huibintemaspampipeline
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huibintemaspampipeline [2017/06/27 08:52] – [Basic pipeline run] huibintema | huibintemaspampipeline [2019/02/18 11:37] – [Old hardware-correlator observations] huibintema | ||
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Then we derive calibration and flagging information from the primary calibrator(s), | Then we derive calibration and flagging information from the primary calibrator(s), | ||
<code python> | <code python> | ||
+ | uvfits_file_name = " | ||
pre_calibrate_targets( uvfits_file_name, | pre_calibrate_targets( uvfits_file_name, | ||
</ | </ | ||
Line 40: | Line 41: | ||
./ | ./ | ||
- | There' | + | The pipeline run can be summarized by typing: |
+ | <code python> | ||
+ | summarize_spam_log( " | ||
+ | </ | ||
+ | |||
+ | |||
+ | There' | ||
----- | ----- | ||
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</ | </ | ||
The output UVFITS can then be passed to // | The output UVFITS can then be passed to // | ||
+ | |||
+ | ----- | ||
+ | |||
+ | ==== Combining observations of the same target ==== | ||
+ | |||
+ | Combining observations on the same target from multiple observations is only possible when the frequency setup was exactly the same during the multiple observations. This is (almost?) always the case when a target was observed during a single project spread over multiple nights. | ||
+ | |||
+ | First, convert and pre-calibrate the observations per night. Then make sure that the multiple pre-calibrated UVFITS files of the target are all located in the same (fits) directory, and have a common naming scheme. E.g., < | ||
+ | <code python> | ||
+ | uvfits_file_name = " | ||
+ | process_target( ' | ||
+ | </ | ||
+ | This will read in all UVFITS files that match the wildcard query, concatenate them, and process them as one. | ||
----- | ----- | ||
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gv ./ | gv ./ | ||
</ | </ | ||
- | Select a channel range over which the bandpass phases are well-behaved (linear) for most antennas, | + | Select a channel range over which the bandpass phases are well-behaved (approximately |
+ | {{ : | ||
+ | |||
+ | Note down the lower- and upper-limit of the channel range. Then re-run | ||
<code python> | <code python> | ||
- | channel_range = [ 10, 225 ] # example channel range to keep | + | channel_range = [ 150, 425 ] # example channel range to keep |
pre_calibrate_targets( uvfits_file_name, | pre_calibrate_targets( uvfits_file_name, | ||
+ | </ | ||
+ | |||
+ | ----- | ||
+ | |||
+ | ==== Old hardware-correlator observations ==== | ||
+ | |||
+ | If you downloaded data from cycle 17 or earlier, it is likely correlated using the GMRT Hardware Backend (GHB; a.k.a. the hardware correlator). For frequencies of 325 MHz and higher, the 32 MHz bandwidth is typically split over an upper-side band (USB) and lower-side band (LSB), both captured in separate LTA files (typical extensions are .lta and .ltb). In SPAM, both sideband LTA files need to be pre-processed separately: | ||
+ | <code python> | ||
+ | convert_lta_to_uvfits( lta_file_name ) | ||
+ | convert_lta_to_uvfits( ltb_file_name ) | ||
+ | </ | ||
+ | The next step is run using the // | ||
+ | <code python> | ||
+ | pre_calibrate_targets( uvfits_file_name_lta, | ||
+ | pre_calibrate_targets( uvfits_file_name_ltb, | ||
+ | </ | ||
+ | The resulting UVFITS files for USB and LSB per target can be combined: | ||
+ | <code python> | ||
+ | uvfits_file_name_usb = " | ||
+ | uvfits_file_name_lsb = " | ||
+ | uvfits_file_name = " | ||
+ | combine_usb_lsb( uvfits_file_name_usb, | ||
+ | </ | ||
+ | The output UVFITS file can be processed further in the main pipeline. | ||
+ | |||
+ | Regarding the main pipeline, there are two options that may be relevant to get to better results. The first option related to the situation explained above, where two sidebands (USB and LSB) are joined together to cover 32 MHz of bandwidth. In that case, it may help to turn on an image-based flagging option that treats the joined USB and LSB separately. Reason for this is that the USB and LSB have separate signal chains, and thus there can be system problems that relate only to one of the two sidebands. | ||
+ | <code python> | ||
+ | process_target( target_uvfits_file_name, | ||
+ | </ | ||
+ | |||
+ | The second option is to turn on baseline-based calibration, | ||
+ | <code python> | ||
+ | process_target( target_uvfits_file_name, | ||
</ | </ | ||
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----- | ----- | ||
+ | |||
+ | Feedback: [[huib.intema@curtin.edu.au|Click here]] | ||
huibintemaspampipeline.txt · Last modified: 2020/10/05 17:46 by huibintema