The easiest way to test out the toolbox is by using the GUI GRtool. Once you have successfully installed the toolbox, typing GRtool at the Matlab prompt should produce the window shown in figure . At the left of the figure are axes upon which time and frequency domain data can be displayed. At start-up the only options are to Read or Simulate data.
If you press Simulate you will enter the simulation GUI from which you can simulate inspirals up to second post-Newtonian order approximations as described in  and . (Note: You can always run the simulation GUI alone by entering GRtool('simulate_build') at the Matlab prompt.) All simulations are done via a function mxMake_filters which is a Matlab version of the function make_filters. The operation of the simulation GUI is straightforward. You can plot the time and frequency domain simulations, play them as sound, or export them to either the Matlab workspace or *.au sound files.
If you press Read you will be asked a series of questions. At first you will be asked if you would like to read from local disks or from a URL. If you choose local disk, you will then be asked what file you want to open. Only frame formatted data files with the LIGO gravity wave channel (IFO_DMRO) can be read--this goes for data read from a local disk or a URL. If you choose URL you will be prompted for a URL address. After providing the URL you will be asked where you would like to write the contents of the URL. When the program has read the data you will be presented with more buttons on the panel to the right.
Figure shows the results of a successful read of a frame file.
Pressing Template Space will change your view from time/frequency space to template space. Your window will change to look like figure .
From template space you can observe the effect of the data set on a grid of matched filters. You can always go back to see the data in time/frequency space by pressing t/f Space. Similarly from time/frequency space, you can always go to template space by pressing Template Space.
When you enter template space you will also see the template space control panel shown in figure .
With this window you control the files and parameters used for any filtering you do. You can always change a parameter or file by pressing it's corresponding Update button. The calibration file must be a text file with one column of numbers which are the swept sine calibration information contained in the fri array returned by the function fget_ch or it's Matlab counterpart mxFget_ch. You can generate the calibration files using the getfri function as described in section . The templates file must be a two column text file. The two columns are the pairs that you wish to include in your grid of filters. There is a very detailed discussion of what pairs to use in section . There is a sample templates file in $GRASP/src/examples/examples_binary-search/templates.ascii where $GRASP is your GRASP root directory. The Low frequency cutoff is the lowest frequency at which the detector you are interested in can operate. This value must be entered in Hz.
After you have specified the necessary files and low frequency cutoff you can filter your data by pressing Filter Data. As the filters are being generated and compared against the data you will see lots of text streaming by in the workspace. This text will say something similar to the following:
GRASP (page-173.caltech.edu): Message from function phase_frequency() at line number 536 of file ''pN_chirp.c''. Frequency evolution no longer monotonic. Phase evolution terminated at frequency and step: 466.659027 4347 Terminating chirp. Termination code set to: 1201 Returning to calling routine. $Id: man_GRtoolbox.tex,v 1.3 1999/09/06 17:37:17 ballen Exp $ $Name: $ max snr: 2.56 offset: 24659 variance: 0.95574 max snr: 2.56 offset: 2376 variance: 0.96000 max snr: 2.70 offset: 10504 variance: 0.95999 Done filtering 32768 data points through 20 filtersThe final line will always tell you how many data points and how many filters were used.
After you filter the data you can visualize the response of the filters by pressing Raise Grid in the main window. A typical response is shown in figure . The color of the plotted points is based on the time at which that specific filter had the greatest SNR. The colors range from pure blue to pure black. The darker the color the earlier the time.
Figure shows the response of the filters when a simulated inspiral of has been injected into the data stream.
You can clearly see the filters responding. Notice also that the colors have a well defined pattern in this image. If you press Export in this window you will be able to export the arrays max(SNR) and timestart to the workspace. The timestart array contains the time in seconds (relative to the first time stamp) at which the corresponding filter had a maximum. A more detailed description of the array timestart can be found in the description of the function find_chirp in section .