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HISTORY:
29-Apr-2010: Babar Ali First write-up
PhotGlobalDriftCorrTask
User Manual
version: 1.0
-1- Purpose:
The task is designed to estimate and remove correlated signal drifts in the
PACS bolometer readouts. The drift for this purpose is defined as systematic,
monotonic, and (usually) decreasing change in the value of the signal that
has similar magnitude and direction for all bolometer pixels. It is assumed
to be correlated because all pixels exhibit this change in synch with each
other.
The purpose of the PhotGlobalDriftCorrTask is to identify and remove this
drift in the signal, which is not due to an astrophysical source but is
purely (likely) an instrument artifact.
-2- Methodology
PhotGlobalDriftCorrTask assumes there is a drift present. It will not try
to second guess the user even if there is no drift present in the data.
There are two different options for identifying the drifts. HOWEVER, ONLY
ONE model is supported and documented here. In all cases, the attempt
is to find the "sky" or background level and assume this is
constant. Under this constancy assumption, any change in the sky or
background level is taken to be due to error or signal drift.
Model Description
1 The algorithm will compute the MEDIAN values for each readout (entire
array). The MEDIAN values are then binned in intervals specified
by the user with the parameter binsize.
Within these bins, the MININUM(median) is taken to represent an actual
"sky" value. The MINIMUM option is used to reject signal that may be
astrophysical. WARNING: depending on your source, you may never
see the sky so the MINIMUM trick may not work at all!
2 For data which are dominated by "sky"/telescope emission, further
liberties can be taken about the drift correction. In model=2, we
take the readout of the individual pixels and fit a baseline to
their history. This baseline is then subtracted to remove the
signal drift. This assumes that the signal drift is not affected
by actual astrophysical sources; thus, if you see sources in the
time streams of the pixels, you should not use this method.
3 An alternative approach is to MEDIAN of the entire array itself and
either fit a trend to these median values or simply subtract the
array median from each of the detector pixels. This approach has
the same caveat about not seeing the source in individual readouts
as for model=2.
-3- Summary of usage:
out_frames = photGlobalDriftCorr( in_frames, model, copy=True|False, \
binsize=, datadir=, outprefix=, doPlot=True|False, \
slowMedian=True|False, useMedian=True|False, order=, \
verbose=True|False )
in_frames Input, Mandatory
This is the input PACS Frames object.
model INPUT, Integer, Optional, default value: 1
Determine which model to use for the global drift correction.
see above discussion.
copy INPUT, Integer, Optional, default value: 0
Indicates if the new frames should be copied and returned
(copy=1) or if the input frame is changed (copy=0)
binsize INPUT, Integer, Optional, default value: 1000
The size of the bin to use for MIN/MEDIAN estimate
datadir INPUT, String, Optional, default value: "./"
Specify where to store the plot.
outprefix INPUT, String, Optional, default value: "plot"
Prefix to add to the filename for the plot when saving.
doPlot INPUT, Boolean, Optional, default value: False
Show and save a plot of the baseline signal drift correction.
slowMedian INPUT, Boolean, Optional, default value: False
Compute the median by looping in jython. The alternative
implementation is faster but crashes HIPE (SPR has been raised)
useMedian INPUT, Boolean, Optional, default value: False
Use MEDIAN instead of MIN. Only applicable to data where most of
the signal is sky not source.
subFit INPUT, Boolean, Optional, default value: False
For model 3. Can either subtract the FIT or optionally the data
itself.
order INPUT, Integer, Optional, default value: 3
Order of the polynomial to fit to the baseline in all applicable
models.
verbose INPUT, Integer, Optional, default value: 0
Set verbose mode.
outFrames OUT, Frames, Optional, NO default value
Returned Frames object
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