All-purpose flagging task based on data-selections and flagging modes/algorithms.
This task can flag a Measurement Set 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 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.
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.
-----------------------------------
When the input is a calibration table, the modes 'elevation' and 'shadow'
will be disabled. Data selection for calibration tables is limited to field,
antenna and spw at the moment. It is only possible to save the parameters
to an external file.
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
shadow = remove antenna-shadowed data
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
extend = extend and/or grow flags beyond what the basic algorithms detect
summary = report the amount of flagged data
unflag = unflag the specified data
Keyword arguments:
vis -- Name of input visibility file or calibration table.
default: '' (none)
example1: vis='uid___A002_X2a5c2f_X54.ms' or
example2: vis='cal-X54.B1'
Any flagging will only be applied to the specified selection.
antenna -- Select data based on baseline
default: '' (all); example: 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
spw -- Select data based on spectral window and channels
default: '' (all); example: spw='1'
spw='<2' #spectral windows less than 2
spw='>1' #spectral windows greater than 1
spw='1:0~10' # first 10 channels from spw 1
spw='1:0~5;120~128' # multiple separated channel chunks.
Note : For modes clip, tfcrop and rflag, channel-ranges can be excluded
from flagging by selecting ranges such as spw='0:0~5;10~63'. This is a way to
protect known spectral-lines from being flagged by the autoflag algorithms.
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.
example: correlation='XX,YY' or
options: Any of 'ABS', 'ARG', 'REAL', 'IMAG', 'NORM' followed by
any of 'ALL', 'I', 'XX', 'YY', 'RR', 'LL', 'Sol1', 'Sol2', 'WVR'
'WVR' refers to the water vapour radiometer of ALMA data.
example: correlation='REAL_XX,XY'
-->correlation selection is not supported for modes other than clip, tfcrop or
rflag in cal tables.
Note that 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.
field -- Select data based on field id(s) or name(s)
default: '' (all); example: field='1'
field='0~2' # field ids inclusive from 0 to 2
field='3C*' # all field names starting with 3C
uvrange -- Select data within uvrange (default units meters)
default: '' (all); example:
uvrange='0~1000klambda'; uvrange from 0-1000 kilo-lambda
uvrange='>4klamda';uvranges greater than 4 kilo-lambda
uvrange='0~1000km'; uvrange in kilometers
-->uvrange selection is not supported for cal tables.
timerange -- Select data based on time range:
default = '' (all); example,
timerange = 'YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss'
Note: YYYY/MM/DD can be dropped as needed:
timerange='09:14:0~09:54:0' # this time range
timerange='09:44:00' # data within one integration of time
timerange='>10:24:00' # data after this time
timerange='09:44:00+00:13:00' #data 13 minutes after time
-->timerange selection is not supported for cal tables.
scan -- Select data based on scan number
default: '' (all); example: scan='>3'
-->scan selection is not supported for cal tables.
intent -- Select data based on observation intent
default: '' (all); example: intent='*CAL*,*BAND*'
-->intent selection is not supported for cal tables.
array -- Selection based on the antenna array
default: '' (all);
-->array selection is not supported for cal tables.
observation -- Selection based on the observation ID
default: '' (all); example: observation='1' or observation=1
-->observation selection is not supported for cal tables.
feed -- Selection based on the feed - NOT IMPLEMENTED YET
mode -- Mode of operation.
options: 'list', 'manual','clip','quack','shadow','elevation', 'tfcrop', 'extend',
'unflag', 'summary'
default: 'manual'
list -- Flag according to the data selection and flag commands specified in the input list.
The input list may come from a text file 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.
The summary mode is not allowed in the input list.
For optimization, 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.
inpfile -- Input ASCII file or a Python list of command strings.
default: ''
options: [] or '' with filename.
Example1: the following commands can be saved into a file 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')
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)
default: 'any' (all flags regardless of reason)
can be a string, or list of strings
example: reason='FOCUS_ERROR'
reason=['FOCUS_ERROR','SUBREFLECTOR_ERROR']
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
See the syntax for writing flag commands at the end of this help.
manual -- 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".
default: False
options: True,False
clip -- Clip data according to values of the following subparameters. The polarization
expression is given by the correlation parameter.
channelavg -- Average data over (selected) channels? Channel
selections are taken into account. Flagged channels
are excluded from the average. The average is done
after applying the specified clipexpr, i.e. scalar
averaging.
default: False
options: True,False
clipminmax -- Range of data (Jy) that will NOT be flagged
default: []; it will flag only NaN and Infs.
example: [0.0,1.5]
It will always flag the NaN/Inf data, even when a range is specified.
datacolumn -- Column to use for clipping.
default: 'DATA'
options: 'DATA', 'CORRECTED','MODEL', 'RESIDUAL, 'RESIDUAL_DATA',
'FPARAM', 'CPARAM', 'SNR'.
NOTE: by default for calibration tables, the framework will try to use FPARAM;
if that does not exist, it will try to use CPARAM. If none of them exist, it
will give an error and exit.
clipoutside -- Clip OUTSIDE the range?
default: True
example: False; flag data WITHIN the range.
clipzeros -- Clip zero-value data.
default: False
quack -- Option to remove specified part of scan beginning/end
quackinterval -- Time in seconds from scan beginning/end to flag. Make time slightly
smaller than the desired time.
default: 0.0
quackmode -- Quack mode
default: 'beg'
options: 'beg' ==> beginning of scan
'endb' ==> end of scan.
'tail' ==> all but beginning of scan
'end' ==> all but end of scan.
quackincrement -- Quack incrementally in time?
default: False
False ==> the quack interval is counted from the
beginning of the scan
True ==> the quack interval is counted from the
first unflagged data in the scan
shadow -- 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
addantenna -- 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: ''
To create a dictionary inside casapy.
> import flaghelper as fh
> antdic = fh.readAntennaList(antfile)
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]
elevation -- 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
tfcrop -- Flag using the TFCrop autoflag algorithm. The polarization
expression is given by the correlation parameter.
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 usually helpful to extend the flags along
time, frequency, and correlation in a second step (via mode='extend)
ntime -- Timerange (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'
example: '1.5min'
: 1.2 (taken in seconds)
The dataset will be iterated through in time-chunks defined here.
combinescans -- accumulate data across scans.
default: False
This parameter is relevant only ntime is specified as a time-interval (not 'scan')
datacolumn -- Column to use for flagging.
default: 'DATA'
options: 'DATA', 'CORRECTED','MODEL', 'RESIDUAL, 'RESIDUAL_DATA',
'FPARAM', 'CPARAM', 'SNR'.
NOTE: by default for calibration tables, the framework will try to use FPARAM;
if that does not exist, it will try to use CPARAM. If none of them exist, it
will give an error and exit.
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 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 -- Flag threshold in frequency. Flag all data-points further than N-stddev
from the fit.
default: 3.0
Same as timecutoff, but along the frequency-dimension. This threshold catches
narrow-band RFI that may or may not be persistent in time.
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
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 -- 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'
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 -- Half width of sliding window to use with 'usewindowstats'
default: 1 (a 3-point window size)
options: 1,2,3
Note: This is experimental !
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
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 an 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 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').
Note : The rflag algorithm was originally developed by Eric Greisen in
AIPS (31DEC11).
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 )
Note : 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.
ntime -- Timerange (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'
example: '1.5min'
: 1.2 (taken in seconds)
The dataset will be iterated through in time-chunks defined here.
combinescans -- accumulate data across scans.
default: False
It only makes sense when ntime not equal 'scan'.
datacolumn -- Column to use for flagging.
default: 'DATA'
options: 'DATA', 'CORRECTED','MODEL', 'RESIDUAL, 'RESIDUAL_DATA',
'FPARAM', 'CPARAM', 'SNR'.
NOTE: by default for calibration tables, the framework will try to use FPARAM;
if that does not exist, it will try to use CPARAM. If none of them exist, it
will give an error and exit.
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.
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 )
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 )
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
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.
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
extensions onto already-flagged data-points are counted as new flags.
An accurate flag count can be obtained via the summary mode.
ntime -- Timerange (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'
example: '1.5min'
: 1.2 (taken in seconds)
The dataset will be iterated through in time-chunks defined here.
combinescans -- accumulate data across scans.
default: False
It only makes sense when ntime not equal 'scan'.
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: 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: This can result in excessive flagging.
unflag -- Unflag according to the data selection specified.
summary -- List the number of rows and data points flagged.
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 -- list the number of flags per spw and per correlation.
default: False
basecnt -- list the number of flags per baseline
default: False
In summary mode, the task returns a dictionary of flagging statistics.
Example:
s = flagdata(..., mode='summary')
Then s will be a dictionary which contains
s['total'] : total number of data
s['flagged'] : amount of flagged data
action -- Action to perform in MS/cal table or in the input list of parameters.
options: 'none', 'apply','calculate'
default: 'apply'
'apply' -- Apply the flags to the MS.
display -- Display data and/or end-of-MS reports at run-time.
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
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
'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.
display -- Display data and/or end-of-MS reports at run-time. See extended description
above.
default: 'none'
options: 'none', 'data', 'report', 'both'
' ' -- 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.
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.
default: False
options: True/False
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.
example: cmdreason='CLIP_ZEROS'
outfile -- Name of output file to save the current parameters.
default: ' '; it will save the parameters to the FLAG_CMD table of the MS.
example: outfile='flags.txt' will save the parameters in a text file.
---- EXAMPLES ----
NOTE: The vector mode of the previous flagdata task can be achieved with this task by using it
with mode='list' and the commands given in a list in inpmode=[]. Example:
flagdata('my.ms', inpmode='list', inpfile=["mode='clip' clipzeros=True","mode='shadow'])
1) 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)
2) 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')
3a) 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)
3b) 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')
4) 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')
5) Flag all the antennas except antenna=5.
flagdata(vis='my.ms', antenna='!5', mode='manual)
6) Clip the NaN in the data. An empty clipminmax will flag only NaN.
flagdata('my.ms', mode='clip')
7) Clip only the water vapour radiometer data.
flagdata('my.ms',mode='clip',clipminmax=[0,50], correlation='ABS_WVR')
8) Clip only zero-value data.
flagdata('my.ms',mode='clip',clipzeros=True)
9a) Flag only auto-correlations using the autocorr parameter.
flagdata('my.ms', autocorr=True)
9b) Flag only auto-correlations using the antenna selection.
flagdata('my.ms', mode='manual', antenna='*&&&')
10a) Flag based on selected reasons from a file.
> 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')
10b) The same result of 10a can be achieved using the task flagcmd.
flagcmd('my.ms', inpmode='file', inpfile='flags.txt', action='apply', reason='MYREASON')
11) 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')
12) 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')
13) 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]
flagdata('my.vis', mode='shadow', tolerance=10.0, addantenna='ant.txt')
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
> antdic = fh.readAntennaList(antfile)
flagdata('my.vis', mode='shadow', tolerance=10.0, addantenna=antdic)
14) 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')
15) Clip zero data from a calibration table.
flagdata('cal-X54.B1', mode='clip', clipzeros=True, datacolumn='CPARAM')
16) Clip data with SNR <4.0.
flagdata('cal-X54.B1', mode='clip', clipminmax=[0.0,4.0], clipoutside=False, datacolumn='SNR')
---- SYNTAX FOR COMMANDS GIVEN IN A FILE or LIST OF STRINGS ----
Basic Syntax Rules
Commands are strings (which may contain internal "strings") consisting of
KEY=VALUE pairs separated by whitespace.
NOTE: There should be no whitespace between KEY=VALUE or within each KEY or
VALUE, since the simple parser first breaks command lines on whitespace,
then on "=".
Use only white spaces to separate the parameters (no commas).
Each key should only appear once on a given command line/string.
There is an implicit "mode" for each command, with the default
being 'manual' if not given.
Comment lines can start with '#' and will be ignored.
Example:
scan='1~3' mode='manual'
# this line will 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
1.8.0