Filtered cross-correlation method for computing frequency changes

This package enables the computing of variation in p-mode frequencies $\delta\omega_\ell$ over time using cross-correlation with filters.

Example

The first step is to obtain mode parameters using peak bagging. Peakbagging is first performed on averaged spectra. The configuration is specifed in config.yml. The contents of the config file are given below

Navg: 180
Nshift: 45
Nmcmc: 10000
nmin: 16
nmax: 26
freqmin: 150.
freqmax: 6000.
data_dir: "./data"
output_dir: "/scratch/seismo/kashyap/processed/p11-seismo-xl"

Navg - is the length of sub-series (days)
Nshift - difference in start times between adjacent sub-series (days)
Nmcmc - Number of MCMC iterations needed for computing errors
nmin - Minimum radial order for peakbagging
nmax - Maximum radial order for peakbagging
freqmin - Minimum frequency for peakbagging (muHz)
freqmax - Maximum frequency for peakbagging (muHz)
data_dir - Path of lightcurves
output_dir - Path of output files

After setting up config file, first run peakbagging using

python peakbag_kepler.py --kic 8006161

Note that this step requires the use of the apollinaire package.

Once the peakbagging is complete. You can compute frequency changes using

python compute_delnu.py --kic 8006161