frb - tool used to search Fast Radio Bursts in auto-spectral data
Up-to-date version will be released soon
numpy, scipy, matplotlib.pyplot (for plots)
user@host:~$ python search_file.py data.dat -nu_max NU_MAX -dnu DNU -dt DT -dm_min DM_MIN -dm_max DM_MAX [-batchsize BATCHSIZE] [-d_t D_T] [-d_dm D_DM] [-perc PERC] [-savefig_dyn fig.png] [-savefig_dedm fig.png] [-threads THREADS]
Parameters:
-
data.dat
- binary or text file with time sequence of dynamical spectra. If text file => # of columns = # of frequency channels and # of rows = # of time measurements. If binary thennp.shape(np.load('data.dat'))
= (# of freq. channels, # of time measurements,) -
-nu_max
- frequency of highest frequency channel [MHz]. -
-dnu
- frequency width of single frequency channel [MHz]. -
-dt
- time step (resolution) [s]. -
dm_min
- minimal value of DM window to search [cm^3 / pc]. -
dm_max
- maximum value of DM window to search [cm^3 / pc]. -
batchsize
- size of image in t-direction, that will be searched for candidates in batches. Default: 100000 -
d_t
- width of feature [s] in (t, DM)-space along t-axis to treat it as candidate. Default: 0.003 -
d_dm
- width of feature [cm^3/pc] in (t, DM)-space along DM-axis to treat it as candidate. Default: 100. -
perc
- percentile of image values that is used to blank image before searching for objects. Default: 99.5 -
savefig_dyn
- file name for saving picture of dynamical spectra. -
savefig_dedm
- file name for saving picture of de-dispersed frequency averaged dynamical spectra. -
save_result
- file name to save (t, DM)-coordinates of found candidates. [s, cm^3/pc] -
threads
- number of threads used for parallelization of grid de-dispersion. Default is1
(don't use parallelization).
Algorithm searches for extended regions in image of de-dispersed frequency averaged dynamical spectra (that is (t, DM)-plane). Currently there are 3 tunable parameters:
-
perc
. Current experience suggests values99.9 - 99.95
. Low value could bring many false features in (t, DM)-space, but as long as we can compare results using different telescopes it doesn't seem to be an issue. Value that is too high can split characteristic x-shaped dispersed signal features. -
d_t
&d_dm
. Some experience with fake FRB injected in real data have shown that 2 most informative features in classification of FRB candidates on (t, DM)-plane are their widths in t- and DM-directions. Currently it is the only method of classification that has been implemented. Nonetheless one can use any other features and their own algorithms of classification by overriding/extendingTDMImageObjects._classify
method that getsimage
andlabelled array
as first two positional arguments.
Current implementation allows parallelization of grid de-dispersion step using
multiprocessing
module.
Using text files requires additional RAM that can be a problem for large data sets.
Copyright 2015 Ilya Pashchenko.
frb is free software made available under the MIT License. For details see the LICENSE file.