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## Reducing spectral line data

The raw data (in MBFITS format) are converted to CLASS format automatically. This is done using the method described in Winkel, Kraus, & Bach, Unbiased flux calibration methods for spectral-line radio observations, A&A 540, A140, 2012 (Section 3.4 and Section 4.5) - assuming a constant system temperature (Tsys) and flat calibration signal (Tcal). The latter is produced by a noise diode the power of which is in most cases directly fed into the waveguide within the receiver. Neglecting the frequency-dependence of Tsys and Tcal introduces a bias, especially for wide-band observations! (For more information see Winkel, Kraus, & Bach 2012 where we also show ways to do it better.)

The data files can be found on the observer4 or observer6 in the directory /daten/Class/[fw_]class_YYYY_MM_DD.100m. There might be a short delay until the data is processed and is visible in the class file after the observation has finished

Spectra in Class are always in units of Tcal! To obtain Kelvins or Jy/Beam you have to multiply with the temperature of the noise diode (Tcal) and appropriate antenna sensitivities/efficiencies. Approximate values for this can be found on our receiver website receiver website. However, it is highly recommended to do calibration measurements during your observation for better accuracy. Furthermore, the automatic Class pipeline does not account for atmospheric opacity, elevation-gain curve, and antenna efficiency. Information about these steps can be found in Kraus 2009 (calibration memo).

Of course it is also possible to use the raw data directly, should one wish to do so.

## Using class

To reduce your (spectroscopic and pointing) data with class one should use the observer4 or observer6 computer. Both are in the MPIfR LDAP environment, i.e., everyone with an account in the MPIfR network can login to this computer with his/her account. It is connected to the /homes server. It is also connected to /soft/astro/gag, so your gildas package of choice should work. It is also possible to use the Effelsberg version of class that is used to write the spectroscopic and pointing data. To enable this (if you are using a C-Shell like tcsh), change to the bash shell by typing

bash

and source the init file:

source /opt/bin/init_rtsoft.sh

afterward you can run

class

The Data from the Class pipeline is stored in /daten/Class. To open the file with Class use

las90> file in "/daten/Class/class_2010_10_20.100m"
las90> find

For pointings you have to switch to continuum mode first:

las90> set type c
las90> find

One can switch back to line observations with:

las90> set type l
las90> find

To look for new data in the file:

las90> new

(Note: This doesn't always work. In such cases simply reopen the file, using file open.)

Printing is possible using:

las90> hardcopy /dev eps color /plot HP-RECH

In some cases, a file permission error may occur, in that case create a temporary eps file, please

las90> hardcopy "/tmp/tmp.eps" /dev eps color /plot HP-RECH /overwrite

A list of available printer names is obtained using

sh> lpstat -p -d

For more information on how to use Class have a look into the Gildas documentation.

## Frequency switching

Note that in frequency switching mode the Class pipeline already performs the folding (since the fold command of Class produces incorrect results for some cases). Details can be found in the News section and Winkel, Kraus & Bach 2012.

## Reprocessing spectra using the Class pipeline

Sometimes it may be necessary to re-reduce the data with the Class pipeline. This can be done on the observer4 computer. The necessary software is installed in /opt/specpipeline. There is a bash script to set all the necessary paths. "tcsh" shell users should change to "bash" by typing bash. Launch the following command:

source /opt/bin/init_rtsoft.sh

Now you should be ready to run the offline Class calibration pipeline. There are two ways to go, an interactive tool and a command-line tool (for simple tasks).

### Interactive mode - OfflinePipeline

Type

OfflinePipeline

Now you are in an ipython interface to reduce the data. There are the following commands to reduce the data:

setFitsDir('/daten/Raw')

which sets the source directory for the rawdata. The program will look in this directory for the raw mbfits files.

setClassName('Filename','Directory')

This sets the output gildas class file. If only:

setClassName('Filename')

is given, the named file is created in the current dictionary. Without this command a file name e.g. like class_2010_10_20.100m is created in the current directory. To write the files one has to know the scan number and must give the subscan number. Subscan numbers are 1 based. If the subscan is not in the file an error is plotted.

reduceSubscan(scannumber, subscannumber)

If you reduce the same scan twice, it is written twice to the class file with the same scan number (which you usually don't want).

#### Batch processing

For projects involving lots of scan numbers, one may want to speed up things by using a small piece of python code such that one doesn't have to type in all scan numbers manually:

setClassName('myfile.100m')
setFitsDir('/daten/Raw')

# choose the desired scan numbers, using the python range function (the second number is exclusive!)
scannums = range(6901, 6930)

# alternatively, you can also use a shell-glob pattern and do some rexexp magic to obtain associated scan numbers
import glob, re
filenames = sorted(glob.glob('/daten/Raw/*_27-13_*.FITS'))
scannums = [re.search(r'20\d{6,6}_(\d{4,4})_.*', f).group(1) for f in filenames]

for scannr in scannums:
info = getScanInfo(scannr)
print scannr
subscanList = info.getSubscanNumList()
for subscan in subscanList:
try:
reduceSubscan(scannr, subscan)
except:
pass

(In python it is important to keep indentation!)

#### Options

There are a couple of options that can be passed to the reduceSubscan method.

KeywordDefaultDescription
febe"*"Process only data with a certain frontend-backend name (e.g., 'S60mm-XFFTS'), "*" means all
baseband"*"Process only data with specified baseband number (e.g., 1), "*" means all
fswFoldTrueThe pipeline does the frequency switch folding operation (you really want this and not let Class do it). See About Bandpass Ghosts.
regridMethod"fft"Which regrid algorithm to use for frequency shifting. The default ('fft') algorithm is fast and is the only which doesn't lower spectral resolution. However, ringing effects might occur. This is because the FFTS spectra are not fully-sampled in the strict sense of Shannon(-Nyquist)s sampling theorem. Ringing usually occurs when strong narrowband features are present. Other methods include 'gaussian' (for a convolution based approach) and scipy's regrid algorithms: 'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'.
averMethod"mean"Allows to specifiy the averaging-method ('mean' or 'median', the latter might help with RFI)
skipDumps(2, 0)Allows to skip dumps at beginning/end of a subscan. At the beginning, the telescope sometimes is not yet fully on-source.
reverseArrayOrientationFalseThe latter function additionally flips the data arrays for the following reduce operations, in case the IF flip was wrongly applied (usually happens only if testing new stuff).
shiftFFTSRefChannelFalseBefore February 2011 the reference channel of the FFTS was wrong (in Fits header) and has to be shifted by +0.5 channels if the spectrum was not flipped, otherwise -0.5 channels. See also the News section.
autoLsrCorrTruePer default the class pipeline attempts to correct the data for LSR Doppler shifts (due observer motion w.r.t. the LSR) if not already accounted for by hardware (LO tuning). If you really wanted a fixed-frequency setup (e.g., for system testing) you can disable automatic correction with this keyword.
doAatmFalseApply AATM atmospheric model opacity correction (based on weather data).
gainCurve(1., 0., 0.)Apply gain curve correction (must be a tuple of (a0, a1, a2); see receiver details.
tcal1.Apply tcal factor. Note, this can also be used to scale the data (e.g., to produce Jy/Beam instead of K, or to apply antenna efficiency). If you want to use different tcal factors per Baseband or FeBe you will need to use the baseband/febe arguments and run the pipeline several times (once for each baseband/febe).

### Shell processing - CmdLinePipeline

Often one just wants to reprocess a bunch of Fitsfiles. For this, we made a small shell-(python-)script.

python CmdLinePipeline.py -h
usage: CmdLinePipeline.py [-h] [-c CLASSFILE] [--baseband BASEBAND]
[--febe FEBE] [--no-fsw-fold]
[--no-auto-lsr-correction]
[--averaging-method {mean,median}]
[--skip-dumps SKIPDUMPS SKIPDUMPS] [--spectral-flip]
[--shift-ref-chan]
fitsnames [fitsnames ...]

Command line interface to Class pipeline

positional arguments:
fitsnames             list of raw-fits files

optional arguments:
-h, --help            show this help message and exit
-c CLASSFILE, --classfile CLASSFILE
name of output class file (default
"<date>_<project>.100m") you can choose any name,
<date> and <project> are meta-variables and will be
replaced by what is found in the fits files
(potentially creating more than one class file)
--baseband BASEBAND   Filter by baseband
--febe FEBE           Filter by febe
--no-fsw-fold         do fswitch fold in pipeline (rather than class)
--aatm                apply AATM opacity correction
--gaincurve a0 a1 a2  apply gain curve correction (default: 1., 0., 0.)
Note, early versions of python-argparse dont support
scientific notation
--tcal TCAL           apply tcal factor (default: 1.) Note, this can also be
used to scale the data (e.g., to produce Jy/Beam
instead of K, or to apply antenna efficiency).If you
want to use different tcal factors per baseband or
FeBe you will need to use the --baseband/--febe
arguments and run the pipeline several times (once for
each baseband/febe)
--no-auto-lsr-correction
do not automatically correct for LSR shifts in fixed-
freq mode, default: False
which method to use for fswitch freq shifting
(default: fft) possible regridMethods are: fft,
linear, gaussian (the latter is recommended if you
experience ringing in the spectrum, linear and
gaussian decrease spectral resolution!)
--averaging-method {mean,median}
averaging method (mean or median, default: mean)
--skip-dumps SKIPDUMPS SKIPDUMPS
dumps to skip at beginning/end (tuple), default: 2 0
--spectral-flip       additional spectral flip of the data arrays, default:
False
--shift-ref-chan      before February 2011 the reference channel of the FFTS
had to beshifted by +0.5 channels if the spectrum was
not IF flipped, otherwise -0.5, default: False

The meaning of the parameters is like in the Table above (just slightly different argument names). The only difference is the '-c CLASSFILE' option that allows to give a certain class name. Default is "<date>_<project>.100m" where the date and project code are automatically filled-in from what is found in the MBfits files (this can lead to multiple class files obviously).

Typical usage would be:

# put everything into one class file
python CmdLinePipeline.py -c '/homes/user/myclass.100m' /daten/Raw/20141201*myprojectcode*XFFTS*

# put everything into separate class files (split by observing date and project code)
python CmdLinePipeline.py -c '/homes/user/<date>_<project>.100m' /daten/Raw/20141201*myprojectcode*XFFTS*

# process just one MBFITS file, and put class file(s) into current directory
python CmdLinePipeline.py /daten/Raw/20141211_2506_62-14_S13mm-XFFTS.FITS