bandpass¶

bandpass(vis, caltable='', field='', spw='', intent='', selectdata=True, timerange='', uvrange='', antenna='', scan='', observation='', msselect='', solint='inf', combine='scan', refant='', minblperant=4, minsnr=3.0, solnorm=False, bandtype='B', smodel='', corrdepflags=False, append=False, fillgaps=0, degamp=3, degphase=3, visnorm=False, maskcenter=0, maskedge=5, docallib=False, callib='', gaintable='', gainfield='', interp='', spwmap='', parang=False)[source]¶

Calculates a bandpass calibration solution

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

Parameters

vis (path) - Name of input visibility file
caltable (string=’’) - Name of output bandpass calibration table
field (string=’’) - Select field using field id(s) or field name(s)
spw (string=’’) - Select spectral window/channels
intent (string=’’) - Select observing intent
selectdata (bool=True) - Other data selection parameters
selectdata = True
- timerange (string=’’) - Select data based on time range
- uvrange (variant=’’) - Select data within uvrange (default units meters)
- antenna (string=’’) - Select data based on antenna/baseline
- scan (string=’’) - Scan number range
- observation (string=’’) - Select by observation ID(s)
- msselect (string=’’) - Optional complex data selection (ignore for now)
solint (variant=’inf’) - Solution interval in time[,freq]
combine (string=’scan’) - Data axes which to combine for solve (obs, scan, spw, and/or field)
refant (string=’’) - Reference antenna name(s)
minblperant (int=4) - Minimum baselines _per antenna required for solve
minsnr (double=3.0) - Reject solutions below this SNR (only applies for bandtype = B)
solnorm (bool=False) - Normalize average solution amplitudes to 1.0
bandtype (string=’B’) - Type of bandpass solution (B or BPOLY)
bandtype = B
- fillgaps (int=0) - Fill flagged solution channels by interpolation
bandtype = BPOLY
- degamp (int=3) - Polynomial degree for BPOLY amplitude solution
- degphase (int=3) - Polynomial degree for BPOLY phase solution
- visnorm (bool=False) - Normalize data prior to BPOLY solution
- maskcenter (int=0) - Number of channels to avoid in center of each band
- maskedge (int=5) - Fraction of channels to avoid at each band edge (in %)
smodel (doubleVec=’’) - Point source Stokes parameters for source model.
corrdepflags (bool=False) - Respect correlation-dependent flags
append (bool=False) - Append solutions to the (existing) table
docallib (bool=False) - Use callib or traditional cal apply parameters
docallib = False
- gaintable (stringVec=’’) - Gain calibration table(s) to apply on the fly
- gainfield (stringVec=’’) - Select a subset of calibrators from gaintable(s)
- interp (stringVec=’’) - Interpolation parameters for each gaintable, as a list
- spwmap (intVec=’’) - Spectral window mappings to form for gaintable(s)
docallib = True
- callib (string=’’) - Cal Library filename
parang (bool=False) - Apply parallactic angle correction

Description

Determines the amplitude and phase as a function of frequency for each spectral window containing more than one channel. Strong sources (or many observations of moderately strong sources) are needed to obtain accurate bandpass functions. The two solution choices are: individual antenna/based channel solutions ‘B’; and a polynomial fit over the channels ‘BPOLY’. The ‘B’ solutions can be determined at any specified time interval, and is recommended in most applications.

Introduction

For channelized data, it is usually desirable to solve for the gain variations in frequency as well as in time. Variation in frequency arises as a result of non-uniform filter passbands or other frequency-dependent effects in signal transmission. It is usually the case that these frequency-dependent effects vary on timescales much longer than the time-dependent effects handled by gaincal. Thus, it makes sense to solve for them as a separate term, using the bandpass task.

It is usually best to solve for the bandpass in channelized data before solving for the gain as a function of time. However, if the gains during the bandpass calibrator observations are fluctuating over the timerange of those observations, then it can be helpful to first solve for those time-dependent gains of that source with gaincal, and input these to bandpass via gaintable. See the examples section for more on how to do this.

Common calibration solve parameters

See “Solving for Calibration” for more information on the task parameters bandpass shares with all solving tasks, including data selection, general solving properties and arrange prior calibration. Below we describe parameters unique to bandpass, and those common parameters with unique properties.

Warning

WARNING: the channelization of the bandpass solution spws is set by the nominal channelization of the input data, not the selected portion. Edge-channels should be flagged if they are not to be taken into account in the further data processing. If edge channels are excluded by the spw selection but not flagged, then solutions for those channels will be extrapolated.

Bandpass types: bandtype

The bandtype parameter selects the type of solution used for the bandpass. The choices are ‘B’ and ‘BPOLY’.

bandtype=’B’

Use of bandtype=’B’ in bandpass differs from gaintype=’G’ in gaincal only in that it is determined for each channel in each spectral window. It is possible to solve for it as a function of time, but it is most efficient to keep the B solving timescale as long as possible, and use gaincal for frequency-independent rapid time-scale variations.

Do not use combine=’spw’ with bandtype=’B’, as this will generate a solution for all spws overlaid in channel coordinates, and for which it is not yet possible to apply to all spws in frequency coordinates.

The B solutions are limited by the signal-to-noise ratio available per channel, which may be limited. It is therefore important that the data be optimally coherent over the time-range of the B solutions. As a result, B solutions are almost always preceded by an initial, provisional gaincal solution. In turn, if the B solution improves the frequency domain coherence significantly, subsequent gaincal solutions using it will be better than the original. The SNR per bandpass channel can also be boosted by using a non-trivial frequency solint to partially average the MS visibility frequency channels for the solution. However, for accuracy, it is important to use a frequency solint that doesn’t obscure actual systematic bandpass structure. If adequate SNR is unachievable by these means with the available data, use of bandtype=’BPOLY’ can be considered.

bandtype=’BPOLY’

For some observations, it may be the case that the SNR per channel is insufficient to obtain a usable per-channel B solution. In this case it is desirable to solve instead for a best-fit functional form for each antenna using the BPOLY solver. The BPOLY solver fits (Chebychev) polynomials to the amplitude and phase of the calibrator visibilities as a function of frequency. Use of combine=’spw’ will cause a single common BPOLY solution to be determined in frequency space for all selected spectral windows in aggregate (plots of such solutions with plotcal will only show the evaluated polynomial for the first spw used in the solve). It is usually most meaningful to do per-spw solutions, unless groups of adjacent spectral windows are known a priori to share a single continuous bandpass response over their combined frequency range.

The BPOLY solver requires a number of unique sub-parameters (default values are given below):

bandtype        =    'BPOLY'   #   Type of bandpass solution (B or BPOLY)
     degamp     =          3   #   Polynomial degree for BPOLY amplitude solution
     degphase   =          3   #   Polynomial degree for BPOLY phase solution
     visnorm    =      False   #   Normalize data prior to BPOLY solution
     maskcenter =          0   #   Number of channels in BPOLY to avoid in center of band
     maskedge   =          0   #   Percent of channels in BPOLY to avoid at each band edge

The degamp and degphase parameters indicate the polynomial degree desired for the amplitude and phase solutions. The maskcenter parameter is used to indicate the number of channels in the center of the band to avoid passing to the solution (e.g., to avoid Gibbs ringing in central channels for PdBI data). The maskedge parameter drops beginning and end channels. The visnorm parameter turns on normalization of the visibilities before the solution is obtained (rather than after as for solnorm).

The combine parameter can be used to combine data across spectral windows, scans, and fields.

Note that bandpass will allow you to use multiple fields, and can determine a single solution for all specified fields using combine=’field’. If you want to use more than one field in the solution, it is prudent to use an initial gaincal using proper flux densities for all sources (not just 1 Jy) and use this table as an input to bandpass because in general the phase towards two (widely separated) sources will not be sufficiently similar to combine them, and you want the same amplitude scale. If you do not include amplitude in the initial gaincal, you probably want to set visnorm=True also to take out the amplitude normalization change. Note also in the case of multiple fields, that the BPOLY solution will be labeled with the field ID of the first field used in the BPOLY solution.

Bandpass calibration considerations

Bandpass normalization (*solnorm*)

The solnorm parameter requires more explanation in the context of the bandpass. Most users are used to seeing a normalized bandpass, where the mean amplitude is unity and fiducial phase is zero. Use of solnorm=True allows this. However, the parts of the bandpass solution normalized away will be still left in any data to which it is applied, and thus you should not use solnorm=True if the bandpass calibration is the end of your calibration sequence (e.g. you have already done all the gain calibration you want to).

Note

NOTE: Setting solnorm=True will NOT rescale any previous calibration tables that the user may have supplied in gaintable.

You can safely use solnorm=True if you do the bandpass first (perhaps using a throw-away initial gaincal calibration) as we suggest above, as later gaincal calibration stages will deal with this remaining calibration term. This does have the benefit of isolating the overall (channel independent) gains to the following gaincal stage. It is also recommended for the case where you have multiple scans on possibly different bandpass calibrators. It may also be preferred when applying the bandpass before doing gaincal and then fluxscale, as significant variation of bandpass among antennas could otherwise enter the gain solution and make (probably subtle) adjustments to the flux scale.

We finally note that solnorm=False at the bandpass step in the calibration chain will still in the end produce the correct results. It only means that there will be a part of what we usually think of the gain calibration inside the bandpass solution, particularly if bandpass is run as the first step.

What if the bandpass calibrator has a significant spectral variation?

The bandpass calibrator may have a spectral slope that will change the spectral properties of the solutions if a flat-spectrum model is used. If the slope is significant, the best remedy is to estimate the spectral shape and store that model in the bandpass calibrator MS. To do so, go through the normal steps of bandpass and the gaincal runs on the bandpass and flux calibrators, followed by setjy of the flux calibrator. The next step would be to use fluxscale on the bandpass calibrator to derive its spectral index. fluxscale can store this information in a python dictionary which is subsequently fed into a second setjy run, this time using the bandpass calibrator as the source and the derived spectrum (the python dictionary) as input. This step will create a source model with the correct overall spectral slope for the bandpass calibrator. Finally, rerun bandpass and all other calibration steps again, making use of the newly created internal bandpass model.

Combining spectral windows for bandpass calibration

It may sometimes be desirable to combine spectral windows in bandpass solving, using combine=’spw’. This is useful, e.g., for calibrating the bandpass for HI observations (e.g., at the VLA) when even the bandpass calibrator has its own HI lines or is absorbed by galactic HI.

When using combine=’spw’ in bandpass, all selected spws (which must all have the same number of selected channels, have the same net sideband, and should probably all have the same net bandwidth, etc.) will effectively be averaged together to derive a single bandpass solution. The channel frequencies assigned to the solution will be a channel-by-channel average over spws of the input channel frequencies (these may or may not coincide with the frequencies of the intended spectral window to which this solution is to be appied, depending on the symmetry of the observing setup). The solution will be assigned the lowest spectral window id from the input spectral windows. This solution can be applied to any other spectral window by using spwmap and adding ‘rel’ to the frequency interpolation string for the bandpass table in the interp parameter. See the section on “Prior calibration” at Solve for Calibration for more information about the mechanics of applying bandpass solutions of this sort.

Examples

To solve for a B-bandpass using a single short scan on the calibrator (with no prior gain calibration available):

bandpass(vis = 'n5921.ms',
         caltable='n5921.bcal',
         gaintable='',                   # No gain tables yet
         gainfield='',
         interp='',
         field='0',                      # Calibrator 1331+305 = 3C286 (FIELD_ID 0)
         spw='',                         # all channels
         selectdata=False,               # No other selection
         bandtype='B',                   # standard time-binned B (rather than BPOLY)
         solint='inf',                   # set solution interval arbitrarily long
         refant='15')                    # ref antenna 15 (=VLA:N2) (ID 14)

On the other hand, we might have a number of scans on the bandpass calibrator spread over time, but we want a single bandpass solution. In this case, we could solve for and then pre-apply an initial gain calibration, and let the bandpass solution cross scans:

bandpass(vis='n5921.ms',
         caltable='n5921.bcal',
         field='0',                      # Calibrator 1331+305 = 3C286 (FIELD_ID 0)
         spw='',                         # all channels
         selectdata=False,               # No other selection
         bandtype='B',                   # standard time-binned B (rather than BPOLY)
         solint='inf',                   # set solution interval arbitrarily long
         combine='scan',                 # Solution crosses scans(ID 14)
         refant='15',                    # ref antenna 15 (=VLA:N2)
         gaintable='n5921.init.gcal',    # Our previously determined G table
         gainfield='0',
         interp='linear')                # Do linear interpolation

To solve for a single bandpass from two spectral windows (0 and 1) that is intended for a third (2), we add ‘spw’ to combine (also using a prior gain solution):

bandpass(vis='n5921.ms',
         caltable='n5921.bcal2',
         field='0',                      # Calibrator 1331+305 = 3C286 (FIELD_ID 0)
         spw='0,1',                      # all channels in spws 0 and 1
         selectdata=False,               # No other selection
         bandtype='B',                   # standard time-binned B (rather than BPOLY)
         solint='inf',                   # set solution interval arbitrarily long
         combine='scan,spw',             # Combine scans and spws into a single solution
         refant='15',                    # ref antenna 15 (=VLA:N2)
         gaintable='n5921.init.gcal',    # Our previously determined G table
         gainfield='0',
         interp='linear')                # Do linear interpolation on gaintable

The resulting bandpass table will have average channels labeled with the average frequencies of the input spectral windows channels. Applying this solution will require use of relative frequency interpolation. See here, for more information.

To solve for a BPOLY (5th order in amplitude, 7th order in phase), using data from field 2, with prior gaincal corrections pre-applied:

bandpass(vis='data.ms',          # input data set
         caltable='cal.BPOLY',   #
         spw='0:2~56',           # Use channels 3-57 (avoid end channels)
         field='0',              # Select bandpass calibrator (field 0)
         bandtype='BPOLY',       # Select bandpass polynomials
         degamp=5,               #   5th order amp
         degphase=7,             #   7th order phase
         gaintable='cal.G',      # Pre-apply gain solutions derived previously
         refant='14')            #

Development: No additional development details

Parameter Details: Detailed descriptions of each function parameter

vis (path) - Name of input visibility file

default: non

Example: vis=’ngc5921.ms’

caltable (string='') - Name of output bandpass calibration table

default: none

Example: caltable=’ngc5921.bcal’

field (string='') - 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

spw (string='') - Select spectral window/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,
INCLUSIVE
spw=’*:5~61’; all spw with channels 5 to 61
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. (NOTE ‘;’ to separate
channel selections)
spw=’0:0~10^2,1:20~30^5’; spw 0, channels
0,2,4,6,8,10, spw 1, channels 20,25,30 
type ‘help par.selection’ for more examples.

intent (string='') - Select observing intent

default: ‘’ (no selection by intent)

Example: intent=’BANDPASS’ (selects data

labelled with BANDPASS intent)

selectdata (bool=True) - Other data selection parameters

default: True

timerange (string='') - 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

uvrange (variant='') - Select data within uvrange (default units meters)

Subparameter of selectdata=True

default: ‘’ (all)

Examples:
uvrange=’0~1000klambda’; uvrange from 0-1000
kilo-lambda
uvrange=’>4klambda’;uvranges greater than 4
kilolambda

antenna (string='') - Select data based on antenna/baseline

Subparameter of selectdata=True
default: ‘’ (all)
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: just for antenna selection, an integer (or
integer list) is converted to a string and
matched against the antenna ‘name’ first. Only if
that fails, the integer is matched with the
antenna ID. The latter is the case for most
observatories, where the antenna name is not
strictly an integer.

scan (string='') - Scan number range

Subparameter of selectdata=True
default: ‘’ = all
Check ‘go listobs’ to insure the scan numbers are
in order.

observation (string='') - Select by observation ID(s)

Subparameter of selectdata=True

default: ‘’ = all

Example: observation=’0~2,4’

msselect (string='') - Optional complex data selection (ignore for now)

solint (variant='inf') - Solution interval in time[,freq]

default: ‘inf’ (~infinite, up to boundaries
controlled by combine, with no pre-averaging in
frequency)
Options for time: ‘inf’ (~infinite), ‘int’ (per
integration), any float or integer value with or
without units
Options for freq: an integer with ‘ch’ suffix
will enforce pre-averaging by the specified
number of channels. A numeric value suffixed with
frequency units (e.g., ‘Hz’,’kHz’,’MHz’) will
enforce pre-averaging by an integral number of
channels amounting to no more than the specified
bandwidth.
Examples: solint=’1min’; solint=’60s’,
solint=60 –> 1 minute
solint=’0s’; solint=0; solint=’int’ –> per
integration
solint=’-1s’; solint=’inf’ –> ~infinite, up
to boundaries enforced by combine 
solint=’inf,8Mhz’ –> ~infinite in time, with
8MHz pre-average in freq 
solint=’int,32ch’ –> per-integration in time,
with 32-channel pre-average in freq

combine (string='scan') - Data axes to combine for solving

default: ‘scan’ –> solutions will break at obs,
field, and spw boundaries but may extend over
multiple scans (per obs, field and spw) up to
solint.
Options: ‘’,’obs’,’scan’,’spw’,field’, or any
comma-separated combination in a single string.
Example: combine=’scan,spw’ –> extend
solutions over scan boundaries (up to the
solint), and combine spws for solving.

refant (string='') - Reference antenna name(s); a prioritized list may be

specified

default: ‘’ (no reference antenna)

Examples:
refant=’13’ (antenna with index 13) 
refant=’VA04’ (VLA antenna #4)
refant=’EA02,EA23,EA13’ (EVLA antenna EA02,
use EA23 and EA13 as alternates if/when EA02
drops out)

Use ‘go listobs’ for antenna listing

minblperant (int=4) - Minimum baselines _per antenna required for solve

default: 4
Antennas with fewer baselines are excluded from
solutions. Amplitude solutions with fewer than 4
baselines, and phase solutions with fewer than 3
baselines are only trivially constrained, and are
no better than baseline-based solutions.
example: minblperant=10 –> Antennas
participating on 10 or more baselines are
included in the solve.

minsnr (double=3.0) - Reject solutions below this SNR (only applies for

bandtype = B)

default: 3.0

solnorm (bool=False) - Normalize bandpass amplitudes and phase for each spw,

pol, ant, and timestamp

default: False (no normalization)

bandtype (string='B') - Type of bandpass solution (B or BPOLY)

default: ‘B’
‘B’ does a channel by channel solution for each
specified spw. 
‘BPOLY’ is somewhat experimental. It will fit an
nth order polynomial for the amplitude and phase
as a function of frequency. Only one fit is made
for all specified spw, and edge channels should
be omitted.
Use taskname=plotcal in order to compare the
results from B and BPOLY.
Example: bandtype=’BPOLY’

smodel (doubleVec='') - Point source Stokes parameters for source model.

corrdepflags (bool=False) - If False (default), if any correlation is flagged, treat all correlations in

the visibility vector as flagged when solving (per channel, per baseline).
If True, use unflagged correlations in a visibility vector, even if one or more
other correlations are flagged.

Default: False (treat correlation vectors with one or more correlations flagged as entirely flagged)

Traditionally, CASA has observed a strict interpretation of 
correlation-dependent flags: if one or more correlations 
(for any baseline and channel) is flagged, then all available 
correlations for the same baseline and channel are 
treated as flagged.  However, it is desirable in some 
circumstances to relax this stricture, e.g., to preserve use
of data from antennas with only one good polarization (e.g., one polarization
is bad or entirely absent).  Solutions for the bad or missing polarization 
will be rendered as flagged.

append (bool=False) - Append solutions to the (existing) table

default: False (overwrite existing table or make
new table)
Append solutions to the (existing) table.
Appended solutions must be derived from the same
MS as the existing caltable, and solution spws
must have the same meta-info (according to spw
selection and solint) or be non-overlapping.

fillgaps (int=0) - Fill flagged solution channels by interpolation

Subparameter of bandtype=’B’

default: 0 (don’t interpolate)

Example: fillgaps=3 (interpolate gaps 3

channels wide and narrower)

degamp (int=3) - Polynomial degree for BPOLY amplitude solution