huibintemaspampipeline
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huibintemaspampipeline [2017/06/27 12:29] – [Basic pipeline run] huibintema | huibintemaspampipeline [2020/10/05 17:46] (current) – [EXPERIMENTAL: Processing uGMRT wideband data] 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 46: | Line 47: | ||
- | There' | + | There' |
----- | ----- | ||
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</ | </ | ||
The output UVFITS file can be processed further in the main pipeline. | 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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----- | ----- | ||
+ | |||
+ | ==== EXPERIMENTAL: | ||
+ | |||
+ | SPAM has some options to process uGMRT wideband data. SPAM does not support the processing of large fractional bandwidths (df/ | ||
+ | |||
+ | The first step is to convert LTA to UVFITS format: | ||
+ | <code python> | ||
+ | lta_file_name = " | ||
+ | convert_lta_to_uvfits( lta_file_name ) | ||
+ | </ | ||
+ | Next, we split the UVFITS file into smaller frequency chunks (subbands): | ||
+ | <code python> | ||
+ | uvfits_file_name = " | ||
+ | split_wideband_uvdata( uvfits_file_name ) | ||
+ | </ | ||
+ | The width of the frequency chunks is automatically set to a sensible value. The resulting 4 or 6 UVFITS files are also located in the fits subdirectory and named " | ||
+ | |||
+ | From here, each frequency chunk is processed independently in a similar fashion as a narrow-band GMRT observations, | ||
+ | < | ||
+ | uvfits_file_name = " | ||
+ | reference_frequency = 450.e6 | ||
+ | pre_calibrate_wideband_targets( uvfits_file_name, | ||
+ | </ | ||
+ | Setting a fixed reference frequency ensures that the frequency averaging of all frequency chunks is the same, which is important when jointly imaging the SPAM output visibilities later. Sensible values seem to be: | ||
+ | < | ||
+ | reference_frequency = 450.e6 | ||
+ | reference_frequency = 650.e6 | ||
+ | </ | ||
+ | The pre-calibrated visibilities per target are located in the fits subdirectory and are named per subband. | ||
+ | |||
+ | Next comes the SPAM main pipeline run. This is best done in separate project directories per subband. If possible, use a good, single reference sky model for all runs. For example, this reference model can be obtained from first running SPAM on the narrow-band GMRT (GSB) data that was recorded alongside the uGMRT wideband data, and extracting a sky model from the final SP2B image using PyBDSF. | ||
+ | < | ||
+ | target_uvfits_file_name = " | ||
+ | UVFITS" | ||
+ | catalog_name = "< | ||
+ | catalog = read_pybdsm_ascii_catalog( catalog_name ) | ||
+ | source_list = create_source_list_from_catalog( catalog ) | ||
+ | resolution = 10. # representative resolution of model image in arcsec | ||
+ | process_wideband_target( target_uvfits_file_name, | ||
+ | </ | ||
+ | |||
+ | If all went well, each SPAM pipeline run on a subband yielded a final image and a calibrated visibility data set (.SP2B.CAL.RR.UVFITS). For use in WSClean, the calibrated visibilities all need to be collected in one directory and converted into measurement sets using CASA. Then WSClean can be used to do a final wideband imaging run. Here is an example: | ||
+ | < | ||
+ | wsclean -weight briggs 0 -pol RR -size 5000 5000 -scale 1.5asec -niter 15000 -auto-threshold 0.5 -auto-mask 3 -gain 0.25 -mgain 0.8 -weighting-rank-filter 3 -join-channels -channels-out 6 -j 8 -mem 80 -name SOURCE_UGMRT3 SOURCE_UGMRT3-01.MS SOURCE_UGMRT3-02.MS SOURCE_UGMRT3-03.MS SOURCE_UGMRT3-04.MS SOURCE_UGMRT3-05.MS SOURCE_UGMRT3-06.MS | ||
+ | </ | ||
+ | Here, SOURCE_UGMRT3-0x.MS are the input measurement sets as produced by CASA. Make sure that " | ||
+ | |||
+ | |||
+ | |||
+ | ----- | ||
+ | |||
+ | Feedback: [[intema@strw.leidenuniv.nl|Click here]] | ||
huibintemaspampipeline.txt · Last modified: 2020/10/05 17:46 by huibintema