MATLAB: How to estimate annual and semi-annual signals

Hi everyone, I would like to estimate annual ans semi-annual signal from my data. Attached is my data as well as my coding, I managed to estimate bias and linear trend. However, I was stuck in estimating annual and semi-annual signal. I hope anyone can help me on this matter. Thank you.

A = load('FPT1.txt');
t = A(:,1);
slv = A(:,5);
dt1 = num2str(t,'%.2f');
dtm1 = datetime(dt1,'InputFormat','yyyyMMddHHmmss.SS');
dtn1 = datenum(dtm1);
dtmFill1 = fillmissing(dtm1,'linear');
%%PARAMETER FIT
% hobs = h0 + h1T + h2*sin(2*pi*T) + h3*cos(2*pi*T) + h4*sin(4*pi*T) + h5*cos(4*pi*T)
% Estimate bias/offset
h0 = mean(slv);
% Estimate linear trend
b = polyfit(dtn1,slv,1);
h1 = polyval(b, dtn1);
%% Stuck here
% Estimate Annual Signals  [h2*sin(2*pi*T) + h3*cos(2*pi*T)]
% However, the time for this data is every 9.9156 days
% f1=1/(365*24);       %frequency of 1 yr oscillations
h2 = sin(2*pi*T)/slv;
h3 = cos(2*pi*T)/slv;
% Estimate Semi-Annual Signals  [h4*sin(4*pi*T) + h5*cos(4*pi*T)]
% f2=1/((365*24))/2;   %frequency of 1/2 year oscillations
h4 = slv*sin(4*pi*T);
h5 = slv*cos(4*pi*T);

A = load('FPT1.txt'); t = A(:,1); slv = A(:,5); dt1 = num2str(t,'%.2f'); dtm1 = datetime(dt1,'InputFormat','yyyyMMddHHmmss.SS'); dtn1 = datenum(dtm1); % resample with constant time sampling % remember time is in days dt = 7; % 7 days interval Fs = 1/dt; % NB not in Hz = 1/s but in 1/day new_t = min(dtn1):dt:max(dtn1); new_data = interp1(dtn1,slv,new_t); % figure(1),plot(dtn1,slv,'b+-',new_t,new_data,'r*-'); %%PARAMETER FIT % hobs = h0 + h1T + h2*sin(2*pi*T) + h3*cos(2*pi*T) + h4*sin(4*pi*T) + h5*cos(4*pi*T) % Estimate bias/offset % h0 = mean(new_data); % Estimate linear trend b = polyfit(new_t,new_data,1); % h1 = polyval(b, new_t); %% Not anymore Stuck here % Estimate Annual Signals [h2*sin(2*pi*T) + h3*cos(2*pi*T)] % However, the time for this data is every 9.9156 days f1=1/(365); %frequency of 1 yr oscillations time base in days) h2 = 2/length(new_t)*sum(sin(2*pi*f1*new_t).*new_data); h3 = 2/length(new_t)*sum(cos(2*pi*f1*new_t).*new_data); % Estimate Semi-Annual Signals [h4*sin(4*pi*T) + h5*cos(4*pi*T)] f2=2*f1; %frequency of 1/2 year oscillations h4 = 2/length(new_t)*sum(sin(2*pi*f2*new_t).*new_data); h5 = 2/length(new_t)*sum(cos(2*pi*f2*new_t).*new_data); % reconstructed signal hobs = h2*sin(2*pi*f1*new_t) + h3*cos(2*pi*f1*new_t) + h4*sin(2*pi*f2*new_t) + h5*cos(2*pi*f2*new_t); % use the delta = new_data-hobs, for better linear trend estimation delta = new_data-hobs; % Estimate linear trend b = polyfit(new_t,delta,1); h0 = b(2); h1 = b(1); hobs = h0 + h1*new_t + h2*sin(2*pi*f1*new_t) + h3*cos(2*pi*f1*new_t) + h4*sin(2*pi*f2*new_t) + h5*cos(2*pi*f2*new_t); figure(2),plot(dtn1,slv,'b+-',new_t,hobs,'r-*');

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