# flagdata¶

flagdata(vis, mode='manual', autocorr=False, inpfile='', reason='any', tbuff=0.0, spw='', field='', antenna='', uvrange='', timerange='', correlation='', scan='', intent='', array='', observation='', feed='', clipminmax=[''], datacolumn='DATA', clipoutside=True, channelavg=False, chanbin=1, timeavg=False, timebin='0s', clipzeros=False, quackinterval=1.0, quackmode='beg', quackincrement=False, tolerance=0.0, addantenna='', lowerlimit=0.0, upperlimit=90.0, ntime='scan', combinescans=False, timecutoff=4.0, freqcutoff=3.0, timefit='line', freqfit='poly', maxnpieces=7, flagdimension='freqtime', usewindowstats='none', halfwin=1, extendflags=True, winsize=3, timedev='', freqdev='', timedevscale=5.0, freqdevscale=5.0, spectralmax=1000000.0, spectralmin=0.0, antint_ref_antenna='', minchanfrac=0.6, verbose=False, extendpols=True, growtime=50.0, growfreq=50.0, growaround=False, flagneartime=False, flagnearfreq=False, minrel=0.0, maxrel=1.0, minabs=0, maxabs=- 1, spwchan=False, spwcorr=False, basecnt=False, fieldcnt=False, name='Summary', action='apply', display='', flagbackup=True, savepars=False, cmdreason='', outfile='', overwrite=True, writeflags=True)[source]

All-purpose flagging task based on data-selections and flagging modes/algorithms.

[Description] [Examples] [Development] [Details]

Parameters
• vis (string) - Name of input visibility file

• mode (string=’manual’) - Flagging mode (list/manual/clip/quack/shadow/elevation/tfcrop/rflag/antint/extent/unflag/summary)

mode = manual
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• autocorr (bool=False) - Flag only the auto-correlations?

mode = manualflag
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• autocorr (bool=False) - Flag only the auto-correlations?

mode = list
• inpfile ({string, stringArray}=’’) - Input ASCII file, list of files or Python list of strings with flag commands.

• reason ({string, stringArray}=’any’) - Select by REASON types

• tbuff ({double, doubleArray}=0.0) - List of time buffers (sec) to pad timerange in flag commands

mode = clip
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• datacolumn ({string, stringArray}=’DATA’) - Data column on which to operate

• clipminmax (doubleArray=[‘’]) - Range to use for clipping

• clipoutside ({bool, boolArray}=True) - Clip outside the range, or within it

• channelavg ({bool, boolArray}=False) - Pre-average data across channels before analyzing visibilities for flagging

• chanbin ({int, intArray}=1) - Bin width for channel average in number of input channels

• timeavg ({bool, boolArray}=False) - Pre-average data across time before analyzing visibilities for flagging.

• timebin (string=’0s’) - Bin width for time average in seconds

• clipzeros (bool=False) - Clip zero-value data

mode = quack
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• quackinterval ({double, doubleArray, int, intArray}=1.0) - Quack n seconds from scan beginning or end

• quackmode ({string, stringArray}=’beg’) - Quack mode. Flag intervals of the scan according to given mode.

• quackincrement ({bool, boolArray}=False) - Increment quack flagging in time taking into account flagged data or not.

• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• tolerance (double=0.0) - Amount of shadow allowed (in meters)

• addantenna ({string, record}=’’) - File name or dictionary with additional antenna names, positions and diameters

mode = elevation
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• lowerlimit (double=0.0) - Lower limiting elevation (in degrees)

• upperlimit (double=90.0) - Upper limiting elevation (in degrees)

mode = tfcrop
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• ntime ({double, string}=’scan’) - Time-range to use for each chunk (in seconds or minutes)

• combinescans (bool=False) - Accumulate data across scans depending on the value of ntime.

• datacolumn ({string, stringArray}=’DATA’) - Data column on which to operate

• timecutoff (double=4.0) - Flagging thresholds in units of deviation from the fit

• freqcutoff (double=3.0) - Flagging thresholds in units of deviation from the fit

• timefit (string=’line’) - Fitting function for the time direction (poly/line)

• freqfit (string=’poly’) - Fitting function for the frequency direction (poly/line)

• maxnpieces (int=7) - Number of pieces in the polynomial-fits (for freqfit or timefit poly)

• flagdimension (string=’freqtime’) - Dimensions along which to calculate fits (freq, time, freqtime, timefreq)

• usewindowstats (string=’none’) - Calculate additional flags using sliding window statistics (none, sum, std, both)

• halfwin (int=1) - Half-width of sliding window to use with usewindowstats (1,2,3).

• extendflags (bool=True) - Extend flags along time, frequency and correlation.

• channelavg ({bool, boolArray}=False) - Pre-average data across channels before analyzing visibilities for flagging

• chanbin ({int, intArray}=1) - Bin width for channel average in number of input channels

• timeavg ({bool, boolArray}=False) - Pre-average data across time before analyzing visibilities for flagging.

• timebin (string=’0s’) - Bin width for time average in seconds

mode = rflag
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• ntime ({double, string}=’scan’) - Time-range to use for each chunk (in seconds or minutes)

• combinescans (bool=False) - Accumulate data across scans depending on the value of ntime.

• datacolumn ({string, stringArray}=’DATA’) - Data column on which to operate

• winsize (int=3) - Number of timesteps in the sliding time window

• timedev (variant=’’) - Time-series noise estimate

• freqdev (variant=’’) - Spectral noise estimate

• timedevscale (double=5.0) - Threshold scaling for timedev

• freqdevscale (double=5.0) - Threshold scaling for freqdev.

• spectralmax (double=1E6) - Flag whole spectrum if freqdev is greater than spectralmax

• spectralmin (double=0.0) - Flag whole spectrum if freqdev is less than spectralmin

• extendflags (bool=True) - Extend flags along time, frequency and correlation.

• channelavg ({bool, boolArray}=False) - Pre-average data across channels before analyzing visibilities for flagging

• chanbin ({int, intArray}=1) - Bin width for channel average in number of input channels

• timeavg ({bool, boolArray}=False) - Pre-average data across time before analyzing visibilities for flagging.

• timebin (string=’0s’) - Bin width for time average in seconds

mode = antint
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• datacolumn ({string, stringArray}=’DATA’) - Data column on which to operate

• antint_ref_antenna (string=’’) - Antenna of interest. Baselines with this antenna will be checked for flagged channels.

• minchanfrac (double=0.6) - Minimum fraction of flagged channels required for a baseline to be deemed as flagged

• verbose (bool=False) - Print timestamps of flagged integrations to the log

mode = extend
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• ntime ({double, string}=’scan’) - Time-range to use for each chunk (in seconds or minutes)

• combinescans (bool=False) - Accumulate data across scans depending on the value of ntime.

• extendpols (bool=True) - If any correlation is flagged, flag all correlations

• growtime (double=50.0) - Flag all ntime integrations if more than X percent of the timerange is flagged (0-100)

• growfreq (double=50.0) - Flag all selected channels if more than X percent of the frequency range is flagged (0-100)

• growaround (bool=False) - Flag data based on surrounding flags

• flagneartime (bool=False) - Flag one timestep before and after a flagged one (True/False)

• flagnearfreq (bool=False) - Flag one channel before and after a flagged one (True/False)

mode = unflag
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

mode = summary
• field ({string, stringArray}=’’) - Select field using field id(s) or field name(s)

• spw ({string, stringArray}=’’) - Select spectral window/channels

• antenna ({string, stringArray}=’’) - Select data based on antenna/baseline

• timerange ({string, stringArray}=’’) - Select data based on time range

• correlation ({string, stringArray}=’’) - Select data based on correlation

• scan ({string, stringArray}=’’) - Scan number range

• intent ({string, stringArray}=’’) - Select observing intent

• array ({string, stringArray}=’’) - (Sub)array numbers

• uvrange ({string, stringArray}=’’) - Select data by baseline length.

• observation ({string, int}=’’) - Select by observation ID(s)

• feed ({string, stringArray}=’’) - Multi-feed numbers: Not yet implemented

• minrel (double=0.0) - Minimum number of flags (relative)

• maxrel (double=1.0) - Maximum number of flags (relative)

• minabs (int=0) - Minimum number of flags (absolute)

• maxabs (int=-1) - Maximum number of flags (absolute). Use a negative value to indicate infinity.

• spwchan (bool=False) - Print summary of channels per spw

• spwcorr (bool=False) - Print summary of correlation per spw

• basecnt (bool=False) - Print summary counts per baseline

• fieldcnt (bool=False) - Produce a separated breakdown for each field

• name (string=’Summary’) - Name of this summary report (key in summary dictionary)

• action (string=’apply’) - Action to perform in MS and/or in inpfile (none/apply/calculate)

action = apply
• display (string=’’) - Display data and/or end-of-MS reports at runtime (data/report/both).

• flagbackup (bool=True) - Back up the state of flags before the run

action = calculate
• display (string=’’) - Display data and/or end-of-MS reports at runtime (data/report/both).

• savepars (bool=False) - Save the current parameters to the FLAG_CMD table or to a file

savepars = True
• cmdreason (string=’’) - Reason to save to output file or to FLAG_CMD table.

• outfile (string=’’) - Name of output file to save current parameters. If empty, save to FLAG_CMD

• overwrite (bool=True) - Overwrite an existing file to save the flag commands

Description

This task can flag a MeasurementSet or a calibration table. It has two main types of operation. One type will read the parameters from the interface and flag using any of the various available modes. The other type will read the commands from a text file, a list of files or a Python list of strings, containing a list of flag commands (each line containing data selection parameters and any parameter specific for the mode being requested). Please see examples at the end of this help.

It is also possible to only save the parameters set in the interface without flagging. The parameters can be saved in the FLAG_CMD sub-table or in a text file. Note that when saving to an external file, the parameters will be appended to the given file.

The available flagging modes are: ‘manual’, ‘clip’, ‘shadow’, ‘quack’, ‘elevation’, ‘tfcrop’, ‘rflag’, ‘extend’, ‘unflag’ and ‘summary’. For automatic flagging, it is recommended to combine auto-flag modes with ‘extend’, via the list mode.

The current flags can be automatically backed up before applying new flags if the parameter flagbackup is set. Previous flag versions can be recovered using the flagmanager task.

Note

on flagging calibration tables:

flagdata can flag many types of calibration tables using mode=’manual’. It can only flag using the auto-flagging algorithms (‘clip’, ‘tfcrop’, or ‘rflag’), the cal tables that have the following data columns: CPARAM, FPARAM or SNR. The solution elements of the data columns are given in the correlation parameter using the names ‘Sol1’, ‘Sol2’, ‘Sol3’, or ‘Sol4’. See examples at the end of this help on how to flag different cal tables.

When the input is a calibration table, the modes ‘elevation’ and ‘shadow’ will be disabled. Data selection for calibration tables is limited to field, scan, timerange, antenna, spw and observation. It is only possible to save the parameters to an external file. If the calibration table was created before CASA 4.1, this task will create a dummy OBSERVATION column and OBSERVATION sub-table in the input calibration table to adapt it to the new cal table format.

Selecting antennas in some calibration tables have a different meaning compared to selecting the MS. Some calibration tables such as the antenna-based ones, created with some modes of gencal or polcal, have the ANTENNA2 column set to -1. This means that when selecting antenna=’ANT’, will select the whole ANT and not the cross-correlations between ANT and the other antennas. Similarly, the baseline syntax do not apply to this type of calibration tables. Those values with ampersand do not have any meaning when selecting antenna/baselines in antenna-based cal tables.

The task will flag a subset of data based on the following modes of operation:

• ‘list’ = list of flagging commands to apply to MS/cal table

• ‘manual’ = flagging based on specific selection parameters

• ‘clip’ = clip data according to values

• ‘quack’ = remove/keep specific time range at scan beginning/end

• ‘elevation’ = remove data below/above given elevations

• ‘tfcrop’ = automatic identification of outliers on the time-freq plane

• ‘rflag’ = automatic detection of outliers based on sliding-window RMS filters

• ‘antint’ = flag integrations if all baselines to a specified antenna are flagged

• ‘extend’ = extend and/or grow flags beyond what the basic algorithms detect

• ‘summary’ = report the amount of flagged data

• ‘unflag’ = unflag the specified data

A progress report line and a partial flagging summary is produced in the CASA logger every approximate 10% of the input data (since CASA 6.2), when the logger priority level is INFO (default). Additional messages are visible when setting a more detailed level of logging.

Parameter descriptions

vis

Name of input visibility file or calibration table. Default: ‘’ (none). Examples: vis=’uid___A002_X2a5c2f_X54.ms’, vis=’cal-X54.B1’

mode

Mode of operation. Options: ‘list’, ‘manual’, ‘clip’, ‘quack’, ‘shadow’, ‘elevation’, ‘tfcrop’, ‘extend’, ‘unflag’, ‘summary’. Default: ‘manual’

mode expandable parameters (except mode=’list’)

field

Select fields in mosaic. Use field id(s) or field name(s). [go listobs to obtain the list id’s or names] Default: ‘’ = all fields. If field string is a non-negative integer, it is assumed to be a field index otherwise, it is assumed to be a field name. Examples: field=’0~2’, field ids 0,1,2; field=’0,4,5~7’, field ids 0,4,5,6,7; field=’3C286,3C295’, field named 3C286 and 3C295; field = ‘3,4C*, field id 3 and all names starting with 4C.

spw

Select data based on spectral window and channels. Default: ‘’ => all spectral windows and channels. Examples: spw=’0~2,4’, spectral windows 0,1,2,4 (all channels); spw=’0:5~61’, spw 0, channels 5 to 61; spw=’<2’, spectral windows less than 2 (i.e. 0,1); spw=’0,10,3:3~45’, spw 0,10 all channels, spw 3, channels 3 to 45; spw=’0~2:2~6’; spw 0,1,2 with channels 2 through 6 in each; spw=’0:0~10;15~60’; spectral window 0 with channels 0-10,15-60; spw=’0:0~10,1:20~30,2:1;2;3’; spw 0, channels 0-10, spw 1, channels 20-30, and spw 2, channels, 1,2 and 3.

Note

For modes ‘clip’, ‘tfcrop’, and ‘rflag’, channel-ranges can be excluded from flagging by leaving them out of the selection range. This is a way to protect known spectral-lines from being flagged by the autoflag algorithms.

antenna

Select data based on baseline. Default: ‘’ (all). Examples: antenna=’DV04&DV06’ baseline DV04-DV06; antenna=’DV04&DV06;DV07&DV10’ baselines DV04-DV06 and DV07-DV10; antenna=’DV06’ all cross-correlation baselines between antenna DV06 and all other available antennas; antenna=’DV04,DV06’ all baselines with antennas DV04 and DV06; antenna=’DV06&&DV06’ only the auto-correlation baselines for antenna DV06; antenna=’DV04&&’* cross and auto-correlation baselines between antenna DV04 and all other available antennas; antenna=’0~2&&&’ only the auto-correlation baselines for antennas in range 0~2

Note

For some antenna-based calibration tables, selecting baselines with the & syntax do not apply.

timerange

Select data based on time range. Default: ‘’ (all). Examples: timerange = ‘YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss’;

Note

if YYYY/MM/DD is missing date defaults to first day in data set.

timerange=’09:14:0~09:54:0’ picks 40 min on first day; timerange=’25:00:00~27:30:00’ picks 1 hr to 3 hr 30min on NEXT day; timerange=’09:44:00’ pick data within one integration of time; timerange=’>10:24:00’ data after this time.

correlation

Correlation types or expression. Default: ‘’ (all correlations). For modes clip, tfcrop or rflag, the default means ABS_ALL. If the input is cal table that does not contain a complex data column, the default will fall back to REAL_ALL. Examples: correlation=’XX,YY’ or options: Any of ‘ABS’, ‘ARG’, ‘REAL’, ‘IMAG’, ‘NORM’ followed by any of ‘ALL’, ‘I’, ‘XX’, ‘YY’, ‘RR’, ‘LL’, ‘WVR’. ‘WVR’ refers to the water vapour radiometer of ALMA data. For calibration tables, the solutions are: ‘Sol1’, ‘Sol2’, Sol3, Sol4. Correlation selection is not supported for modes other than ‘clip’, ‘tfcrop’, or ‘rflag’ in cal tables.

Note

The operators ABS, ARG, REAL, etc. are written only once as the first value. If more than one correlation is given, the operator will be applied to all of them. The expression is used only in modes ‘clip’, ‘tfcrop’, and ‘rflag’.

scan

Scan number range. Default: ‘’ (all). Examples: scan=’1~5’. Check ‘go listobs’ to insure the scan numbers are in order.

intent

Select data based on scan intent. Intent selection is not supported for cal tables. Default: ‘’ (all). Examples: intent=’*CAL,*BAND*’*

array

Selection based on the antenna array. Array selection is not supported for cal tables. Default: ‘’ (all).

uvrange

Select data within uvrange (default units meters). Default: ‘’ (all). Examples: uvrange=’0~1000klambda’, uvrange from 0-1000 kilo-lambda; uvrange=’>4klambda’, uvranges greater than 4 kilo lambda. uvrange selection is not supported for cal tables.

observation

Selection based on the observation ID. Default: ‘’ (all). Examples: observation=’1’ or observation=1

feed

Selection based on the feed - NOT IMPLEMENTED YET

mode=’manual’ expandable parameters

Flag according to the data selection specified. This is the default mode (used when the mode is not specified).

autocorr

Flag only the auto-correlations. Note that this parameter is only active when set to True. If set to False it does NOT mean “do not flag auto-correlations”. When set to True, it will only flag data from a processor of type CORRELATOR. Default: False. Otions: True, False

mode=’list’ expandable parameters

Flag according to the data selection and flag commands specified in the input list. The input list may come from a text file, a list of text files or from a Python list of strings. Each input line may contain data selection parameters and any parameter specific to the mode given in the line. Default values will be used for the parameters that are not present in the line. Each line will be taken as a command to the task. If data is pre-selected using any of the selection parameters, then flagging will apply only to that subset of the MS.

For optimization and whenever possible, the task will create a union of the data selection parameters present in the list and select only that portion of the MS.

Note

The flag commands will be applied only when action=’apply’. If action=’calculate’ the flags will be calculated, but not applied. This is useful if display is set to something other than ‘none’. If action=’’ or ‘none’, the flag commands will not be applied either. An empty action is useful only to save the parameters of the list to a file or to the FLAG_CMD sub-table.

NOTE2: quackincrement = True works based on the state of prior flagging, and unless it is the first item in the list the agent doing the quacking in list mode doesn’t know about the state of prior flags. In this case, the command with quackincrement=True will be ignored and the task will issue a WARNING.

inpfile

Input ASCII file, list of files or a Python list of command strings. Default: ‘’. Options: [ ] with flag commands or [ ] with filenames or ‘ ‘ with a filename.

Warning

IMPORTANT: From CASA 4.3 onwards, the parser will be strict and accept only valid flagdata parameters in the list. It will check each parameter name and type and exit with an error if any of them is wrong. String values must contain quotes around them or the parser will not work. The parser evaluates the commands in the list and considers only existing Python types.

Note

There should be no whitespace between KEY=VALUE since the parser first breaks command lines on whitespace, then on “=”. Use only one whitespace to separate the parameters (no commas). Scroll down to the bottom to see a detailed description of the input list syntax..

Example1: The following commands can be saved to a file or group of files and given to the task (e.g., save it to ‘flags.txt’):

scan='1~3' mode='manual'
mode='clip' clipminmax=[0,2] correlation='ABS_XX' clipoutside=False
spw='9' mode='tfcrop' correlation='ABS_YY' ntime=51.0
mode='extend' extendpols=True

flagdata(vis, mode='list', inpfile='flags.txt')


or

flagdata(vis, mode='list', inpfile=['onlineflags.txt','otherflags.txt'])


Example2: The same commands can be given in a Python list on the command line to the task.

cmd=["scan='1~3' mode='manual'",
"mode='clip' clipminmax=[0,2] correlation='ABS_XX' clipoutside=False",
"spw='9' mode='tfcrop' correlation='ABS_YY' ntime=51.0",
"mode='extend' extendpols=True"]
flagdata(vis,mode='list',inpfile=cmd)


reason

Select flag commands based on REASON(s). Can be a string, or list of strings. If inpfile is a list of files, the reasons given in this parameter will apply to all the files. Default: ‘any’ (all flags regardless of reason). Examples: reason=’FOCUS_ERROR’; reason=[‘FOCUS_ERROR’, ‘SUBREFLECTOR_ERROR’]

Note

NOTE: what is within the string is literally matched, e.g. reason=’’ matches only blank reasons, and r eason = ‘FOCUS_ERROR, SUBREFLECTOR_ERROR’ matches this compound reason string only. See the syntax for writing flag commands at the end of this help.

tbuff

A time buffer or list of time buffers to pad the timerange parameters in flag commands. When a list of 2 time buffers is given, it will subtract the first value from the lower time and the second value will be added to the upper time in the range. The 2 time buffer values can be different, allowing to have an irregular time buffer padding to time ranges. If the list contains only one time buffer, it will use it to subtract from t0 and add to t1. If more than one list of input files is given, tbuff will apply to all of the flag commands that have timerange parameters in the files.

Each tbuff value should be a float number given in seconds. Default: 0.0 (it will not apply any time padding). Example: tbuff=[0.5, 0.8] inpfile=[‘online.txt’,’userflags.txt’]. The timerange parameters in the ‘online.txt’ file are first converted to seconds. Then, 0.5 is subtracted from t0 and 0.8 is added to t1, where t0 and t1 are the two intervals given in timerange. Similarly, tbuff will be applied to any timerange in ‘userflags.txt’.

Warning

IMPORTANT: This parameter assumes that timerange = t0 ~ t1, therefore it will not work if only t0 or t1 is given.

Note

The most common use-case for tbuff is to apply the online flags that are created by importasdm when savecmds=True. The value of a regular time buffer should be tbuff=0.5*max (integration time).

mode=’clip’ expandable parameters

Clip data according to values of the following subparameters. The polarization expression is given by the correlation parameter. For calibration tables, the solutions are also given by the correlation parameter.

clipminmax

Range of data (Jy) that will NOT be flagged. It will always flag the NaN/Inf data, even when a range is specified. Default: [ ]. Example: clipminmax=[0.0,1.5]

clipoutside

Clip OUTSIDE the range. Default: True. Example: clipoutside=False, flag data WITHIN the clipminmax range.

clipzeros

Clip zero-value data. Default: False.

mode=’clip’, ‘tfcrop’, or ‘rflag’ expandable parameters

datacolumn

Column to use for clipping. Default: ‘DATA’. Options: MS columns: ‘DATA’, ‘CORRECTED’, ‘MODEL’, ‘RESIDUAL’, ‘RESIDUAL_DATA’, ‘WEIGHT_SPECTRUM’, ‘WEIGHT’, ‘FLOAT_DATA’. Cal table columns: ‘FPARAM’, ‘CPARAM’, ‘SNR’, ‘WEIGHT’.

Note

RESIDUAL = CORRECTED - MODEL

RESIDUAL_DATA = DATA - MODEL

When datacolumn is WEIGHT, the task will internally use WEIGHT_SPECTRUM. If WEIGHT_SPECTRUM does not exist, it will create one on-the-fly based on the values of WEIGHT.

channelavg

Pre-average data across channels before analyzing visibilities for flagging. Partially flagged data is not be included in the average unless all data contributing to a given output channel is flagged. If present, WEIGHT_SPECTRUM/ SIGMA_SPECTRUM are used to compute a weighted average (WEIGHT_SPECTRUM for CORRECTED_DATA and SIGMA_SPECTRUM for DATA). Default: False. Options: True/False

Note

NOTE: Pre-average across channels is not supported for calibration tables.

chanbin

Bin width for channel average in number of input channels. If a list is given, each bin applies to one of the selected SPWs. When chanbin is set to 1 all input channels are used considered for the average to produce a single output channel, this behaviour aims to be preserve backwards compatibility with the previous pre-averaging feature of clip mode. Default: 1

timeavg

Pre-average data across time before analyzing visibilities for flagging. Partially flagged data is not be included in the average unless all data contributing to a given output channel is flagged. If present, WEIGHT_SPECTRUM/ SIGMA_SPECTRUM are used to compute a weighted average (WEIGHT_SPECTRUM for CORRECTED_DATA and SIGMA_SPECTRUM for DATA). Otherwise WEIGHT/ SIGMA are used to average together data from different integrations. Default: False. Options: True/False

Note

NOTE: Pre-average across time is not supported for calibration tables

timebin

Bin width for time average in seconds. Default: ‘0s’

mode=’quack’ expandable parameters

Option to remove specified part of scan beginning/end.

quackinterval

Time in seconds from scan beginning or end to flag. Make time slightly smaller than the desired time. Default: 0.0. Type: int or float.

quackmode

Quack mode. Default: ‘beg’. Options:

• ‘beg’ ==> flag an interval at the beginning of scan

• ‘endb’ ==> flag an interval at the end of scan

• ‘tail’ ==> flag all but an interval at the beginning of scan

• ‘end’ ==> flag all but an interval at end of scan

Visual representation of quack mode flagging one scan with 1s duration. The following diagram shows what is flagged for each quack mode when quackinterval is set to 0.25s. The flagged part is represented by crosses (+++++++++):

           scan with 1s duration
--------------------------------------------
beg
+++++++++++---------------------------------
endb
---------------------------------+++++++++++
tail
-----------+++++++++++++++++++++++++++++++++
end
+++++++++++++++++++++++++++++++++-----------


quackincrement

Increment quack flagging in time taking into account flagged data or not. Default: False. Type: bool

• False ==> the quack interval is counted from the scan boundaries, as determined by the quackmode parameter, regardless if data has been flagged or not.

• True ==> the quack interval is counted from the first unflagged data in the scan.

Warning

quackincrement = True works based on the state of prior flagging, and unless it is the first item in the list the agent doing the quacking in list mode doesn’t know about the state of prior flags. In this case, the command with quackincrement=True will be ignored and the task will issue a WARNING.

Option to flag data of shadowed antennas. This mode is not available for cal tables.

All antennas in the ANTENNA subtable of the MS (and the corresponding diameters) will be considered for shadow-flag calculations. For a given timestep, an antenna is flagged if any of its baselines (projected onto the uv-plane) is shorter than radius $$_{1}$$ $$+$$ radius $$_{2}$$ $$-$$ tolerance. The value of ‘w’ is used to determine which antenna is behind the other. The phase-reference center is used for antenna-pointing direction.

tolerance

Amount of shadowing allowed (or tolerated), in meters. A positive number allows antennas to overlap in projection. A negative number forces antennas apart in projection. Zero implies a distance of radius $$_{1}$$ $$+$$ radius $$_{2}$$ between antenna centers. Default: 0.0

It can be either a file name with additional antenna names, positions and diameters, or a Python dictionary with the same information. You can use the flaghelper functions to create the dictionary from a file. Default: ‘’. Type: string or {} (dictionary). To create a dictionary inside CASA:

import flaghelper as fh


Where antfile is a text file in disk that contains information such as:

name=VLA01
diameter=25.0
position=[-1601144.96146691, -5041998.01971858, 3554864.76811967]
name=VLA02
diameter=25.0
position=[-1601105.7664601889, -5042022.3917835914, 3554847.245159178]


mode=’elevation’ expandable parameters

Option to flag based on antenna elevation. This mode is not available for cal tables.

lowerlimit

Lower limiting elevation in degrees. Data coming from a baseline where one or both antennas were pointing at a strictly lower elevation (as function of time), will be flagged. Default: 0.0

upperlimit

Upper limiting elevation in degrees. Data coming from a baseline where one or both antennas were pointing at a strictly higher elevation (as function of time), will be flagged. Default: 90.0

mode=’tfcrop’, ‘rflag’, or ‘extend’ expandable parameters

ntime

Time range (in seconds or minutes) over which to buffer data before running the algorithm. Options: ‘scan’ or any other float value or string containing the units. Default: ‘scan’. Examples: ntime=’1.5min’; ntime=1.2 (taken in seconds). The dataset will be iterated through in time-chunks defined here.

Warning

WARNING: If ntime=’scan’ and combinescans=True, all the scans will be loaded at once, thus requesting a lot of memory depending on the available spws.

combinescans

Accumulate data across scans depending on the value of ntime. Default: False. This parameter should be set to True only when ntime is specified as a time-interval (not ‘scan’). When set to True, it will remove SCAN from the sorting columns, therefore it will only accumulate across scans if ntime is not set to ‘scan’.

mode=’tfcrop’ expandable parameters

Flag using the TFCrop autoflag algorithm. For each field, spw, timerange (specified by ntime), and baseline:

1. Average visibility amplitudes along time dimension to form an

average spectrum

1. Calculate a robust piece-wise polynomial fit for the band-shape at the base of RFI spikes. Calculate ‘stddev’ of (data - fit).

2. Flag points deviating from the fit by more than N-stddev

3. Repeat (1-3) along the other dimension.

This algorithm is designed to operate on un-calibrated data (step (2)), as well as calibrated data. It is recommended to extend the flags after running this algorithm. See the sub-parameter extendflags below.

timecutoff

Flag threshold in time. Flag all data-points further than N-stddev from the fit. This threshold catches time-varying RFI spikes (narrow and broad-band), but will not catch RFI that is persistent in time. Default: 4.0.

Flagging is done in up to 5 iterations. The stddev calculation is adaptive and converges to a value that reflects only the data and no RFI. At each iteration, the same relative threshold is applied to detect flags. (Step (3) of the algorithm).

freqcutoff

Flag threshold in frequency. Flag all data-points further than N-stddev from the fit. Same as timecutoff, but along the frequency-dimension. This threshold catches narrow-band RFI that may or may not be persistent in time. Default: 3.0

timefit

Fitting function for the time direction. Default: ‘line’. Options: ‘line’, ‘poly’

A ‘line’ fit is a robust straight-line fit across the entire timerange (defined by ntime). A ‘poly’ fit is a robust piece-wise polynomial fit across the timerange.

Note

A robust fit is computed in upto 5 iterations. At each iteration, the stddev between the data and the fit is computed, values beyond N-stddev are flagged, and the fit and stddev are re-calculated with the remaining points. This stddev calculation is adaptive, and converges to a value that reflects only the data and no RFI. It also provides a varying set of flagging thresholds, that allows deep flagging only when the fit best represents the true data. Choose ‘poly’ only if the visibilities are expected to vary significantly over the timerange selected by ntime, or if there is a lot of strong but intermittent RFI.

freqfit

Fitting function for the frequency direction. Same as for the timefit parameter. Default: ‘poly’. Options: ‘line’, ‘poly’. Choose ‘line’ only if you are operating on bandpass-corrected data, or residuals, and expect that the bandshape is linear. The ‘poly’ option works better on uncalibrated bandpasses with narrow-band RFI spikes.

maxnpieces

Maxinum number of pieces to allow in the piecewise-polynomial fits. Default: 7. Options: 1 - 9. This parameter is used only if timefit or freqfit are chosen as ‘poly’. If there is significant broad-band RFI, reduce this number. Using too many pieces could result in the RFI being fitted in the clean bandpass. In later stages of the fit, a third-order polynomial is fit per piece, so for best results, please ensure that nchan/maxnpieces is at-least 10.

flagdimension

Choose the directions along which to perform flagging. Default: ‘freqtime’; first flag along frequency, and then along time. Options: ‘time’, ‘freq’, ‘timefreq’, ‘freqtime’. For most cases, ‘freqtime’ or ‘timefreq’ are appropriate, and differences between these choices are apparant only if RFI in one dimension is significantly stronger than the other. The goal is to flag the dominant RFI first. If there are very few (less than 5) channels of data, then choose ‘time’. Similarly for ‘freq’.

usewindowstats

Use sliding-window statistics to find additional flags. Default: ‘none’. Options: ‘none’, ‘sum’, ‘std’, ‘both’

Warning

This parameter is experimental!

The ‘sum’ option chooses to flag a point, if the mean-value in a window centered on that point deviates from the fit by more than N-stddev $$/ 2.0$$.

Note

stddev is calculated between the data and fit as explained in Step (2). This option is an attempt to catch broad-band or time-persistent RFI that the above polynomial fits will mistakenly fit as the clean band. It is an approximation to the sumThreshold method found to be effective by Offringa et.al (2010) for LOFAR data.

The ‘std’ option chooses to flag a point, if the ‘local’ stddev calculated in a window centered on that point is larger than N-stddev $$/2.0$$. This option is an attempt to catch noisy RFI that is not excluded in the polynomial fits, and which increases the global stddev, and results in fewer flags (based on the N-stddev threshold).

halfwin

Half width of sliding window to use with usewindowstats. Default: 1 (a 3-point window size). Options: 1,2,3

Warning

This is experimental!

mode=’tfcrop’ or ‘rflag’ expandable parameters

extendflags

Extend flags along time, frequency and correlation. Default: True

Note

It is usually helpful to extend the flags along time, frequency, and correlation using this parameter, which will run the ‘extend’ mode after ‘tfcrop’ and extend the flags if more than 50% of the timeranges are already flagged, and if more than 80% of the channels are already flagged. It will also extend the flags to the other polarizations. The user may also set extendflags to False and run the ‘extend’ mode in a second step within the same flagging run. See the example below.

mode=’rflag’ expandable parameters

Detect outliers based on the RFlag algorithm [1]. The polarization expression is given by the correlation parameter. Iterate through the data in chunks of time. For each chunk, calculate local statistics, and apply flags based on user supplied (or auto-calculated) thresholds.

• Time analysis (for each channel):

• calculate local RMS of real and imaginary visibilities within a sliding time window

• calculate the median RMS across time windows, deviations of local RMS from this median, and the median deviation

• flag if local RMS is larger than timedevscale $$x$$ (medianRMS $$+$$ medianDev)

• Spectral analysis (for each time):

• calculate avg of real and imaginary visibilities and their RMS across channels

• calculate the deviation of each channel from this avg, and the median-deviation

• flag if deviation is larger than freqdevscale $$x$$ medianDev

It is recommended to extend the flags after running this algorithm. See the sub-parameter extendflags below.

Notice that by default the flag implementation in CASA is able to calculate the thresholds and apply them on-the-fly (OTF). There is a significant performance gain with this approach, as the visibilities don’t have to be read twice, and therefore is highly recommended (see example 1). Otherwise it is possible to reproduce the AIPS usage pattern by doing a first run with action=’calculate’ and a second run with action=’apply’. The advantage of this approach is that the thresholds are calculated using the data from all scans, instead of calculating them for one scan only (see example 3).

Example usage :

1. Calculate thresholds automatically per scan, and use them to find flags. Specify scale-factor for time-analysis thresholds, use default for frequency.

flagdata('my.ms', mode='rflag', spw='9', timedevscale=4.0)

2. Supply noise-estimates to be used with default scale-factors.

flagdata(vis='my.ms', mode='rflag', spw='9', timedev=0.1,
freqdev=0.5, action='calculate')

3. Two-passes. This replicates the usage pattern in AIPS.

• The first pass saves commands in output text files, with auto-calculated thresholds. Thresholds are returned from ‘rflag’ only when action=’calculate’. The user can edit this file before doing the second pass, but the python-dictionary structure must be preserved. The parameters timedevscale and freqdevscale are not used in this first pass.

• The second pass applies these commands (action=’apply’).

flagdata(vis='my.ms', mode='rflag', spw='9,10',
timedev='tdevfile.txt', freqdev='fdevfile.txt',
action='calculate')

flagdata(vis='my.ms', mode='rflag', spw='9,10',
timedev='tdevfile.txt', freqdev='fdevfile.txt',
action='apply')


With action=’calculate’, display=’report’ will produce diagnostic plots showing data-statistics and thresholds (the same thresholds as those written out to ‘tdevfile.txt’ and ‘fdevfile.txt’). In this second pass, with action=’apply’, the parameters freqdevscale and timedevscale can be used to re-scale the thresholds calculated in the first pass.

Note

NOTE1: The RFlag algorithm was originally developed by Eric Greisen in AIPS [1] .

NOTE2: Since this algorithm operates with two passes through each chunk of data (time and freq axes), some data points get flagged twice. This can affect the flag-percentage estimate printed in the logger at runtime. An accurate estimate can be obtained via the ‘summary’ mode.

NOTE3: RFlag calculates statistics across all selected correlations. Therefore, if there is a significant amplitude difference between parallel-hand and cross-hand correlations, or between different solutions in a gain table, it is advisable to pre-select subsets of correlations (or sols) on which to run one instance of RFlag. For example, correlation=’RR,LL’ or correlation=’ABS sol1,sol2’.

Note

Dictionaries returned by action=’calculate’. Rflag with action=’calculate’ (the first pass of the two-passes usage) can return a dictionary. The dictionary holds the freqdev and timedev thresholds calculated in that first pass. For example:

thresholds = flagdata(vis=’my.ms’, mode=’rflag’, action=’calculate’)

print(thresholds)

{‘type’: ‘list’, ‘report0’: {‘type’: ‘rflag’, ‘freqdev’: array([[ 1.0e+00, 0.0e+00, 3.13e-02], … , ‘name’: ‘Rflag’, ‘timedev’: array([[ 1.0e+00, 0.0e+00, 6.8e-03], … ])}, ‘nreport’: 1}

The timedev and freqdev items from this dictionary can be used in the second pass call to flagdata, but their respective values need to be passed as separate parameters. For example:

flagdata(vis=ms, mode=’rflag’, action=’apply’, timedev=thresholds[‘report0’][‘timedev’], freqdev=thresholds[‘report0’][‘freqdev’])

This is an alternative approach (and fully equivalent) to using two files to save and reuse the timedev and freqdev values.

winsize

Number of timesteps in the sliding time window (fparm(1) in AIPS). Default: 3

timedev

Time-series noise estimate (noise in AIPS). Default: [ ]. Examples: timedev = 0.5: Use this noise-estimate to calculate flags. Do not recalculate; timedev = [[1,9,0.2], [1,10,0.5]]: Use noise-estimate of 0.2 for field 1, spw 9, and noise-estimate of 0.5 for field 1, spw 10; timedev = [ ]: Auto-calculate noise estimates; timedev=’timedevfile’: Auto-calculate noise estimates and write them into a file with the name given (any string will be interpreted as a file name which will be checked for existence).

freqdev

Spectral noise estimate (scutoff in AIPS). This step depends on having a relatively-flat bandshape. Same parameter-options as timedev. Default: [ ]

timedevscale

For Step 1 (time analysis), flag a point if local RMS around it is larger than timedevscale $$x$$ timedev (fparm(0) in AIPS). This scale parameter is only applied when flagging (action=’apply’) and displaying reports (display option). It is not used when the thresholds are simply calculated and saved into files (action=’calculate’, as in the two-passes usage pattern of AIPS). Default: 5.0

freqdevscale

For Step 2 (spectral analysis), flag a point if local rms around it is larger than freqdevscale $$x$$ freqdev (fparm(10) in AIPS). Similarly as with timedevscale, freqdevscale is not used when the thresholds are simply calculated and saved into files (action=’calculate’, as in the two-passes usage pattern of AIPS). Default: 5.0

spectralmax

Flag whole spectrum if freqdev is greater than spectralmax (fparm(6) in AIPS). Default: 1E6

spectralmin

Flag whole spectrum if freqdev is less than spectralmin (fparm(5) in AIPS). Default: 0.0

mode=’extend’ expandable parameters

Extend and/or grow flags beyond what the basic algorithms detect. This mode will extend the accumulated flags available in the MS, regardless of which algorithm created them. It is recommended that any autoflag (tfcrop, rflag) algorithm be followed up by a flag extension. Extensions will apply only within the selected data, according to the settings of extendpols, growtime, growfreq, growaround, flagneartime, and flagnearfreq.

Note

Runtime summary counts in the logger can sometimes report larger flag percentages than what is actually flagged. This is because extensions onto already-flagged data-points are counted as new flags. An accurate flag count can be obtained via the ‘summary’ mode.

extendpols

Extend flags to all selected correlations. Default: True. Options: True/False. For example, to extend flags from RR to only RL and LR, a data-selection of correlation=’RR,LR,RL’ is required along with extendpols=True.

growtime

For any channel, flag the entire timerange in the current 2D chunk (set by ntime) if more than X% of the timerange is already flagged. Default: 50.0. Options: 0.0 - 100.0. This option catches the low-intensity parts of time-persistent RFI.

growfreq

For any timestep, flag all channels in the current 2D chunk (set by data-selection) if more than X% of the channels are already flagged. Default: 50.0. Options: 0.0 - 100.0. This option catches broad-band RFI that is partially identified by earlier steps.

growaround

Flag a point based on the number of flagged points around it. Default: False. Options: True/False. For every un-flagged point on the 2D time/freq plane, if more than four surrounding points are already flagged, flag that point. This option catches some wings of strong RFI spikes.

flagneartime

Flag points before and after every flagged one, in the time-direction. Default: False. Options: True/False. Note that this can result in excessive flagging.

flagnearfreq

Flag points before and after every flagged one, in the frequency-direction. Default: False. Options: True/False. This option allows flagging of wings in the spectral response of strong RFI. Note that this can result in excessive flagging.

mode=’antint’ expandable parameters

This mode flag all integrations in which a specified antenna is flagged. This mode operates for an spectral window. It flags any integration in which all baselines to a specified antenna are flagged, but only if this condition is satisfied in a fraction of channels within the spectral window of interest greater than a nominated fraction. For simplicity, it assumes that all polarization products must be unflagged for a baseline to be deemed unflagged. The antint mode implements the flagging approach introduced in ‘antintflag’ (https://doi.org/10.5281/zenodo.163546)

The motivating application for introducing this mode is removal of data that will otherwise lead to changes in reference antenna during gain calibration, which will in turn lead to corrupted polarization calibration.

antint_ref_antenna

Check the baselines to this antenna. Note that this is not the same as the general ‘antenna’ parameter of flagdata. The parameter antint_ref_antenna is mandatory with the ‘antint’ mode and chooses the antenna for which the fraction of channels flagged will be checked.

minchanfrac

Minimum fraction of flagged channels required for a baseline to be deemed as flagged. Takes values between 0-1 (float). In this mode flagdata does the following for every point in time. It checks the fraction of channels flagged for any of the polarization products and for every baseline to the antenna of interest. If the fraction is higher than this ‘minchanfrac’ threshold then the data are flagged for this pont in time (this includes all the rows selected with the flagdata command that have that timestamp). This parameter will be ignored if spw specifies a channel.

verbose

Print timestamps of flagged integrations to the log.

mode=’unflag’ expandable parameters

Unflag according to the data selection specified.

mode=’summary’ expandable parameters

List the number of rows and flagged data points for the MS’s meta-data. The resulting summary will be returned as a Python dictionary.

In ‘summary’ mode, the task returns a dictionary of flagging statistics.

Example1:

s = flagdata(..., mode='summary')


s will be a dictionary which contains:

• s[‘total’]: total number of data

• s[‘flagged’]: amount of flagged data

Example2: two summary commands in ‘list’ mode, intercalating a manual flagging command.

s = flagdata(..., mode='list', inpfile=["mode='summary'
name='InitFlags'", "mode='manual' autocorr=True",
"mode='summary' name='Autocorr'"])


The dictionary returned in s will contain two dictionaries, one for each of the two summary modes.

• s[‘report0’][‘name’]: ‘InitFlags’

• s[‘report1’][‘name’]: ‘Autocorr’

minrel

Minimum number of flags (relative) to include in histogram. Default: 0.0

maxrel

Maximum number of flags (relative) to include in histogram. Default: 1.0

minabs

Minimum number of flags (absolute, inclusive) to include in histogram. Default: 0

maxabs

Maximum number of flags (absolute, inclusive) to include in histogram. To indicate infinity, use any negative number. Default: -1

spwchan

List the number of flags per spw and per channel. Default: False

spwcorr

Llist the number of flags per spw and per correlation. Default: False

basecnt

List the number of flags per baseline. Default: False

fieldcnt

Produce a separated breakdown per field. Default: False

name

Name for this summary, to be used as a key in the returned Python dictionary. It is possible to call the ‘summary’ mode multiple times in ‘list’ mode. When calling the ‘summary’ mode as a command in a list, one can give different names to each one of them so that they can be easily pulled out of the summary’s dictionary. Default: ‘Summary’

action

Action to perform in MS/cal table or in the input list of parameters. Options: ‘none’, ‘apply’, ‘calculate’. Default: ‘apply’

action=’apply’ or ‘calculate’ expandable parameters

action=’apply’ applies the flags to the MS. action=’calculate’ only calculates the flags but does not write them to the MS. This is useful if used together with the display to analyze the results before writing to the MS.

display

Display data and/or end-of-MS reports at run-time. It needs to read a datacolumn for the plotting. The default for an MS is DATA, but the task will use FLOAT_DATA for a Single-dish MS. Default: ‘none’. Options: ‘none’, ‘data’, ‘report’, ‘both’

‘none’ –> It will not display anything. ‘data’ –> display data and flags per-chunk at run-time, within an interactive GUI.

• This option opens a GUI to show the 2D time-freq planes of the data with old and new flags, for all correlations per baseline.

• The GUI allows stepping through all baselines (prev/next) in the current chunk (set by ntime), and stepping to the next-chunk.

• The flagdata task can be quit from the GUI, in case it becomes obvious that the current set of parameters is just wrong.

• There is an option to stop the display but continue flagging.

‘report’ –> displays end-of-MS reports on the screen. ‘both’ –> displays data per chunk and end-of-MS reports on the screen

action=’apply’ expandable parameters

flagbackup

Automatically backup flags before running the tool. Flagversion names are chosen automatically, and are based on the mode being used. Default: True. Options: True/False

action=’’ or ‘none’ description

When set to empty or ‘none’, the underlying tool will not be executed and no flags will be produced. No data selection will be done either. This is useful when used together with the parameter savepars to only save the current parameters (or list of parameters) to the FLAG_CMD sub-table or to an external file.

savepars

Save the current parameters to the FLAG_CMD table of the MS or to an output text file.

Note that when display is set to anything other than ‘none’, savepars will be disabled. This is done because in an interactive mode, the user may skip data which may invalidate the initial input parameters and there is no way to save the interactive commands. When the input is a calibration table it is only possible to save the parameters to a file.

Default: False. Options: True/False

savepars=True expandable parameters

cmdreason

A string containing a reason to save to the FLAG_CMD table or to an output text file given by the outfile sub-parameter. If the input contains any reason, they will be replaced with this one. At the moment it is not possible to add more than one reason. Default: ‘ ‘, no reason will be added to output. Examples: cmdreason=’CLIP_ZEROS’

outfile

Name of output file to save the current parameters. Default: ‘ ‘, will save the parameters to the FLAG_CMD table of the MS. Examples: outfile=’flags.txt’ will save the parameters in a text file.

overwrite

Overwrite the existing file given in outfile. Options: True/False. Default: True, it will remove the existing file given in outfile and save the current flag commands to a new file with the same name. When set to False, the task will exit with an error message if the file exist.

SYNTAX FOR COMMANDS GIVEN IN A FILE or LIST OF STRINGS

Basic Syntax Rules

1. Commands are strings (which may contain internal “strings”) consisting of KEY=VALUE pairs separated by one whitespace only.

Note

There should be no whitespace between KEY=VALUE.The parser first breaks command lines on whitespace, then on “=”.

1. Use only ONE white space to separate the parameters (no commas). Each key should only appear once on a given command line/string.

2. There is an implicit mode for each command, with the default being ‘manual’ if not given.

3. Comment lines can start with ‘#’ and will be ignored. The parser used in flagdata will check each parameter name and type and exit with an error if the parameter is not a valid flagdata parameter or of a wrong type.

Example:

scan='1~3' mode='manual'
# this line will be ignored
spw='9' mode='tfcrop' correlation='ABS_XX,YY' ntime=51.0
mode='extend' extendpols=True
scan='1~3,10~12' mode='quack' quackinterval=1.0


Bibliography

1. Greisen, Eric, Dec 31, 2011. AIPS documentation: Section E.5 of the AIPS cookbook (Appendix E: Special Considerations for EVLA data calibration and imaging in AIPS, http://www.aips.nrao.edu/cook.html#CEE ) #ref-cit1

Examples

Examples of flagging a MeasurementSet

Note

NOTE: The vector mode of the flagdata task (pre-dating CASA 3.4) can be achieved with this task by using it with mode=’list’ and the commands given in a list in inpfile=[].

Flag using the ‘list’ mode and flag commands

flagdata('my.ms', inpmode='list', inpfile=["mode='clip'


Manually flag scans 1~3 and save the parameters to the FLAG_CMD sub-table.

flagdata('my.ms', scan='1~3, mode='manual', savepars=True)


Save the parameters to a file that is open in append mode.

flagdata('my.ms', scan='1~3, mode='manual', savepars=True,
outfile='flags.txt')


Flag all the commands given in the Python list of strings.

cmd = ["scan='1~3' mode='manual'", "spw='9' mode='tfcrop' correlation='ABS_RR,LL' ntime=51.0",
"mode='extend' extendpols=True"]

flagdata('my.ms', mode='list', inpfile=cmd)


Flag all the commands given in the file called ‘flags.txt’.

cat flags.txt
scan='1~3' mode='manual' spw='9' mode='tfcrop' correlation='ABS_RR,LL'
ntime=51.0 mode='extend' extendpols=True

flagdata('my.ms', mode='list', inpfile='flags.txt')


Display the data and flags per-chunk and do not write flags to the MS.

flagdata('my.ms', mode='list', inpfile='flags.txt',
action='calculate', display='data')


Flag all the antennas except antenna=5.

flagdata(vis='my.ms', antenna='!5', mode='manual)


Clip the NaN in the data. An empty clipminmax will flag only NaN.

flagdata('my.ms', mode='clip')


Clip only the water vapor radiometer data.

flagdata('my.ms',mode='clip',clipminmax=[0,50], correlation='ABS_WVR')


Clip only zero-value data.

flagdata('my.ms',mode='clip',clipzeros=True)


Flag only auto-correlations of non-radiometer data using the autocorr parameter.

flagdata('my.ms', autocorr=True)


Flag only auto-correlations using the antenna selection.

flagdata('my.ms', mode='manual', antenna='*&amp;&amp;&amp;')


Flag based on selected reasons from a file.

This box is intended for CASA Inputs. Insert your text here.>
cat flags.txt
scan='1~3' mode='manual' reason='MYREASON'
spw='9' mode='clip' clipzeros=True reason='CLIPZEROS'
mode='manual' scan='4' reason='MYREASON'

flagdata('my.ms', mode='list', inpfile='flags.txt',
reason='MYREASON').


The same result of 10a can be achieved using the task flagcmd.

flagcmd('my.ms', inpmode='file', inpfile='flags.txt',
action='apply', reason='MYREASON')


Automatic flagging using ‘rflag’, using auto-thresholds, and specifying a threshold scale-factor to use for flagging.

flagdata('my.ms', mode='rflag', spw='9', timedevscale=4.0,
action='apply')


Save the interface parameters to the FLAG_CMD sub-table of the MS. Add a reason to the flag command. This cmdreason will be added to the REASON column of the FLAG_CMD sub-table. Apply flags in flagcmd.

flagdata('my.ms', mode='clip', channelavg=False,
clipminmax=[30., 60.], spw='0:0~10',
correlation='ABS_XX,XY', action='',
savepars=True, cmdreason='CLIPXX_XY')
#Select based on the reason.
flagcmd('my.ms', action='apply', reason='CLIPXX_XY')


Flag antennas that are shadowed by antennas not present in the MS.

> Create a text file with information about the antennas.
> cat ant.txt
name=VLA01
diameter=25.0
position=[-1601144.96146691, -5041998.01971858, 3554864.76811967]
name=VLA02
diameter=25.0
position=[-1601105.7664601889, -5042022.3917835914, 3554847.245159178]
name=VLA09
diameter=25.0
position=[-1601197.2182404203, -5041974.3604805721, 3554875.1995636248]
name=VLA10
diameter=25.0

position=[-1601227.3367843349,-5041975.7011900628,3554859.1642644769]



The antenna information can also be given as a Python dictionary. To create the dictionary using the flaghelper functions, do the following inside casapy:

> import flaghelper as fh


Apply the online flags that come from importasdm.

> In importasdm, save the online flags to a file.
importasdm('myasdm', 'asdm.ms', process_flags=True,
savecmds=True, outfile='online_flags.txt')
> You can edit the online_flags.txt to add other flagging
commands or apply it directly.
flagdata('asdm.ms', mode='list', inpfile='online_flags.txt')
> The same result can be achieved using the task flagcmd.
flagcmd('asdm.ms', inpmode='file', inpfile='online_flags.txt', action='apply')


Clip mode pre-averaging data across channels and across time.

flagdata(vis='Four_ants_3C286.ms', flagbackup=False, mode='clip', datacolumn='DATA',
timeavg=True, timebin='2s', channelavg=True, chanbin=2)


Reduce the fraction of channels that are required to be flagged, and print information for every integration that is flagged.

flagdata(vis, ..., mode='antint', spw='9', antint_ref_antenna='ea01', minchanfrac=0.3, verbose=True)


Examples of flagging a calibration table

Clip zero data from a bandpass calibration table.

flagdata('cal-X54.B1', mode='clip', clipzeros=True, datacolumn='CPARAM')


Clip data from a cal table with SNR <4.0.

flagdata('cal-X54.B1', mode='clip', clipminmax=[0.0,4.0], clipoutside=False, datacolumn='SNR')


Clip the g values of a switched power caltable created using the gencal task. The g values are usually < 1.0.

flagdata('cal.12A.syspower', mode='clip', clipminmax=[0.1,0.3],
correlation='Sol1,Sol3', datacolumn='FPARAM')


Now, clip the Tsys values of the same table from above. The Tsys solutions have values between 10 – 100s.

flagdata('cal.12A.syspower', mode='clip', clipminmax=[10.0,95.0],
correlation='Sol2,Sol4', datacolumn='FPARAM')

Development

Parameter Details

Detailed descriptions of each function parameter

vis (string) - Name of input visibility file
Default: none
Example: vis=’ngc5921.ms’
mode (string='manual') - Flagging mode
Default: ‘manual’
Options: ‘list’, ‘manual’, ‘clip’, ‘quack’,
‘antint’, ‘extend’, ‘unflag’, ‘summary’
* ‘list’: Flag according to the data selection
and flag commands specified in the input
list. The input list may come from a text file,
a list of text files or from a Python list of
strings. Each input line may contain data
selection parameters and any parameter
specific to the mode given in the line. Default
values will be used for the parameters that are
not present in the line. Each line will be
taken as a command to the task. If data is
pre-selected using any of the selection
parameters, then flagging will apply only to
that subset of the MS.

For optimization and whenever possible, the
task will create a union of the data selection
parameters present in the list and select only
that portion of the MS.
NOTE1: the flag commands will be applied only
when action=’apply’. If action=’calculate’ the
flags will be calculated, but not applied. This
is useful if display is set to something other
than ‘none’. If action=’’ or ‘none’, the flag
commands will not be applied either. An empty
action is useful only to save the parameters of
the list to a file or to the FLAG_CMD
sub-table.
NOTE2: In list mode the parameter
quackincrement=True is not supported as part of
any quack flag command, unless it is the first
quack mode section of this help.
* ‘manual’: Flag according to the data selection
specified. This is the default mode
* ‘clip’: Clip data according to values of the
following subparameters. The polarization
expression is given by the correlation
parameter. For calibration tables, the
solutions are also given by the correlation
parameter.
* ‘quack’: Option to remove specified part of
scan beginning/end.
antennas. This mode is not available for cal
tables.

All antennas in the antenna-subtable of the MS
(and the corresponding diameters) will be
considered for shadow-flag calculations. For a
given timestep, an antenna is flagged if any of
its baselines (projected onto the uv-plane) is
tolerance. The value of ‘w’ is used to
determine which antenna is behind the
other. The phase-reference center is used for
antenna-pointing direction.
* ‘elevation’: Option to flag based on antenna
elevation. This mode is not available for cal
tables.
* ‘tfcrop’: Flag using the TFCrop autoflag
algorithm.

For each field, spw, timerange (specified by
ntime), and baseline,
(1) Average visibility amplitudes along time
dimension to form an average spectrum
(2) Calculate a robust piece-wise polynomial
fit for the band-shape at the base of RFI
spikes. Calculate ‘stddev’ of (data - fit).
(3) Flag points deviating from the fit by more
than N-stddev
(4) Repeat (1-3) along the other dimension.
This algorithm is designed to operate on
un-calibrated data (step (2)), as well as
calibrated data. It is recommended to extend
the flags after running this algorithm. See the
sub-parameter extendflags below.
* ‘rflag’: Detect outliers based on the RFlag
algorithm (ref. E.Greisen, AIPS, 2011). The
polarization expression is given by the
correlation parameter.
Iterate through the data in chunks of time.
For each chunk, calculate local statistics, and
apply flags based on user supplied (or
auto-calculated) thresholds.
Step 1 : Time analysis (for each channel)
– calculate local rms of real and imag
visibilities, within a sliding time window
– calculate the median rms across time
windows, deviations of local rms from this
median, and the median deviation
– flag if local rms is larger than
timedevscale x (medianRMS + medianDev)
Step 2 : Spectral analysis (for each time)
– calculate avg of real and imag
visibilities and their rms across channels
– calculate the deviation of each channel
from this avg, and the median-deviation
– flag if deviation is larger than
freqdevscale x medianDev
It is recommended to extend the flags after
running this algorithm. See the sub-parameter
extendflags below.
Note that by default the flag implementation in
CASA is able to calculate the thresholds and
apply them on-the-fly (OTF). There is a
significant performancegain with this approach,
as the visibilities don’t have to be read
twice,and therefore is highly
recommended. Otherwise it is possible
toreproduce the AIPS usage pattern by doing a
first run with ‘calculate’ mode and a second
run with ‘apply’ mode. The advantage of this
approach is that the thresholdsare calculated
using the data from all scans, instead of
calculating them for one scan only.
see the task pages of rflag in CASA Docs

* ‘antint’: Flag integrations if all baselines to
a specified antenna are flagged
This mode flag all integrations in which a
specified antenna is flagged. This mode
operates for an spectral window. It flags any
integration in which all baselines to a
specified antenna are flagged, but only if this
condition is satisfied in a fraction of
channels within the spectral window of interest
greater than a nominated fraction. For
simplicity, it assumes that all polarization
products must be unflagged for a baseline to be
deemed unflagged. The antint mode implements
the flagging approach introduced in
‘antintflag’
The motivating application for introducing this
mode is removal of data that will otherwise
lead to changes in reference antenna during
gain calibration, which will in turn lead to
corrupted polarization.
* ‘extend’: Extend and/or grow flags beyond what
the basic algorithms detect. This mode will
extend the accumulated flags available in the
MS, regardless of which algorithm created them.
It is recommended that any autoflag (tfcrop,
rflag) algorithm be followed up by a flag
extension.

Extensions will apply only within the selected
data, according to the settings of
extendpols,growtime,growfreq,growaround,
flagneartime,flagnearfreq.

Note : Runtime summary counts in the logger can
sometimes report larger flag percentages than
what is actually flagged. This is because
counted as new flags. An accurate flag count
can be obtained via the summary mode.
* ‘unflag’: Unflag according to the data
selection specified.
* ‘summary’: List the number of rows and flagged
data points for the MS’s meta-data. The
resulting summary will be returned as a Python
dictionary.
autocorr (bool=False) - Flag only the auto-correlations?
Subparameter of mode=’manual’
Default: False
Options: False|True
NOTE: this parameter is only active when set to
True. If set to False it does NOT mean “do not
flag auto-correlations”. When set to True, it
will only flag data from a processor of type
CORRELATOR.
inpfile ({string, stringArray}='') - Input ASCII file, list of files or Python list of strings
with flag commands.
Subparameter of mode=’list’
Default: ‘’
Options: [] with flag commands or
[] with filenames or
‘’ with a filename.
The parser will be strict and accept only valid
flagdata parameters in the list. The parser
evaluates the commands in the list and considers
only existing Python types.It will check each
parameter name and type and exit with an error if
any of them is wrong.
NOTE: There should be no whitespace between
KEY=VALUE since the parser first breaks command
lines on whitespace, then on “=”. Use only one
whitespace to separate the parameters (no
commas).
reason ({string, stringArray}='any') - Select flag commands based on REASON(s)
Subparameter of mode=’list’
Default: ‘any’ (all flags regardless of reason)
Can be a string, or list of strings
Examples:
reason=’FOCUS_ERROR’
reason=[‘FOCUS_ERROR’,’SUBREFLECTOR_ERROR’]
If inpfile is a list of files, the reasons given
in this parameter will apply to all the files.
NOTE: what is within the string is literally
matched, e.g. reason=’’ matches only blank
reasons, and reason =
‘FOCUS_ERROR,SUBREFLECTOR_ERROR’ matches this
compound reason string only.
tbuff ({double, doubleArray}=0.0) - A time buffer or list of time buffers to pad the
timerange parameters in flag commands.
Subparameter of mode=’list’
Default: 0.0 (it will not apply any time padding)
When a list of 2 time buffers is given, it will
subtract the first value from the lower time and
the second value will be added to the upper time
in the range. The 2 time buffer values can be
different, allowing to have an irregular time
buffer padding to time ranges. If the list
contains only one time buffer, it will use it to
subtract from t0 and add to t1. If more than one
list of input files is given, tbuff will apply to
all of the flag commands that have timerange
parameters in the files. Each tbuff value should
be a Float number given in seconds.
Examples:
tbuff=[0.5, 0.8]
inpfile=[‘online.txt’,’userflags.txt’]
The timeranges in the online.txt file are
first converted to seconds. Then, 0.5 is
subtracted from t0 and 0.8 is added to t1,
where t0 and t1 are the two intervals given in
timerange. Similarly, tbuff will be applied to
any timerange in userflags.txt.
IMPORTANT: This parameter assumes that timerange
= t0 ~ t1, therefore it will not work if only t0
or t1 is given.
NOTE: The most common use-case for tbuff is to
apply the online flags that are created by
importasdm when savecmds=True. The value of a
regular time buffer should be
tbuff=0.5*max(integration time).
spw ({string, stringArray}='') - 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.
NOTE: For modes clip, tfcrop and rflag,
channel-ranges can be excluded from flagging by
leaving them out of the selection range. This is
a way to protect known spectral-lines from being
flagged by the autoflag algorithms. For example,
if spectral-lines fall in channels 6~9, set the
selection range to spw=’0:0~5;10~63’.
field ({string, stringArray}='') - Select field using field id(s) or field name(s)
Default: ‘’ (all fields)

Use ‘go listobs’ to obtain the list id’s or
names. If field string is a non-negative integer,
it is assumed a field index, otherwise, it is
assumed a field name.
Examples:
field=’0~2’; field ids 0,1,2
field=’0,4,5~7’; field ids 0,4,5,6,7
field=’3C286,3C295’; field named 3C286 and
3C295
field = ‘3,4C*’; field id 3, all names
starting with 4C
antenna ({string, stringArray}='') - Select data based on antenna/baseline
Subparameter of selectdata=True
Default: ‘’ (all)
If antenna string is a non-negative integer, it
is assumed an antenna index, otherwise, it is
assumed as an antenna name

Examples:
antenna=’5&6’; baseline between antenna
index 5 and index 6.
antenna=’VA05&VA06’; baseline between VLA
antenna 5 and 6.
antenna=’5&6;7&8’; baselines with
indices 5-6 and 7-8
antenna=’5’; all baselines with antenna index
5
antenna=’05’; all baselines with antenna
number 05 (VLA old name)
antenna=’5,6,10’; all baselines with antennas
5,6,10 index numbers
NOTE: for some antenna-based calibration tables,
selecting baselines with the & syntax do not
apply.
uvrange ({string, stringArray}='') - Select data by baseline length.
Default = ‘’ (all)
Examples:
uvrange=’0~1000klambda’; uvrange from 0-1000 kilo-lambda
uvrange=’>4klambda’;uvranges greater than 4 kilo-lambda
uvrange=’0~1000km’; uvrange in kilometers
NOTE: uvrange selection is not supported for cal tables.
timerange ({string, stringArray}='') - Select data based on time range
Subparameter of selectdata=True
Default = ‘’ (all)
Examples:
timerange =
‘YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss’
(Note: if YYYY/MM/DD is missing date defaults
to first day in data set.)
timerange=’09:14:0~09:54:0’ picks 40 min on
first day
timerange= ‘25:00:00~27:30:00’ picks 1 hr to 3
hr 30min on NEXT day
timerange=’09:44:00’ pick data within one
integration of time
timerange=’>10:24:00’ data after this time
correlation ({string, stringArray}='') - Select data based on correlation
Default: ‘’ ==> all
Options: Any of ‘ABS’, ‘ARG’, ‘REAL’, ‘IMAG’,
‘NORM’ followed by any of ‘ALL’, ‘I’, ‘XX’, ‘YY’,
‘RR’, ‘LL’, ‘WVR’ (‘WVR’ = water vapour
Example: correlation=”XX,YY”.
For modes clip, tfcrop or rflag, the default
means ABS_ALL. If the input is cal table that
does not contain a complex data column, the
default will fall back to REAL_ALL.
For calibration tables, the solutions are:
‘Sol1’, ‘Sol2’, Sol3, Sol4.
NOTE: correlation selection is not supported for
modes other than clip, tfcrop or rflag in cal
tables.
scan ({string, stringArray}='') - Scan number range
Subparameter of selectdata=True
Default: ‘’ = all
intent ({string, stringArray}='') - Select observing intent
Default: ‘’ (no selection by intent)
Example: intent=’BANDPASS’ (selects data
labelled with BANDPASS intent)
NOTE: intent selection is not supported for cal
tables.
array ({string, stringArray}='') - Selection based on the antenna array
Default: ‘’ (all)
NOTE: array selection is not supported for cal
tables.
observation ({string, int}='') - Select by observation ID(s)
Subparameter of selectdata=True
Default: ‘’ = all
Example: observation=’0~2,4’
feed ({string, stringArray}='') - Selection based on the feed: Not yet implemented
clipminmax (doubleArray=['']) - Range to use for clipping
Subparameter of mode=’clip’
Default: [] (it will flag only NaN and Infs)
It will always flag the NaN/Inf data, even when a range is specified.
Example: [0.0,1.5]
datacolumn ({string, stringArray}='DATA') - Data column to image (data or observed, corrected)
Subparameter of mode=’clip|tfcrop|rflag’
Default:’corrected’
Options: data, corrected, model, weight, etc.

If ‘corrected’ does not exist, it will use ‘data’
clipoutside ({bool, boolArray}=True) - Clip outside the range?
Subparameter of mode=’clip’
Default: True
Options: True|False
channelavg ({bool, boolArray}=False) - Pre-average data across channels before analyzing
visibilities for flagging
Subparameter of mode=’clip|tfcrop|rflag’
Default: False
Options: False|True
Pre-average data across channels before analyzing
visibilities for flagging. Partially flagged data
is not be included in the average unless all
data contributing to a given output channel is
flagged. If present, WEIGHT_SPECTRUM /
SIGMA_SPECTRUM are used to compute a weighted
average (WEIGHT_SPECTRUM for CORRECTED_DATA and
SIGMA_SPECTRUM for DATA).
NOTE 1: Pre-average across channels is not
supported in list mode.
NOTE 2: Pre-average across channels is not
supported for calibration tables
chanbin ({int, intArray}=1) - Bin width for channel average in number of input channels
Subparameter of mode=’clip|tfcrop|rflag’
Default: 1
Bin width for channel average in number of input
channels. If a list is given, each bin applies to
one of the selected SPWs. When chanbin is set to
1 all input channels are used considered for the
average to produce a single output channel, this
behaviour aims to be preserve backwards
compatibility with the previous pre-averaging
feature of clip mode.
timeavg ({bool, boolArray}=False) - Pre-average data across time before analyzing
visibilities for flagging.
Subparameter of mode=’clip|tfcrop|rflag’
Default: False
Options: False|True
Pre-average data across time before analyzing
visibilities for flagging. Partially flagged data
is not be included in the average unless all
data contributing to a given output channel is
flagged. If present, WEIGHT_SPECTRUM /
SIGMA_SPECTRUM are used to compute a weighted
average (WEIGHT_SPECTRUM for CORRECTED_DATA and
SIGMA_SPECTRUM for DATA). Otherwise WEIGHT/SIGMA
are used to average together data from different
integrations.
NOTE 1: Pre-average across time is not
supported in list mode.
NOTE 2: Pre-average across time is not
supported for calibration tables
timebin (string='0s') - Bin width for time average in seconds
Subparameter of mode=’clip|tfcrop|rflag’
Default: ‘0s’
clipzeros (bool=False) - Clip zero-value data
Subparameter of mode=’clip’
Default: False
Options: False|True
quackinterval ({double, doubleArray, int, intArray}=1.0) - Time in seconds from scan beginning or end to flag.
Subparameter of mode=’quack’
Default: 0.0
Note: Make time slightly smaller than the desired
time.
quackmode ({string, stringArray}='beg') - Quack mode flags the region of the scan given by one of the
options below using the time set at quackinterval.
Subparameter of mode=’quack’
Default: ‘beg’
Options:
‘beg’ : flag an interval at the beginning of scan
‘endb’: flag an interval at the end of scan
‘tail’: flag all but an interval at the beginning of scan
‘end’ : flag all but an interval at end of scan
Visual representation of quack mode flagging one
scan with 1s duration. The following diagram
shows what is flagged for each quack mode when
quackinterval is set to 0.25s. The flagged part
is represented by crosses (+++++++++)
scan with 1s duration
——————————————–
beg
+++++++++++———————————
endb
———————————+++++++++++
tail
———–+++++++++++++++++++++++++++++++++
end
+++++++++++++++++++++++++++++++++———–
quackincrement ({bool, boolArray}=False) - Increment quack flagging in time taking into account
flagged data or not.
Subparameter of mode=’quack’
Default: False
Options: False|True
False: the quack interval is counted from the
scan boundaries, as determined by the quackmode
parameter, regardless of if data has been flagged
or not.
True: the quack interval is counted from the
first unflagged data in the scan.
NOTE: on adding quack to a command in ‘list’
mode: quackincrement = True works based on the
state of prior flagging, and unless it is the
first item in the list the agent doing the
quacking in list mode doesn’t know about the
state of prior flags. In this case, the command
with quackincrement=True will be ignored and the
tolerance (double=0.0) - Amount of shadowing allowed (or tolerated), in meters.
Default: 0.0
A positive number allows antennas to overlap in
projection. A negative number forces antennas
apart in projection. Zero implies a distance of
addantenna ({string, record}='') - File name or dictionary with additional antenna names,
positions and diameters
Default: ‘’
It can be either a file name with additional
antenna names, positions and diameters, or a
Python dictionary with the same information. You
can use the flaghelper functions to create the
dictionary from a file.
To create a dictionary inside casapy.
> import flaghelper as fh

Where antfile is a text file in disk that
contains information such as:
name=VLA01
diameter=25.0
position=[-1601144.96146691, -5041998.01971858,
3554864.76811967]
name=VLA02
diameter=25.0
position=[-1601105.7664601889,
-5042022.3917835914, 3554847.245159178]
lowerlimit (double=0.0) - Lower limiting elevation (in degrees)
Subparameter of mode=’elevation’
Default: 0.0
Lower limiting elevation in degrees. Data coming
from a baseline where one or both antennas were
pointing at a strictly lower elevation (as
function of time), will be flagged.
upperlimit (double=90.0) - Upper limiting elevation (in degrees)
Subparameter of mode=’elevation’
Default: 90.0
Upper limiting elevation in degrees. Data coming
from a baseline where one or both antennas were
pointing at a strictly higher elevation (as
function of time), will be flagged.
ntime ({double, string}='scan') - Timerange (in seconds or minutes) over which to buffer
data before running the algorithm.
Subparameter of mode=’tfcrop|rflag|extend’
Default: ‘scan’
Options: ‘scan’ or any other float value or
string containing the units.
The dataset will be iterated through in
time-chunks defined here.
Example: ntime=’1.5min’; 1.2 (taken in
seconds)
WARNING: if ntime=’scan’ and combinescans=True,
all the scans will be loaded at once, thus
requesting a lot of memory depending on the
available spws.
combinescans (bool=False) - Accumulate data across scans depending on the value of
ntime.
Subparameter of mode=’tfcrop|rflag|extend’
Default: False
Options: False|True
This parameter should be set to True only when
ntime is specified as a time-interval (not
‘scan’). When set to True, it will remove SCAN
from the sorting columns, therefore it will only
accumulate across scans if ntime is not set to
‘scan’.
timecutoff (double=4.0) - Flagging thresholds in units of deviation from the fit
Subparameter of mode=’tfcrop’
Default: 4.0
Flag all data-points further than N-stddev from
the fit. This threshold catches time-varying RFI
spikes (narrow and broad-band), but will not
catch RFI that is persistent in time.
Flagging is done in upto 5 iterations. The stddev
calculation is adaptive and converges to a value
that reflects only the data and no RFI. At each
iteration, the same relative threshold is applied
to detect flags. (Step (3) of the algorithm).
freqcutoff (double=3.0) - Flag threshold in frequency.
Subparameter of mode=’tfcrop’
Default: 3.0
Flag all data-points further than N-stddev from
the fit. Same as timecutoff, but along the
frequency-dimension. This threshold catches
narrow-band RFI that may or may not be persistent
in time.
timefit (string='line') - Fitting function for the time direction (poly/line)
Subparameter of mode=’tfcrop’
Default: ‘line’
Options: line|poly
‘line’: fit is a robust straight-line fit across
the entire timerange (defined by ‘ntime’).
‘poly’: fit is a robust piece-wise polynomial fit
across the timerange.
NOTE: A robust fit is computed in upto 5
iterations. At each iteration, the stddev between
the data and the fit is computed, values beyond
N-stddev are flagged, and the fit and stddev are
re-calculated with the remaining points. This
stddev calculation is adaptive, and converges to
a value that reflects only the data and no RFI.
It also provides a varying set of flagging
thresholds, that allows deep flagging only when
the fit best represents the true data.
Choose ‘poly’ only if the visibilities are
expected to vary significantly over the timerange
selected by ‘ntime’, or if there is a lot of
strong but intermittent RFI.
freqfit (string='poly') - Fitting function for the frequency direction (poly/line)
Subparameter of mode=’tfcrop’
Default: ‘poly’
Options: line|poly
Same as for the ‘timefit’ parameter.
Choose ‘line’ only if you are operating on
bandpass-corrected data, or residuals,and expect
that the bandshape is linear. The ‘poly’ option
works better on uncalibrated bandpasses with
narrow-band RFI spikes.
maxnpieces (int=7) - Number of pieces in the polynomial-fits (for “freqfit” or
“timefit” = “poly”)
Subparameter of mode=’tfcrop’
Default: 7
Options: 1-9
This parameter is used only if ‘timefit’ or
‘freqfit’ are chosen as ‘poly’. If there is
number. Using too many pieces could result in the
RFI being fitted in the ‘clean’ bandpass. In
later stages of the fit, a third-order polynomial
is fit per piece, so for best results, please
ensure that nchan/maxnpieces is at-least 10.
flagdimension (string='freqtime') - Choose the directions along which to perform flagging
Subparameter of mode=’tfcrop’
Default: ‘freqtime’ (first flag along frequency,
and then along time)
Options: ‘time’, ‘freq’, ‘timefreq’, ‘freqtime’
For most cases, ‘freqtime’ or ‘timefreq’ are
appropriate, and differences between these
choices are apparant only if RFI in one dimension
is significantly stronger than the other. The
goal is to flag the dominant RFI first.
If there are very few (less than 5) channels of
data, then choose ‘time’. Similarly for ‘freq’.
usewindowstats (string='none') - Use sliding-window statistics to find additional flags.
Subparameter of mode=’tfcrop’
Default: ‘none’
Options: ‘none’, ‘sum’, ‘std’, ‘both’
NOTE: This is experimental!
The ‘sum’ option chooses to flag a point, if the
mean-value in a window centered on that point
deviates from the fit by more than N-stddev/2.0.
NOTE: stddev is calculated between the data and
fit as explained in Step (2). This option is an
attempt to catch broad-band or time-persistent
RFI that the above polynomial fits will
mistakenly fit as the clean band. It is an
approximation to the sumThreshold method found to
be effective by Offringa et.al (2010) for LOFAR
data. The ‘std’ option chooses to flag a point,
if the ‘local’ stddev calculated in a window
centered on that point is larger than
N-stddev/2.0. This option is an attempt to catch
noisy RFI that is not excluded in the polynomial
fits, and which increases the global stddev, and
results in fewer flags (based on the N-stddev
threshold).
halfwin (int=1) - Half-width of sliding window to use with “usewindowstats”
(1,2,3).
Subparameter of mode=’tfcrop’
Default: 1 (a 3-point window size)
Options: 1, 2, 3
NOTE: This is experimental!
extendflags (bool=True) - Extend flags along time, frequency and correlation.
Subparameter of mode=’tfcrop|rflag’
Default: True
Options: True|False
NOTE: It is usually helpful to extend the flags
along time, frequency, and correlation using this
parameter, which will run the “extend” mode after
“tfcrop” and extend the flags if more than 50% of
the timeranges are already flagged, and if more
than 80% of the channels are already flagged. It
will also extend the flags to the other
polarizations. The user may also set extendflags
to False and run the “extend” mode in a second
step within the same flagging run:
Example: cmd=[“mode=’tfcrop’ freqcutoff=3.0
usewindowstats=’sum’ extendflags=False”,
“mode=’extend’ extendpols=True growtime=50.0
growaround=True”]
flagdata(vis, mode=’list’, inpfile=cmd)
winsize (int=3) - Number of timesteps in the sliding time window ( fparm(1)
in AIPS )
Subparameter of mode=’rflag’
Default: 3
timedev (variant='') - Time-series noise estimate ( noise in AIPS )
Subparameter of mode=’rflag’
Default: []
Examples:
timedev = 0.5 : Use this noise-estimate to
calculate flags. Do not recalculate.
timedev = [ [1,9,0.2], [1,10,0.5] ] : Use
noise-estimate of 0.2 for field 1, spw 9, and
noise-estimate of 0.5 for field 1, spw 10.
timedev = [] : Auto-calculate noise
estimates.
‘tdevfile.txt’ : Auto-calculate noise
estimates and write them into a file with the
name given (any string will be interpreted as
a file name which will be checked for
existence).
freqdev (variant='') - Spectral noise estimate ( scutoff in AIPS )
Subparameter of mode=’rflag’
Default: []
This step depends on having a relatively-flat
bandshape. Same parameter-options as ‘timedev’.
timedevscale (double=5.0) - Threshold scaling for timedev( fparm(9) in AIPS ).
For Step 1 (time analysis), flag a point if local
rms around it is larger than ‘timedevscale’ x
‘timedev’ (fparm(0) in AIPS)
Subparameter of mode=’rflag’
Default: 5.0
This scale parameter is only applied when
flagging (action=’apply’) and displaying reports
(display option). It is not used when the
thresholds are simply calculated and saved into
files (action=’calculate’, as in the two-passes
usage pattern of AIPS)
freqdevscale (double=5.0) - Threshold scaling for freqdev (fparm(10) in AIPS ).
For Step 2 (spectral analysis), flag a point if
local rms around it is larger than ‘freqdevscale’
x ‘freqdev’ (fparm(10) in AIPS)
Subparameter of mode=’rflag’
Default: 5.0
Similarly as with timedevscale, freqdevscale is
used when applying flags and displaying
reports. It is not used when the thresholds are
simply calculated and saved into files
(action=’calculate’, as in the two-passes usage
pattern of AIPS)
spectralmax (double=1E6) - Flag whole spectrum if ‘freqdev’ is greater than
spectralmax ( fparm(6) in AIPS )
Subparameter of mode=’rflag’
Default: 1E6
spectralmin (double=0.0) - Flag whole spectrum if ‘freqdev’ is less than spectralmin
( fparm(5) in AIPS )
Subparameter of mode=’rflag’
Default: 0.0
antint_ref_antenna (string='') - Antenna of interest. Baselines with this antenna will be
checked for flagged channels.
Subparameter of mode=’antint’
Note that this is not the same as the general
‘antenna’ parameter of flagdata. The parameter
antint_ref_antenna is mandatory with the ‘antint’
mode and chooses the antenna for which the
fraction of channels flagged will be checked.
minchanfrac (double=0.6) - Minimum fraction of flagged channels required for a
baseline to be deemed as flagged
Subparameter of mode=’antint’
Takes values between 0-1 (float).
In this mode flagdata does the following for
every point in time. It checks the fraction of
channels flagged for any of the polarization
products and for every baseline to the antenna of
interest. If the fraction is higher than this
‘minchanfrac’ threshold then the data are flagged
for this pont in time (this includes all the rows
selected with the flagdata command that have that
timestamp).
This parameter will be ignored if spw specifies a
channel.
verbose (bool=False) - Print timestamps of flagged integrations to the log
Subparameter of mode=’antint’
Examples:
flagdata(vis, …, spw=’9’,
antint_ref_antenna=’ea01’)
flagdata(vis, …, spw=’9’,
antint_ref_antenna=’ea01’, minchanfrac=0.3,
verbose=True) ==> reduce the fraction of
channels that are required to be flagged, and
print information for every integration that
is flagged.
extendpols (bool=True) - Extend flags to all selected correlations
Subparameter of mode=’extend’
Default: True
Options: True|False
For example, to extend flags from RR to only
RL and LR, a data-selection of
correlation=’RR,LR,RL’ is required along with
extendpols=True.
growtime (double=50.0) - For any channel, flag the entire timerange in the current
2D chunk (set by ‘ntime’) if more than X% of the timerange is already
flagged.
Subparameter of mode=’extend’
Default: 50.0
Options: 0.0 - 100.0
This option catches the low-intensity parts of
time-persistent RFI.
growfreq (double=50.0) - For any timestep, flag all channels in the current 2D
chunk (set by data-selection) if more than X% of the channels are
Subparameter of mode=’extend’
Default: 50.0
Options: 0.0 - 100.0
This option catches broad-band RFI that is
partially identified by earlier steps.
growaround (bool=False) - Flag a point based on the number of flagged points around it.
Subparameter of mode=’extend’
Default: False
Options: False|True
For every un-flagged point on the 2D time/freq
plane, if more than four surrounding points are
already flagged, flag that point. This option
catches some wings of strong RFI spikes.
flagneartime (bool=False) - Flag points before and after every flagged one, in the
time-direction.
Subparameter of mode=’extend’
Default: False
Options: False|True
NOTE: This can result in excessive flagging.
flagnearfreq (bool=False) - Flag points before and after every flagged one, in the
frequency-direction
Subparameter of mode=’extend’
Default: False
Options: False|True
NOTE: This can result in excessive flagging
minrel (double=0.0) - Minimum number of flags (relative) to include in
histogram
Subparameter of mode=’summary’
Default: 0.0
maxrel (double=1.0) - Maximum number of flags (relative) to include in
histogram
Subparameter of mode=’summary’
Default: 1.0
minabs (int=0) - Minimum number of flags (absolute, inclusive) to include
in histogram
Subparameter of mode=’summary’
Default: 0
maxabs (int=-1) - Maximum number of flags (absolute, inclusive) to include
in histogram
Subparameter of mode=’summary’
Default: -1
To indicate infinity, use any negative number.
spwchan (bool=False) - List the number of flags per spw and per channel.
Subparameter of mode=’summary’
Default: False
Options: False|True
spwcorr (bool=False) - List the number of flags per spw and per correlation.
Subparameter of mode=’summary’
Default: False
Options: False|True
basecnt (bool=False) - List the number of flags per baseline
Subparameter of mode=’summary’
Default: False
Options: False|True
fieldcnt (bool=False) - Produce a separated breakdown per field
Subparameter of mode=’summary’
Default: False
Options: False|True
name (string='Summary') - Name for this summary, to be used as a key in the
returned Python dictionary
Subparameter of mode=’summary’
Default: ‘Summary’
It is possible to call the summary mode multiple
times in list mode. When calling the summary mode
as a command in a list, one can give different
names to each one of them so that they can be
easily pulled out of the summary’s dictionary.
In summary mode, the task returns a dictionary of flagging statistics.

Example 1:
s = flagdata(…, mode=’summary’)
Then s will be a dictionary which contains
s[‘total’] : total number of data
s[‘flagged’] : amount of flagged data
Example 2:
Two summary commands in list mode, intercalating a
manual flagging command.
s = flagdata(…, mode=’list’,
inpfile=[“mode=’summary’ name=’InitFlags’”,
“mode=’manual’ autocorr=True”, “mode=’summary’
name=’Autocorr’”])
The dictionary returned in ‘s’ will contain two
dictionaries, one for each of the two summary
modes.
s[‘report0’][‘name’] : ‘InitFlags’
s[‘report1’][‘name’] : ‘Autocorr’
action (string='apply') - Action to perform in MS/cal table or in the input list of
parameters.
Default: ‘apply’
Options: ‘none’, ‘apply’,’calculate’
* ‘apply’: Apply the flags to the MS.
* ‘calculate’: Only calculate the flags but do
not write them to the MS. This is useful if
used together with the display to analyse the
results before writing to the MS.
* ‘’: When set to empty, the underlying tool will
not be executed and no flags will be
produced. No data selection will be done
either. This is useful when used together with
the parameter savepars to only save the current
parameters (or list of parameters) to the
FLAG_CMD sub-table or to an external file.
display (string='') - Display data and/or end-of-MS reports at runtime.
Subparameter of action=’apply|calculate’
Default: ‘none’
Options: ‘none’, ‘data’, ‘report’, ‘both’
* ‘none’: will not display anything.
* ‘data’: display data and flags per-chunk at
run-time, within an interactive GUI.
This option opens a GUI to show the 2D
time-freq planes of the data with old and new
flags, for all correlations per baseline.
– The GUI allows stepping through all
baselines (prev/next) in the current chunk (set
by ‘ntime’), and stepping to the next-chunk.
– The ‘flagdata’ task can be quit from the
GUI, in case it becomes obvious that the
current set of parameters is just wrong.
– There is an option to stop the display but
continue flagging.
* ‘report’: displays end-of-MS reports on the
screen.
* ‘both’: displays data per chunk and end-of-MS
reports on the screen
flagbackup (bool=True) - Automatically backup flags before the run?
Default: True
Options: True|False
Flagversion names are chosen automatically, and
are based on the mode being used.
savepars (bool=False) - Save the current parameters to the FLAG_CMD table of the
MS or to an output text file?
Default: False
Options: False|True
Note that when display is set to anything other
than ‘none’, savepars will be disabled. This is
done because in an interactive mode, the user may
skip data which may invalidate the initial input
parameters and there is no way to save the
interactive commands. When the input is a
calibration table it is only possible to save the
parameters to a file.
cmdreason (string='') - A string containing a reason to save to the FLAG_CMD
table or to an output text file given by the outfile sub-parameter.
Subparameter of savepars=True
Default: ‘’ (no reason will be added to output)
If the input contains any reason, they will be
replaced with this one. At the moment it is not
possible to add more than one reason.
Example: cmdreason=’CLIP_ZEROS’
outfile (string='') - Name of output file to save current parameters. If empty,
save to FLAG_CMD
Subparameter of savepars=True
Default: ‘’ (save the parameters to the FLAG_CMD
table of the MS)
Example: outfile=’flags.txt’ will save the
parameters in a text file.
overwrite (bool=True) - Overwrite the existing file given in ‘outfile’
Default: True
Options: True|False
The default True will remove the existing file
given in ‘outfile’ and save the current flag
commands to a new file with the same name. When
set to False, the task will exit with an error
message if the file exist.
writeflags (bool=True) - Internal hidden parameter. Do not modify.