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update_spatial_components.m
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update_spatial_components.m
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function [A,b,C] = update_spatial_components(Y,C,f,A_,P,options)
% update spatial footprints and background through Basis Pursuit Denoising
% for each pixel i solve the problem
% [A(i,:),b(i)] = argmin sum(A(i,:))
% subject to || Y(i,:) - A(i,:)*C + b(i)*f || <= sn(i)*sqrt(T);
% for each pixel the search is limited to a few spatial components
% INPUTS:
% Y: raw data
% C: temporal components
% f: temporal background
% A_: current estimate of spatial footprints (used for determining search locations only)
% P: dataset parameters (used for noise values and interpolated entries)
% options parameter struct (for noise values and other parameters)
% OUTPUTS:
% A: new estimate of spatial footprints
% b: new estimate of spatial background
% C: temporal components (updated only when spatial components are completely removed)
% Written by:
% Eftychios A. Pnevmatikakis, Simons Foundation, 2015
warning('off', 'MATLAB:maxNumCompThreads:Deprecated');
memmaped = isobject(Y);
if memmaped
sizY = size(Y,'Y');
d = prod(sizY(1:end-1));
T = sizY(end);
else
[d,T] = size(Y);
end
if nargin < 6 || isempty(options); options = []; end
if ~isfield(options,'d1') || isempty(options.d1); d1 = input('What is the total number of rows? \n'); options.d1 = d1; else d1 = options.d1; end % # of rows
if ~isfield(options,'d2') || isempty(options.d2); d2 = input('What is the total number of columns? \n'); options.d2 = d2; else d2 = options.d2; end % # of columns
if ~isfield(options,'show_sum'); show_sum = 0; else show_sum = options.show_sum; end % do some plotting while calculating footprints
if ~isfield(options,'interp'); Y_interp = sparse(d,T); else Y_interp = options.interp; end % identify missing data
if ~isfield(options,'use_parallel'); use_parallel = ~isempty(which('parpool')); else use_parallel = options.use_parallel; end % use parallel toolbox if present
if ~isfield(options,'search_method'); method = []; else method = options.search_method; end % search method for determining footprint of spatial components
if nargin < 2 || (isempty(A_) && isempty(C)) % at least either spatial or temporal components should be provided
error('Not enough input arguments')
else
if ~isempty(C); K = size(C,1); elseif islogical(A_); K = size(A_,2); else K = size(A_2,2) - options.nb; end
end
if nargin < 5 || isempty(P); P = preprocess_data(Y,1); end % etsimate noise values if not present
if nargin < 4 || isempty(A_);
IND = ones(d,size(C,1));
else
if islogical(A_) % check if search locations have been provided, otherwise estimate them
IND = A_;
if isempty(C)
INDav = double(IND)/diag(sum(double(IND)));
px = (sum(IND,2)>0);
f = mean(Y(~px,:));
b = max(Y*f',0)/norm(f)^2;
C = max(INDav'*Y - (INDav'*b)*f,0);
end
else
IND = determine_search_location(A_(:,1:K),method,options);
end
end
options.sn = P.sn;
if ~memmaped
Y(P.mis_entries) = NaN; % remove interpolated values
end
Cf = [C;f];
if use_parallel % solve BPDN problem for each pixel
Nthr = max(2*maxNumCompThreads,round(d*T/2^24));
siz_row = [floor(d/Nthr)*ones(Nthr-1,1);d-floor(d/Nthr)*(Nthr-1)];
indeces = [0;cumsum(siz_row)];
if ~memmaped
Ycell = mat2cell(Y,siz_row,T);
else
Ycell = cell(Nthr,1);
end
INDc = mat2cell(IND,siz_row,K);
Acell = cell(Nthr,1);
Psnc = mat2cell(options.sn(:),siz_row,1);
Yf = cell(Nthr,1);
parfor nthr = 1:Nthr
Acell{nthr} = zeros(siz_row(nthr),size(Cf,1));
if memmaped
Ytemp = double(Y.Yr(indeces(nthr)+1:indeces(nthr+1),:));
else
Ytemp = Ycell{nthr};
end
Yf{nthr} = Ytemp*f';
for px = 1:siz_row(nthr)
fn = ~isnan(Ytemp(px,:)); % identify missing data
ind = find(INDc{nthr}(px,:));
if ~isempty(ind);
ind2 = [ind,K+(1:size(f,1))];
%[~, ~, a, ~] = lars_regression_noise(Ycell{nthr}(px,fn)', Cf(ind2,fn)', 1, Psnc{nthr}(px)^2*T);
[~, ~, a, ~] = lars_regression_noise(Ytemp(px,fn)', Cf(ind2,fn)', 1, Psnc{nthr}(px)^2*T);
Acell{nthr}(px,ind2) = a';
end
end
end
A = cell2mat(Acell);
Yf = cell2mat(Yf);
else
A = [zeros(d,K),zeros(d,size(f,1))];
sA = zeros(d1,d2);
Yf = Y*f';
for px = 1:d % estimate spatial components
fn = ~isnan(Y(px,:)); % identify missing data
ind = find(IND(px,:));
if ~isempty(ind);
ind2 = [ind,K+(1:size(f,1))];
[~, ~, a, ~] = lars_regression_noise(Y(px,fn)', Cf(ind2,fn)', 1, options.sn(px)^2*T);
A(px,ind2) = a';
sA(px) = sum(a);
end
if show_sum
if mod(px,d1) == 0;
figure(20); imagesc(sA); axis square;
title(sprintf('Sum of spatial components (%i out of %i columns done)',round(px/d1),d2)); drawnow;
end
end
end
end
A(isnan(A))=0;
A = sparse(A);
A = threshold_components(A,options); % post-processing of components
fprintf('Updated spatial components \n');
ff = find(sum(A(:,1:K))==0); % remove empty components
if ~isempty(ff)
K = K - length(ff);
A(:,ff) = [];
C(ff,:) = [];
end
b = max((Yf - A(:,1:K)*(C(1:K,:)*f'))/(f*f'),0);
A = A(:,1:K);