sidelobes_bb

 first let's cut out the noise source data

0001 function sidelobes_bb(d);
0002 
0003 
0004 
0005 % first let's cut out the noise source data
0006 [d_noise, d_src] = cutNoise(d);
0007 
0008 
0009 %  bins in 3 seconds
0010 numBin = floor(length(d_src.antenna0.receiver.utc)./300);
0011 diff_array = zeros(numBin,1);
0012 err_array = zeros(numBin,1);
0013 
0014 for D = 0:numBin-1
0015 
0016   range1 = D*300 + 1;
0017   range2 = (D+1)*300;
0018   
0019   data_slice = d.antenna0.receiver.data(range1:range2,1);
0020   
0021   % Find the min and max of this particular chunk of data
0022   data_min = min(data_slice);
0023   data_max = max(data_slice);
0024   
0025   if (data_max - data_min > 10)
0026     
0027     bin_min = 0.1*(1:50) + floor(data_min);
0028     bin_max = 0.1*(1:50) + ceil(data_max) - 5;
0029     
0030     bins = cat(1,transpose(bin_min),transpose(bin_max));
0031     
0032   else
0033     
0034     bins = 0.1*(1:100) + floor(data_min);
0035     
0036   end
0037   
0038   
0039   data_hist = histc(data_slice,bins);
0040   data_hist(50) = 0;
0041   
0042   fitted_g = fit(transpose(1:100),data_hist,'gauss2', 'Startpoint', [1 25 10 1 75 10]);
0043   
0044   
0045   data_diff = (ceil(data_max) - (100 - fitted_g.b2)) - (floor(data_min) + fitted_g.b1);
0046   
0047   confidence = confint(fitted_g);
0048   
0049   data_err = sqrt((confidence(2,2) - confidence(1,2))^2 + (confidence(2,5) - confidence(1,5))^2);
0050   
0051   diff_array(D+1) = data_diff;
0052   err_array(D+1) = data_diff;
0053 end
0054   
0055 keyboard;