adjust variable names, minor changes

binary_fstat.m → binaryfilter.m

-function [b,h] = binary_fstat(f,f0,P,a0)
-% Generate the Bessel-weighted matched filter (also generate Comb filter)
+function [b,h] = binaryfilter(f,f0,P,a0)
+% BINARYFILTER Generate the Bessel-weighted matched filter (also generate Comb filter)
 % 
 % Inputs:
 % f - Frequency bin list (i.e. hidden stats)
@@ -11,9 +11,10 @@ function [b,h] = binary_fstat(f,f0,P,a0)
 % b - Bessel-weighted matched filter
 % h - Comb filter (not used in the current search)
 
+
 % Fstat power concentrated at [-c c]/P where c = 2*pi*a0*f0 
 % f0 is fixed to setup matched filter for each 1-Hz sub-band 
-c=(2*pi*a0*f0);
+c = 2*pi*a0*f0;
 % Make it slightly wider than c
 N = ceil(c)+100;
 n = -N:N;

readfstats.m

 function [f, X, N, T] = readfstats(dirName, startFreq, steps)
-% Read Fstat files
+% READFSTATS Read Fstat files
 % 
 % Inputs:
 % dirName - Directory of Fstat files
@@ -13,9 +13,8 @@ function [f,X,N,T]=readfstats(dirName, startFreq, steps)
 % N - Number of bins
 % T - Total number of steps
 
-d = dir(dirName);
-X = [];
 
+X = [];
 for n = 1:steps
     fileName = ['Fstat-' num2str(startFreq) '-' num2str(n-1) '.dat']
 

search_O1.m

 function search_O1(freq,idx)
-% This script is for O1 Sco X-1 search. 
+% SEARCH_O1 This script is for O1 Sco X-1 search. 
 % The search is separated into parallel 1-Hz sub-jobs.
 % The start frequency for each sub-job is the overal start frequency plus job index.
 %
@@ -11,6 +11,7 @@ function search_O1(freq,idx)
 % Outputs:
 % Save output files to the output directory.
 
+
 format long g
 
 startFreq = str2num(freq) + str2num(idx)
@@ -21,7 +22,7 @@ outdir = '/home/ling.sun/O1/Viterbi/output/';
 outfile = strcat(outdir,'Viterbi-', num2str(startFreq),'.dat')
 
 % Set parameters
-a = 1.44
+a0median = 1.44
 P = 68023.70496
 steps = 13
 
@@ -32,13 +33,14 @@ toc
 
 fmean = f(1)+0.5; % Take the mean value of each 1-Hz sub-band
 g = [-0.1:0.01:0.1]; % asini grid (+/-10% uncertainty, may search larger uncertainty)
-a0=(1+g)*a; % asini values being searched
+a0 = (1+g)*a0median; % asini values being searched
 
 for m=1:length(g)
     disp(sprintf('a0=%g',a0(m)))
+    
     tic
     % Generate Bessel-weighted matched filter
-    [b,h] = binary_fstat(f,fmean,P,a0(m)); 
+    [b,h] = binaryfilter(f,fmean,P,a0(m)); 
 
 	% Compute Bessel-weighted Fstat
     for n=1:steps
@@ -46,7 +48,6 @@ for m=1:length(g)
     end
 
     [path0(m,:), delta, psi, sc(m)] = viterbi_colFLT(3, Y); 
-    
     toc
 end
 

viterbi_colFLT.m

 function [path,delta,psi,score] = viterbi_colFLT(M, obslik)
-% VITERBI Find the most-probable (Viterbi) path through the HMM states (frequency bins).
+% VITERBI_COLFLT Find the most-probable (Viterbi) path through the HMM states (frequency bins).
 % 
 % Inputs:
 % M      - One dimension of the colfilt matrix, in the current model M=3, 
@@ -15,7 +15,10 @@ function [path,delta,psi,score] = viterbi_colFLT(M, obslik)
 %              of paths to all the states at the final step
           
 
-if round(M/2)==M/2 M=M+1;end
+if round(M/2)==M/2
+    M = M+1;
+end
+
 [Q,T] = size(obslik); % Q bins, T steps
 
 delta = zeros(Q,T);
@@ -28,9 +31,10 @@ path = zeros(1,T);
 % compares all three possible paths for each step and the operation
 % is efficient.
 
+cor = (0:Q-1)'-(M-1)/2; % correction term for each bin
+
 t = 1;
 delta(:,t) = obslik(:,t);
-cor=(0:Q-1)'-(M-1)/2; % correction term for each bin
 
 disp('forward')
 for t=2:T