EOVSA flare pipeline: Difference between revisions
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The frequency of observing calibrator sources during EOVSA solar observations is much less than that one typically would like to observe to properly take into account the instrumental gain variations. Hence self-calibration is often needed to calibrate the data to a level of satisfaction. A self-calibration pipeline suitable for generating calibrated dataset and quicklook images have been developed. Here we explain the various inputs of that pipeline and discuss various factors that is needed to be considered before supplying the input values. We also provide the steps for running the code on the pipeline machine. | The frequency of observing calibrator sources during EOVSA solar observations is much less than that one typically would like to observe to properly take into account the instrumental gain variations. Hence self-calibration is often needed to calibrate the data to a level of satisfaction. A self-calibration pipeline suitable for generating calibrated dataset and quicklook images have been developed. Here we explain the various inputs of that pipeline and discuss various factors that is needed to be considered before supplying the input values. We also provide the steps for running the code on the pipeline machine. | ||
'''Format of the pipeline''' | |||
The pipeline for now consists of an input file and two codes. The input file is named as "inputs.py" .The main reason for having two separate codes is that the code which is used to generate the measurement set (MS) uses SUNCASA which for now runs on CASA versions<=5.4 . However, the newly developed self-calibration code requires CASA >=5.6. Hence for now the user needs to run the codes using the appropriate CASA versions. | |||
Revision as of 15:27, 13 January 2022
The frequency of observing calibrator sources during EOVSA solar observations is much less than that one typically would like to observe to properly take into account the instrumental gain variations. Hence self-calibration is often needed to calibrate the data to a level of satisfaction. A self-calibration pipeline suitable for generating calibrated dataset and quicklook images have been developed. Here we explain the various inputs of that pipeline and discuss various factors that is needed to be considered before supplying the input values. We also provide the steps for running the code on the pipeline machine.
Format of the pipeline
The pipeline for now consists of an input file and two codes. The input file is named as "inputs.py" .The main reason for having two separate codes is that the code which is used to generate the measurement set (MS) uses SUNCASA which for now runs on CASA versions<=5.4 . However, the newly developed self-calibration code requires CASA >=5.6. Hence for now the user needs to run the codes using the appropriate CASA versions.