Rebinning Data

Rebinning Functions

1. sitar_lbin_cpd( f, psda, psdb, cpd, dff, [noisea, noiseb, &rev] );
   Logarithmically rebin two Power Spectral Densities (and, optionally, their associated Poisson, noise levels) and the associated Cross Power Spectral Density
   Run as: isis> (af_lo,af_hi,apsda,apsdb,acpd,nf,[anoisea,anoiseb]) = sitar_lbin_cpd(f,psda,psdb,cpd,dff,[noisea,noiseb,&rev]);
   Variables in [] are optional, but are order specific.
   Variables omitted take on default values.
   
   Inputs:
   • f : Array of Fourier frequencies
   • psda/b : Arrays of PSD values
   • cpd : Array of Cross Power Spectral Density
   • dff : Delta f/f value for logarithmic bin spacing
   Optional Inputs:
   • noisea/b : Array (*or single value*) of Poisson noise levels
   • rev : If declared (i.e., `isis> variable rev;') and input, returns the reverse indices for the binning
   Outputs:
   • af_lo/af_hi : Lower/Upper bounds of Fourier frequency bins
   • apsda/b : Rebinned Power Spectral Density values
   • acpd : Rebinned Cross Power Spectral Density
   • nf : Number of frequencies going into the bin
   Optional Outputs:
   • anoisea/b : Rebinned noise level (array, even for single value input)
   
   
2. sitar_lbin_psd( f, psd, dff, [noise, &rev] );
   Logarithmically rebin a Power Spectral Density (and, optionally, its associated Poisson noise)
   Run as: isis> (af_lo,af_hi,apsd,nf,[anoise]) = sitar_lbin_psd(f,psd,dff,[noise, &rev]);
   Variables in [] are optional, but are order specific.
   Variables omitted take on default values.
   
   Inputs:
   • f : Array of Fourier frequencies
   • psd : Array of PSD values
   • dff : Delta f/f value for logarithmic bin spacing
   Optional Inputs:
   • noise : Array (*or single value*) of Poisson noise levels
   • rev : If declared (i.e., `isis> variable rev;') and input, returns the reverse indices for the binning
   Outputs:
   • af_lo/af_hi : Lower/Upper Bounds of Fourier frequency bins
   • apsd : Rebinned Power Spectral Density values
   • nf : Number of frequencies going into the bin
   Optional Outputs:
   • anoise : Rebinned noise level (array, even for single value input)
   
   
3. sitar_rebin_rate( t, dt, rate [, err] [; tstart=#, tstop=#, minbin=#, user_bin=array, weight] )
   Rebin a rate lightcurve (i.e., good time intervals, effective areas, etc., assumed applied) to a new time grid.
   Run as: isis> lc = sitar_rebin_rate(t, dt, rate, err; tstart=#, tstop=#, minbin=#,weight);
   Variables in [] are optional (or are optional qualifiers, after the semi-colon).
   Omitted variables take on default values.
   
   Inputs:
   • t : Discrete times at which rates are measured
   • dt : Width of evenly spaced time bins
   • rate : (Presumed GTI & Exposure Corrected) rates
   Optional Input:
   • err : Error on the rates
   Optional Qualifier Inputs:
   • tstart : Beginning of first time bin (Defaults to time of first event -1)
   • tstop : End of last time bin (Defaults to time of last event+1)
   • minbin : Minimum required events per bin, else the bin is set to 0, or ignored. Default=1.
   • user_bin_lo : Lower time bounds for a user defined binning grid
   • user_bin_hi : Upper time bounds for a user defined binning grid Overrides dt, tstart, and tstop inputs
   • delgap : If set, delete empty bins from the lightcurve, otherwise, set them to 0 (default)
   • weight : If set and err input, the mean is weighted by the error. I.e., mean = (\sum rate/err^2)/(\sum 1/err^2), and the output variance becomes (\sum (mean-rate/err^2)^2)/(\sum 1/err^2)^2
   Outputs:
   • lc.rate : Mean rate of resulting rebinning
   • lc.bin_lo : Lower time bounds of rebinned lightcurve
   • lc.bin_hi : Upper time bounds of rebinned lightcurve
   • lc.num : Number of events going into a bin
   • lc.var : Variance of the rate in a time bin. ** Only calculated where lc.num >= 2 **, otherwise set to zero.
   Optional Outputs:
   • lc.err : Rate errors combined in quadrature, i.e., it's sqrt{\sum{err^2}}/N, where N is the number of points in a given bin. Only calculate if err is input [otherwise, just use sqrt(lc.var)].