1.18. cleanDF

Full Name: herschel.hifi.pipeline.level0.CleanDFTask
Alias: cleanDF
Type: Java Task - Java Task
Import: from herschel.hifi.pipeline.level0 import CleanDFTask

HIFI/Pipeline/Level 0 Pipeline



This task manually flags and/or removes bad or duplicate data frames from the HifiTimeline data product. The input to this task is found in the calibration tree under Downlink->Level0->CleanDF and is attached to all HIFI observation contexts. Ghost (or duplicate) data frames are removed, all other issues with the dataframes are flag with {@link RowMask.BAD_DATA} and the dataframe is retained in the HifiTimelineProduct. Additionally, this task repairs datasets in the HifiTimelineProduct where the stored LO Frequency value for the observation is incorrect. Again, there is a calibration table under Downlink->Level0->CleanDF that specifies the correct value. This task corrects the data for a small number of problematic observations. This task is not intended for regular use.


Example 1: CleanDFTask
# Assume that obs and cal exist
cdf = cleanDF
cdf.obs = obs                      # choose an ObservationContext
cdf.cal = cal                      # calibration file
cdf()                              # set flags

API details


ObservationContext obs [INPUT, MANDATORY, default=null]

to be cleaned if necessary

Product cal [INPUT, MANDATORY, default=null]

the calfile to be used

See also


  • 2012-03-30 - DK: Initial Version
  • 2015-06-21 - KE: Updated Documentation