Load all mat files to Workspace and plot

Hi,

I am recording signals from DSP in External mode and I can record a certain number of data (depends on sample time, number of signals, heap size, etc.) before a new mat file is created (increment file after one-shot). So I end up with e.g., Data_0.mat, Data_1.mat, Data_2.mat, etc.

I can load each mat file into Workspace and plot. e.g.:

load('Data_8.mat')
figure
subplot(2,1,1)
plot(ScopeData1.time,ScopeData1.signals.values)
grid on
subplot(2,1,2)
plot(ScopeData2.time,ScopeData2.signals.values).

For the next recoded section I have to load another mat file (e.g., Data_9.mat), run the plot script, etc.

How to load all mat files into Workspace and plot them all in one single graph?

Note that the signal name is the same in each mat file (ScopeData1.signals.values and ScopeData2.signals.values) and time is not continuous, i.e., there is a break in time for MATLAB to save this recoding and start another.

Answer 1

[Mathieu NOE]

hello

this is one example how to load all mat files and sort them in natural order (what matlab does not do by default)

you need : https://fr.mathworks.com/matlabcentral/fileexchange/47434-natural-order-filename-sort

the results is that all data are being concatenated but there is this time gap between the recordings , I don't know how you deal with that

fileDir = pwd;

fileNames = dir(fullfile(fileDir,'Data*.mat')); % get list of data files in directory
fileNames_sorted = natsortfiles({fileNames.name}); % sort file names into natural order 
                    % (download FEX : https://fr.mathworks.com/matlabcentral/fileexchange/47434-natural-order-filename-sort)

M= length (fileNames_sorted);

out_data = [];
time1 = [];
signal1 = [];
time2 = [];
signal2 = [];    
    
for f = 1:M 
    
    S = load(fullfile(fileDir, fileNames_sorted{f}))
    %  contatenation of all individual files data
    time1 = [time1; S.ScopeData1.time];
    signal1 = [signal1; S.ScopeData1.signals.values];
    time2 = [time2; S.ScopeData2.time];
    signal2 = [signal2; S.ScopeData2.signals.values];    
    
end

figure
subplot(2,1,1)
plot(time1,signal1,'*-')
grid on
subplot(2,1,2)
plot(time2,signal2,'*-');

Answer 2

[venkat reddy]

plz help me to plot the file

close all;clear all;clc;

Nt0 = [8;8];
Nt = Nt0(1)*Nt0(2);
Nr0 = [8;8];
Nr = Nt0(1)*Nt0(2);
Ny = 32;
Nx = 32;
N_RF = 4;
L = 3;
d = 0.5;
H = zeros(Nr, Nt);

X = 1/sqrt(Nt)*exp(-1i*2*pi*rand(Nt, Nx));
mu_X = max(max(abs(X*X'-Nx/Nt*eye(Nt))));
W = 1/sqrt(Nr)*exp(-1i*2*pi*rand(Ny, Nr));
mu_W = max(max(abs(W'*W-Ny/Nr*eye(Nr))));
for i = 1:1000000
    X0 = 1/sqrt(Nt)*exp(-1i*2*pi*rand(Nt, Nx));
    mu_X0 = max(max(abs(X0*X0'-Nx/Nt*eye(Nt))));
    if (mu_X0 < mu_X)
        X = X0;
        mu_X = mu_X0;
    end
    W0 = 1/sqrt(Nr)*exp(-1i*2*pi*rand(Ny, Nr));
    mu_W0 = max(max(abs(W0'*W0-Ny/Nr*eye(Nr))));
    if (mu_W0 < mu_W)
        W = W0;
        mu_W = mu_W0;
    end
end

SNR_list = -5:2.5:20;
sample_num = 3;
nmse_result = zeros(numel(SNR_list),sample_num);
alpha_true = zeros(numel(SNR_list),sample_num,L);
phi_t_true = zeros(numel(SNR_list),sample_num,L,2);
phi_r_true = zeros(numel(SNR_list),sample_num,L,2);
L_result = zeros(numel(SNR_list),sample_num);
theta_result = zeros(numel(SNR_list),sample_num,4,10);
z_result = zeros(numel(SNR_list),sample_num,10);

for sample_ii = 1:sample_num
    H = zeros(Nr, Nt);
    alpha = zeros(L,1);
    alpha(1:L) = (normrnd(0, 1, L, 1) + 1i*normrnd(0, 1, L, 1)) / sqrt(2);
    while (find(abs(alpha)<0.01))
        alpha(1:L) = (normrnd(0, 1, L, 1) + 1i*normrnd(0, 1, L, 1)) / sqrt(2);
    end
    alpha = sort(alpha, 'descend');
    phi_t = 2*rand(L,2)-1;%virtual AoD
    phi_r = 2*rand(L,2)-1;%virtual AoA
        
    for l = 1:L
        at = kron(exp(-1i*2*pi*[0:Nt0(1)-1]'*d*phi_t(l,1)), exp(-1i*2*pi*[0:Nt0(2)-1]'*d*phi_t(l,2)));
        ar = kron(exp(-1i*2*pi*[0:Nr0(1)-1]'*d*phi_r(l,1)), exp(-1i*2*pi*[0:Nr0(2)-1]'*d*phi_r(l,2)));
        H = H + alpha(l)*ar*at';
    end
    
    for snr_ii = 1:numel(SNR_list)
        snr = SNR_list(snr_ii);
        noise = sqrt(10^(-snr/10)/2);
        
        H = zeros(Nr, Nt);
        alpha = zeros(L,1);
        alpha(1:L) = (normrnd(0, 1, L, 1) + 1i*normrnd(0, 1, L, 1)) / sqrt(2);
        while (find(abs(alpha)<0.01))
            alpha(1:L) = (normrnd(0, 1, L, 1) + 1i*normrnd(0, 1, L, 1)) / sqrt(2);
        end
        alpha = sort(alpha, 'descend');
        phi_t = 2*rand(L,2)-1;%virtual AoD
        phi_r = 2*rand(L,2)-1;%virtual AoA
        
        for l = 1:L
            at = kron(exp(-1i*2*pi*[0:Nt0(1)-1]'*d*phi_t(l,1)), exp(-1i*2*pi*[0:Nt0(2)-1]'*d*phi_t(l,2)));
            ar = kron(exp(-1i*2*pi*[0:Nr0(1)-1]'*d*phi_r(l,1)), exp(-1i*2*pi*[0:Nr0(2)-1]'*d*phi_r(l,2)));
            H = H + alpha(l)*ar*at';
        end

        Y = W*(H*X + noise*(normrnd(0, 1, Nr, Nx) + 1i*normrnd(0, 1, Nr, Nx)));
        Rth = noise*sqrt(Ny*Nx);
        [theta_es,z_es,err]=IR_SURE_CE_UPA(Y,X,W,Nx,Nt0,Nr0,Ny,Rth);
        H_es = zeros(Nr, Nt);
        at = zeros(Nt,1);
        ar = zeros(Nr,1);
        for l = 1:numel(z_es)
            at = kron(exp(-1i*2*pi*[0:Nt0(1)-1]'*theta_es(1,l)), exp(-1i*2*pi*[0:Nt0(2)-1]'*theta_es(2,l)));
            ar = kron(exp(-1i*2*pi*[0:Nt0(1)-1]'*theta_es(3,l)), exp(-1i*2*pi*[0:Nt0(2)-1]'*theta_es(4,l)));
            H_es = H_es + z_es(l)*ar*at';
        end
        
        nmse_sample = sum(sum(abs(H-H_es).^2))/sum(sum(abs(H).^2));
        disp(['sample_ii=' num2str(sample_ii) ' snr=' num2str(SNR_list(snr_ii)) ' nmse=' num2str(nmse_sample) ' err=' num2str(err)]);
        
       %% spectral efficiency
        [U_perfectCSI,S,V_perfectCSI] = svd(H);
        P_perfectCSI = V_perfectCSI(:,1:N_RF);
        Q_perfectCSI = U_perfectCSI(:,1:N_RF);
        R_perfectCSI = 0.5*noise*noise*(Q_perfectCSI'*Q_perfectCSI);
        SE_perfectCSI = log2(det(eye(N_RF)+(1/N_RF)*inv(R_perfectCSI)*((Q_perfectCSI'*H*P_perfectCSI)*(Q_perfectCSI'*H*P_perfectCSI)')));
        
        [U_estCSI_SURE,S,V_estCSI_SURE] = svd(H_es);
        P_estCSI_SURE = V_estCSI_SURE(:,1:N_RF);
        Q_estCSI_SURE = U_estCSI_SURE(:,1:N_RF);
        R_estCSI_SURE = 0.5*noise*noise*(Q_estCSI_SURE'*Q_estCSI_SURE);
        SE_estCSI_SURE = log2(det(eye(N_RF)+(1/N_RF)*inv(R_estCSI_SURE)*((Q_perfectCSI'*H*P_estCSI_SURE)*(Q_perfectCSI'*H*P_estCSI_SURE)')));
        
        alpha_true(snr_ii, sample_ii,:) = alpha;
        phi_t_true(snr_ii, sample_ii,:,:) = phi_t;
        phi_r_true(snr_ii, sample_ii,:,:) = phi_r;
        nmse_result(snr_ii, sample_ii) = nmse_sample;
        L_sample = numel(z_es);
        L_result(snr_ii, sample_ii) = L_sample;
        theta_result(snr_ii, sample_ii, :, 1:L_sample) = theta_es(:,:);
        z_result(snr_ii, sample_ii, 1:L_sample) = z_es(:);
    end
    save result.mat
    
    load result.mat
    
figure;
    hold on; box on;
    
     plot(snr,result,'rd-','LineWidth',1);
%     plot(nmse_result,SE_estCSI_SURE,'ko:','LineWidth',1);
%     plot(nmse_result,V_estCSI_SURE,'k*--','LineWidth',1);
%     plot(nmse_result,V_perfectCSI,'bs-','LineWidth',1);
%     
    xlabel('SNR (dB)');
    ylabel('NMSE');
    
    legend('ULA','UPA','OMP','AUXILARY BEAM PAIR','Location','NorthWest');
    set(gca,'XScale','log');
    title('NMSE performance comparison of different channel estimation schemes under NLoS channel');
end

[Mathieu NOE]

hello

what is the issue ? I tried to run your code but got this error :

Unrecognized function or variable 'rndcheck'.

Error in normrnd (line 27)

[errorcode rows columns] =

rndcheck(4,2,mu,sigma,m,n);

Error in venkat (line 45)

alpha(1:L) = (normrnd(0, 1, L, 1) + 1i*normrnd(0,

1, L, 1)) / sqrt(2);

[Mathieu NOE]

maybe I fixed the previous error - could find the rndcheck function on internet

but now Ihave another issue as this function is not provided in your code :

Unrecognized function or variable 'IR_SURE_CE_UPA'.

Error in venkat (line 80)

[theta_es,z_es,err]=IR_SURE_CE_UPA(Y,X,W,Nx,Nt0,Nr0,Ny,Rth);