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casa
$Rev:20696$
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casa
$Rev:20696$
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Public Member Functions | |
| def | __init__ |
| def | __call__ |
Private Attributes | |
| __bases__ | |
| __doc__ | |
Static Private Attributes | |
| string | __name__ |
Definition at line 18 of file statwt_pg.py.
| def statwt_pg.statwt_pg_.__init__ | ( | self | ) |
Definition at line 21 of file statwt_pg.py.
| def statwt_pg.statwt_pg_.__call__ | ( | self, | |
vis = None, |
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dorms = None, |
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byantenna = None, |
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sepacs = None, |
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fitspw = None, |
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fitcorr = None, |
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combine = None, |
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timebin = None, |
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minsamp = None, |
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field = None, |
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spw = None, |
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antenna = None, |
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timerange = None, |
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scan = None, |
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intent = None, |
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array = None, |
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correlation = None, |
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observation = None, |
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datacolumn = None, |
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async = None |
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| ) |
Reweight visibilities according to their scatter (Experimental)
The WEIGHT and SIGMA columns of measurement sets are often set to arbitrary
values (e.g. 1), or theoretically estimated from poorly known antenna and
receiver properties. Many tasks (e.g. clean) are insensitive to an overall
scale error in WEIGHT, but are affected by errors in the relative weights
between visibilities. Other tasks, such as uvmodelfit, or anything which
depends on theoretical estimates of the noise, require (reasonably) correct
weights and sigmas. statwt empirically measures the visibility scatter
(typically as a function of time, antenna, and/or baseline) and uses that
to set WEIGHT and SIGMA. It is important that all necessary calibrations
are applied to the data prior to running this task for correct determination of
weights and sigmas.
Note: Some of the parameters (byantenna, sepacs, fitcorr, and timebin)
are not fully implemeted for CASA 3.4.
Keyword arguments:
vis -- Name of the measurement set.
default: none; example: vis='ngc5921.ms'
dorms -- Estimate the scatter using rms instead of the standard
deviation?
Ideally the visibilities used to estimate the scatter, as
selected by fitspw and fitcorr, should be pure noise. If you
know for certain that they are, then setting dorms to True
will give the best result. Otherwise, use False (standard
sample standard deviation). More robust scatter estimates
like the interquartile range or median absolute deviation from
the median are not offered because they require sorting by
value, which is not possible for complex numbers.
default: False
byantenna -- Assume that the noise is factorable by antenna (feed).
If false, treat it seperately for each baseline
(recommended if there is strong signal).
default: False (*** byantenna=True is not yet implemented)
sepacs -- If solving by antenna, treat autocorrelations separately.
(Acknowledge that what autocorrelations "see" is very
different from what crosscorrelations see.)
default: True (*** not yet implemented)
--- Data Selection (see help par.selectdata for more detailed
information)
fitspw -- The (ideally) signal-free spectral window:channels to
estimate the scatter from.
default: '' (All)
fitcorr -- The (ideally) signal-free correlations to
estimate the scatter from.
default: '' (All)
*** not yet implemented
combine -- Let samples span multiple spws, corrs, scans, and/or states.
combine = 'spw': Recommended when a line spans an entire spw
- set fitspw to the neighboring spws and
apply their weight to the line spw(s).
However, the effect of the line signal per
visibility may be relatively harmless
compared to the noise difference between
spws.
combine = 'scan': Can be useful when the scan number
goes up with each integration,
as in many WSRT MSes.
combine = ['scan', 'spw']: disregard scan and spw
numbers when gathering samples.
combine = 'spw,scan': Same as above.
default: '' (None)
timebin -- Sample interval.
default: '0s' or '-1s' (1 integration at a time)
example: timebin='30s'
'10' means '10s'
*** not yet implemented
minsamp -- Minimum number of unflagged visibilities for estimating the
scatter. Selected visibilities for which the weight cannot
be estimated will be flagged. Note that minsamp is
effectively at least 2 if dorms is False, and 1 if it is
True.
field -- Select field using field id(s) or field name(s).
[run 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
field='0~2'; field ids 0,1,2
field='0,4,5~7'; field ids 0,4,5,6,7
field='3C286,3C295'; fields named 3C286 and 3C295
field = '3,4C*'; field id 3, all names starting with 4C
spw -- Select spectral window/channels for changing WEIGHT and SIGMA.
default: ''=all spectral windows and channels
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.
statwt does not support multiple channel ranges per spectral
window (';') because it is not clear whether to keep the ranges
in the original spectral window or make a new spectral window
for each additional range.
antenna -- Select antennas/baselines for changing WEIGHT and SIGMA.
default: '' (all)
Non-negative integers are assumed to be antenna indices, and
anything else is taken 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 5-6 and 7-8
antenna='5': all baselines with antenna 5
antenna='5,6,10': all baselines including antennas 5, 6, or 10
antenna='5,6,10&': all baselines with *only* antennas 5, 6, or
10. (cross-correlations only. Use &&
to include autocorrelations, and &&&
to get only autocorrelations.)
antenna='!ea03,ea12,ea17': all baselines except those that
include EVLA antennas ea03, ea12, or
ea17.
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, timerange defaults to the
first day in the dataset
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' data within one integration of time
timerange='>10:24:00' data after this time
scan -- Scan number range
default: ''=all
intent -- Select by scan intent (state). Case sensitive.
default: '' = all
Examples:
intent = 'CALIBRATE_ATMOSPHERE_REFERENCE'
intent = 'calibrate_atmosphere_reference'.upper() # same as above
# Select states that include one or both of CALIBRATE_WVR.REFERENCE
# or OBSERVE_TARGET_ON_SOURCE.
intent = 'CALIBRATE_WVR.REFERENCE, OBSERVE_TARGET_ON_SOURCE'
array -- (Sub)array number range
default: ''=all
correlation -- Select correlations, e.g. 'rr, ll' or ['XY', 'YX'].
default '' (all).
observation -- Select by observation ID(s).
default: '' = all
datacolumn -- Which data column to calculate the scatter from
default='corrected'; example: datacolumn='data'
Options: 'data', 'corrected', 'model', 'float_data'
note: 'corrected' will fall back to DATA if CORRECTED_DATA
is absent.Definition at line 26 of file statwt_pg.py.
References vla_uvfits_line_sf.verify.
statwt_pg.statwt_pg_.__bases__ [private] |
Definition at line 22 of file statwt_pg.py.
statwt_pg.statwt_pg_.__doc__ [private] |
Definition at line 23 of file statwt_pg.py.
string statwt_pg.statwt_pg_.__name__ [static, private] |
Definition at line 19 of file statwt_pg.py.
1.8.0