importatca – Import ATCA RPFITS file(s) to a measurement set – import/export task

Description

Imports an arbitrary number of ATCA RPFITS format data sets into a casa measurement set. If more than one band is present, they will be put in the same measurement set but in a separate spectral window. The task will handle both old ATCA and new CABB (after April 2009) archive data.

Parameters

Title
Parameter	Default	Description
files	`numpy.array( [ ] )`	Name of input ATCA RPFits file(s)
vis	`''`	Name of output MeasurementSet
options	`''`	Processing options: birdie, reweight, noxycorr, fastmosaic, hires, noac (comma separated list)
spw	`numpy.array( [ ] )`	Select spectral window/channels
nscans	`numpy.array( [ ] )`	Number of scans to skip followed by number of scans to read
lowfreq	`{'value': float(0.1), 'unit': 'GHz'}`	Lowest reference frequency to select
highfreq	`{'value': float(999), 'unit': 'GHz'}`	Highest reference frequency to select
fields	`numpy.array( [ ] )`	List of field names to select
edge	`float(8)`	Percentage of edge channels to flag. For combined zooms, this specifies the percentage for a single zoom window

Parameter Explanations

files

numpy.array( [ ] )

Name of input ATCA RPFits file(s)

vis

''

Name of output MeasurementSet: Default: none

Example: vis=’mydata.ms’

options

''

Processing options

Default: none Options: birdie, reweight, noxycorr, fastmosaic, hires, noac (comma separated list)

birdie: (pre-CABB data only) discard edge channels and channels affected by internal RFI
reweight: (pre-CABB data only) suppress ringing of RFI spikes by reweighting of the lag spectrum
noxycorr: do not apply the xy phase correction as derived from the switched noise calibration, by default this is applied during loading of the data
fastmosaic: use this option if you are loading mosaic data with many pointings and only one or two integrations per pointing; this option changes the tiling of the data to avoid excessive I/O
hires: use this option if you have data in time binning mode (as used for pulsars) but you want to make it look like data with very short integration time (no bins)
noac: discard the auto-correlation data

spw

numpy.array( [ ] )

Select spectral window/channels: Default: ‘’ (all spectral windows and channels)

Examples: spw=’0~2,4’; spectral windows 0,1,2,4 (all channels) spw=’<2’; spectral windows less than 2 (i.e. 0,1) spw=’0:5~61’; spw 0, channels 5 to 61 spw=’0,10,3:3~45’; spw 0,10 all channels, spw 3 - chans 3 to 45. spw=’0~2:2~6’; spw 0,1,2 with channels 2 through 6 in each. spw = ‘*:3~64’ channels 3 through 64 for all sp id’s spw = ‘ :3~64’ will NOT work.

nscans

numpy.array( [ ] )

Number of scans to skip followed by number of scans to read

Default: [0, 0]

lowfreq

{'value': float(0.1), 'unit': 'GHz'}

Lowest reference frequency to select: Default: 0.1GHz

highfreq

{'value': float(999), 'unit': 'GHz'}

Highest reference frequency to select: Default: 999GHz

fields

numpy.array( [ ] )

List of field names to select

edge

float(8)

The edge parameter specifies how many edge channels to discard as a percentage of the number of channels in each band.

Default: 8 (e.g., discard 82 channels from the top and bottom of a 2048 channel spectrum)

For combined zooms, this specifies the percentage for a single zoom window