CompressiveSensingDictionaryLearning/demo.m at master · AswinVasudevan21/CompressiveSensingDictionaryLearning · GitHub

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clear all
clc
close all

addpath('../code/Utils/rwt-master/src');
addpath('../code/Data');
addpath('../code/Recon');
addpath('../Human/MG85');

load('Data2_lat12.mat');

singledata = data(1,:);

hold off
figure
hold on
plot(singledata(1:10000));
hold off


forder = 50;
lowband = 800;
highband = 8000;
sampl = 20000;
b = fir1(forder,[lowband/sampl*2,highband/sampl*2]);
singledataHPF = filter(b,1,singledata);

figure
hold on
plot(singledataHPF(1:10000));
hold off

allLocs = getSpikeLocations(singledataHPF,1,20000,'threshGain',1);
hold off

cleaneddataHPF = zeros(size(singledataHPF));

for i = 1:size(allLocs,2)
    cleaneddataHPF(1,allLocs(1,i)-5:allLocs(1,i)+10) = singledataHPF(1,allLocs(1,i)-5:allLocs(1,i)+10);
end
figure
plot (cleaneddataHPF(1:10000));

patchsize = 1000;
mfact = 0.6;
M = floor(patchsize*mfact);
fullN = size(cleaneddataHPF,2);
noofpatches = ceil(fullN/patchsize);
singledataHPF_recon1 = zeros(size(cleaneddataHPF));

D = dctmtx(patchsize);
iD = D^-1;
perm = randperm(patchsize);
Phi = iD(perm(1:M),:);

for i = 1:noofpatches
    starti = ((i-1)*patchsize)+1;
    endi = min(((i-1)*patchsize)+patchsize,fullN);
    if ((endi-starti+1)<patchsize)
        M = floor((endi-starti+1)*mfact);
        D = dctmtx(endi-starti+1);
        iD = D^-1;
        perm = randperm(endi-starti+1);
        Phi = iD(perm(1:M),:);
    end
    shortdata = cleaneddataHPF (starti:endi);
    N = size(shortdata,2);
    K = nnz(shortdata);

    %Measurement
    y = Phi*dct(shortdata(1,:).'); %dct

    alpha = FISTA(Phi,y); % FISTA

    x_recon = reshape(idct(alpha),N,1);
    singledataHPF_recon(starti:endi) = x_recon.';
    %patch_recon = reshape(midwt(alpha,h,L),n,n); % idwt
end

figure
hold on
plot(cleaneddataHPF(8400:8700));
plot(singledataHPF_recon(8400:8700));
hold off

spikesHPF = getSpikeLocations(cleaneddataHPF,1,20000,'threshGain',1);
spikesHPF_recon = getSpikeLocations(singledataHPF_recon,1,20000,'threshGain',1);

figure
hold on
for i = 1:size(spikesHPF,2)
    plot(singledataHPF(1,spikesHPF(1,i)-5:spikesHPF(1,i)+10));
end
hold off

figure
hold on
for i = 1:size(spikesHPF_recon,2)
    plot(singledataHPF(1,spikesHPF_recon(1,i)-5:spikesHPF_recon(1,i)+10));
end
hold off