CrossCorrelate

If the engineers plumb the same IF into two different backed sections and take data, it is possible to load these scans into Linuxpops and perform a Cross-Correlation on the data.

To do this, first load the scans using the parallel display mode:

Linuxpops(H0) >>
Linuxpops(H0) >> pshow 223.01
Linuxpops(H0) >>

will produce a plot something like this:

Figure 1: Plot Results of 'pshow' Command

Now you can do a Cross Correlation on the two data arrays.

Linuxpops(H0) >>
Linuxpops(H0) >> xcorrelate
Linuxpops(H0) >>  # Entering correlateScans(): 124.03
Linuxpops(H0) >>  # PD: Preparing Data
Linuxpops(H0) >>  #  
Linuxpops(H0) >>  # CM: Computing Pass: 1 Mean
Linuxpops(H0) >>  # CM: First-Pass: Mean-X =   0.061670, Mean-Y =   0.019247,  512 Good Channels
Linuxpops(H0) >>  # Removing any Channels 10X Mean: Pass 2
Linuxpops(H0) >>  # Flagging Y-Chan 157: Value:  -0.205285: Factor: 10.67
Linuxpops(H0) >>  # Flagging Y-Chan 244: Value:   0.213948: Factor: 11.12
Linuxpops(H0) >>  # Flagging Y-Chan 375: Value:  -0.217477: Factor: 11.30
Linuxpops(H0) >>  # Flagging Y-Chan 472: Value:   0.211250: Factor: 10.98
Linuxpops(H0) >>  #  
Linuxpops(H0) >>  # CM: Computing Pass: 2 Mean
Linuxpops(H0) >>  # CM: First-Pass: Mean-X =   0.061438, Mean-Y =   0.019394,  508 Good Channels
Linuxpops(H0) >>  # Removing any Channels 10X Mean: Pass 3
Linuxpops(H0) >>  #  
Linuxpops(H0) >>  # CM: Computing Pass: 3 Mean
Linuxpops(H0) >>  # CM: First-Pass: Mean-X =   0.061438, Mean-Y =   0.019394,  508 Good Channels
Linuxpops(H0) >>  # Converged on 3 tries
Linuxpops(H0) >>  #  
Linuxpops(H0) >>  # Now Remove any Channels 5X the rms
Linuxpops(H0) >>  # Skipping Already Flagged Channel:  157,       0.120074      -0.205285
Linuxpops(H0) >>  # Skipping Already Flagged Channel:  244,       0.023823       0.213948
Linuxpops(H0) >>  # Skipping Already Flagged Channel:  375,       0.168449      -0.217477
Linuxpops(H0) >>  # Skipping Already Flagged Channel:  472,       0.051694       0.211250
Linuxpops(H0) >>  #  
Linuxpops(H0) >>  # Sorting 508 channels
Linuxpops(H0) >>  #  
Linuxpops(H0) >>  # Fitting linear regression using grace_fit()
Linuxpops(H0) >>  # Ignoring Results with vaules 1.5 times rms:   0.075662
Linuxpops(H0) >>  # Copying H0 to H3
Linuxpops(H0) >>  # Ignoring channel[  41]: data  -0.129393 <  -0.098282
Linuxpops(H0) >>  # Ignoring channel[ 256]: data   0.181434 >   0.128818
Linuxpops(H0) >>  # Ignoring channel[  38]: data  -0.103072 <  -0.098082
Linuxpops(H0) >>  # Ignoring channel[ 328]: data  -0.146742 <  -0.096542
Linuxpops(H0) >>  # Ignoring channel[  64]: data   0.157778 >   0.130651
Linuxpops(H0) >>  # Ignoring channel[ 260]: data   0.132553 >   0.130979
Linuxpops(H0) >>  # Ignoring channel[ 508]: data  -0.116672 <  -0.095947
Linuxpops(H0) >>  # Ignoring channel[ 426]: data   0.177147 >   0.131430
Linuxpops(H0) >>  # Ignoring channel[ 248]: data   0.136979 >   0.131815
Linuxpops(H0) >>  # Ignoring channel[ 223]: data   0.170450 >   0.131920
Linuxpops(H0) >>  # Ignoring channel[ 440]: data  -0.097402 <  -0.095041
Linuxpops(H0) >>  # Ignoring channel[ 449]: data  -0.105819 <  -0.094987
Linuxpops(H0) >>  # Ignoring channel[ 331]: data   0.141761 >   0.132268
Linuxpops(H0) >>  # Ignoring channel[ 194]: data  -0.126251 <  -0.094405
Linuxpops(H0) >>  # Ignoring channel[  33]: data  -0.101860 <  -0.094335
Linuxpops(H0) >>  # Ignoring channel[ 364]: data   0.160987 >   0.132778
Linuxpops(H0) >>  # Ignoring channel[ 175]: data  -0.097583 <  -0.094152
Linuxpops(H0) >>  # Ignoring channel[ 164]: data   0.154618 >   0.132928
Linuxpops(H0) >>  # Ignoring channel[ 275]: data  -0.112457 <  -0.093935
Linuxpops(H0) >>  # Ignoring channel[  82]: data   0.154902 >   0.133270
Linuxpops(H0) >>  # Ignoring channel[ 374]: data  -0.114313 <  -0.093663
Linuxpops(H0) >>  # Ignoring channel[ 473]: data   0.158425 >   0.133351
Linuxpops(H0) >>  # Ignoring channel[ 135]: data   0.163898 >   0.133399
Linuxpops(H0) >>  # Ignoring channel[ 386]: data   0.158020 >   0.133493
Linuxpops(H0) >>  # Ignoring channel[ 471]: data   0.165662 >   0.133674
Linuxpops(H0) >>  # Ignoring channel[ 227]: data   0.148235 >   0.133710
Linuxpops(H0) >>  # Ignoring channel[ 278]: data  -0.161853 <  -0.093203
Linuxpops(H0) >>  # Ignoring channel[ 196]: data   0.166383 >   0.133868
Linuxpops(H0) >>  # Ignoring channel[ 370]: data  -0.139665 <  -0.093068
Linuxpops(H0) >>  # Ignoring channel[ 404]: data   0.137668 >   0.134037
Linuxpops(H0) >>  # Ignoring channel[ 295]: data  -0.126329 <  -0.092845
Linuxpops(H0) >>  # Ignoring channel[ 217]: data  -0.099472 <  -0.092618
Linuxpops(H0) >>  # Ignoring channel[ 172]: data  -0.143187 <  -0.092214
Linuxpops(H0) >>  # Ignoring channel[ 116]: data   0.184228 >   0.135618
Linuxpops(H0) >>  # Ignoring channel[ 305]: data   0.141168 >   0.136091
Linuxpops(H0) >>  # Ignoring channel[ 190]: data  -0.105240 <  -0.090404
Linuxpops(H0) >>  # Copying H3 to H0
Linuxpops(H0) >>  # Wrote 472 data points
Linuxpops(H0) >>

Figure 2: Cross-Correlate Results of 'xcorrelate' Command

As can be seen, 472 out of 512 channels correlated and survived the rigorous criteria. The red bars are the linear fit of the data and +/- the rms. All data outside these limits was removed.

This is provided for illustrative purposes.