAP,NFFT,Fs,'power');
sensor_spectrum_filtered_dB = 10*log10(sensor_spectrum_filtered);% convert to dB scale (ref = 1)
figure(4),plot(freq,sensor_spectrum_dB,'b',freq,sensor_spectrum_filtered_dB,'r');grid
title(['Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);
legend('before filter','after filter');
xlabel('Frequency (Hz)');ylabel(' dB')
figure(5),plot(time,signal_filtered,'b');grid
title('Filtered signal');
xlabel('Time (s)');ylabel(' amplitude')

MATLAB: Edges of narrow objects in image

I resized the image 3 times and use bwboundaries to find its boundaries, then for the boundaries of the real image, I found the closest integer to points of the resized boundaries divided by 3.

Best Answer

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MATLAB: How to make Narrow Bandpass filter

MATLAB: Edges of narrow objects in image

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