##################### generated by xml-casa (v2) from ptclean.xml ###################
##################### 241408438008de8701d1ddbf418b317d ##############################
from __future__ import absolute_import
from casashell.private.stack_manip import find_local as __sf__
from casashell.private.stack_manip import find_frame as _find_frame
from casatools.typecheck import validator as _pc
from casatools.coercetype import coerce as _coerce
from suncasatasks import ptclean as _ptclean_t
from collections import OrderedDict
import numpy
import sys
import os
import shutil
[docs]
def static_var(varname, value):
def decorate(func):
setattr(func, varname, value)
return func
return decorate
[docs]
class _ptclean:
"""
ptclean ---- Parallelized tclean in consecutive time steps
Parallelized clean in consecutive time steps. Packed over CASA tclean.
--------- parameter descriptions ---------------------------------------------
vis Name(s) of input visibility file(s)
default: none;
example: vis='ngc5921.ms'
vis=['ngc5921a.ms','ngc5921b.ms']; multiple MSes
imageprefix Prefix of output image names (usually useful in defining the output path)
imagesuffix Suffix of output image names (usually useful in specifyting the image type, version, etc.)
ncpu Number of cpu cores to use
twidth Number of time pixels to average
doreg True if use vla_prep to register the image
usephacenter True if use the phacenter information from the measurement set (e.g., VLA); False to assume the phase center is at the solar disk center (EOVSA)
reftime Reference time of the J2000 coordinates associated with the ephemeris target. e.g., "2012/03/03/12:00". This is used for helioimage2fits.py to find the solar x y offset in order to register the image. If not set, use the actual timerange of the image (default)
toTb True if convert to brightness temperature
sclfactor scale the brightness temperature up by its value
subregion The name of a CASA region string
The name of a CASA image or region file or region string. Only locations within the region will
output to the fits file.
If regions specified fall completely outside of the image, ptclean will throw an error.
Manual mask options/examples :
subregion='box[[224pix,224pix],[288pix,288pix]]' : A CASA region string.
docompress True if compress the output fits files
overwrite True if overwrite the image
selectdata Enable data selection parameters.
field to image or mosaic. Use field id(s) or 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
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
For multiple MS input, a list of field strings can be used:
field = ['0~2','0~4']; field ids 0-2 for the first MS and 0-4
for the second
field = '0~2'; field ids 0-2 for all input MSes
spw l window/channels
NOTE: channels de-selected here will contain all zeros if
selected by the parameter mode subparameters.
default: ''=all spectral windows and channels
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.
For multiple MS input, a list of spw strings can be used:
spw=['0','0~3']; spw ids 0 for the first MS and 0-3 for the second
spw='0~3' spw ids 0-3 for all input MS
spw='3:10~20;50~60' for multiple channel ranges within spw id 3
spw='3:10~20;50~60,4:0~30' for different channel ranges for spw ids 3 and 4
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
spw='1~4;6:15~48' for channels 15 through 48 for spw ids 1,2,3,4 and 6
timerange Range of time to select from data
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
For multiple MS input, a list of timerange strings can be
used:
timerange=['09:14:0~09:54:0','> 10:24:00']
timerange='09:14:0~09:54:0''; apply the same timerange for
all input MSes
uvrange Select data within uvrange (default unit is meters)
default: '' (all); example:
uvrange='0~1000klambda'; uvrange from 0-1000 kilo-lambda
uvrange='> 4klambda';uvranges greater than 4 kilo lambda
For multiple MS input, a list of uvrange strings can be
used:
uvrange=['0~1000klambda','100~1000klamda']
uvrange='0~1000klambda'; apply 0-1000 kilo-lambda for all
input MSes
antenna Select data based on antenna/baseline
default: '' (all)
If antenna string is a non-negative integer, it is
assumed to be an antenna index, otherwise, it is
considered an antenna name.
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 index 5
antenna='05'; all baselines with antenna number 05
(VLA old name)
antenna='5,6,9'; all baselines with antennas 5,6,9
index number
For multiple MS input, a list of antenna strings can be
used:
antenna=['5','5\&6'];
antenna='5'; antenna index 5 for all input MSes
antenna='!DV14'; use all antennas except DV14
scan Scan number range
default: '' (all)
example: scan='1~5'
For multiple MS input, a list of scan strings can be used:
scan=['0~100','10~200']
scan='0~100; scan ids 0-100 for all input MSes
observation Observation ID range
default: '' (all)
example: observation='1~5'
intent Scan Intent(s)
default: '' (all)
example: intent='TARGET_SOURCE'
example: intent='TARGET_SOURCE1,TARGET_SOURCE2'
example: intent='TARGET_POINTING*'
datacolumn Data column to image (data or observed, corrected)
default:'data'
( If 'corrected' does not exist, it will use 'data' instead )
imsize Number of pixels
example : imsize = [350,250]
imsize = 500 is equivalent to [500,500]
To take proper advantage of internal optimized FFT routines, the
number of pixels must be even and factorizable by 2,3,5,7 only.
cell Cell size
example: cell=['0.5arcsec,'0.5arcsec'] or
cell=['1arcmin', '1arcmin']
cell = '1arcsec' is equivalent to ['1arcsec','1arcsec']
phasecenter Phase center of the image (string or field id); if the phasecenter is the name known major solar system object ('MERCURY', 'VENUS', 'MARS', 'JUPITER', 'SATURN', 'URANUS', 'NEPTUNE', 'PLUTO', 'SUN', 'MOON') or is an ephemerides table then that source is tracked and the background sources get smeared. There is a special case, when phasecenter='TRACKFIELD', which will use the ephemerides or polynomial phasecenter in the FIELD table of the MS's as the source center to track.
example: phasecenter=6
phasecenter='J2000 19h30m00 -40d00m00'
phasecenter='J2000 292.5deg -40.0deg'
phasecenter='J2000 5.105rad -0.698rad'
phasecenter='ICRS 13:05:27.2780 -049.28.04.458'
phasecenter='myComet_ephem.tab'
phasecenter='MOON'
phasecenter='TRACKFIELD'
stokes Stokes Planes to make
default='I'; example: stokes='IQUV';
Options: 'I','Q','U','V','IV','QU','IQ','UV','IQUV','RR','LL','XX','YY','RRLL','XXYY','pseudoI'
Note : Due to current internal code constraints, if any correlation pair
is flagged, by default, no data for that row in the MS will be used.
So, in an MS with XX,YY, if only YY is flagged, neither a
Stokes I image nor an XX image can be made from those data points.
In such a situation, please split out only the unflagged correlation into
a separate MS.
Note : The 'pseudoI' option is a partial solution, allowing Stokes I imaging
when either of the parallel-hand correlations are unflagged.
The remaining constraints shall be removed (where logical) in a future release.
projection Coordinate projection
Examples : SIN, NCP
A list of supported (but untested) projections can be found here :
http://casa.nrao.edu/active/docs/doxygen/html/classcasa_1_1Projection.html#a3d5f9ec787e4eabdce57ab5edaf7c0cd
startmodel Name of starting model image
The contents of the supplied starting model image will be
copied to the imagename.model before the run begins.
example : startmodel = 'singledish.im'
For deconvolver='mtmfs', one image per Taylor term must be provided.
example : startmodel = ['try.model.tt0', 'try.model.tt1']
startmodel = ['try.model.tt0'] will use a starting model only
for the zeroth order term.
startmodel = ['','try.model.tt1'] will use a starting model only
for the first order term.
This starting model can be of a different image shape and size from
what is currently being imaged. If so, an image regrid is first triggered
to resample the input image onto the target coordinate system.
A common usage is to set this parameter equal to a single dish image
Negative components in the model image will be included as is.
[ Note : If an error occurs during image resampling/regridding,
please try using task imregrid to resample the starting model
image onto a CASA image with the target shape and
coordinate system before supplying it via startmodel ]
specmode Spectral definition mode (mfs,cube,cubedata, cubesource)
mode='mfs' : Continuum imaging with only one output image channel.
(mode='cont' can also be used here)
mode='cube' : Spectral line imaging with one or more channels
Parameters start, width,and nchan define the spectral
coordinate system and can be specified either in terms
of channel numbers, frequency or velocity in whatever
spectral frame is specified in 'outframe'.
All internal and output images are made with outframe as the
base spectral frame. However imaging code internally uses the fixed
spectral frame, LSRK for automatic internal software
Doppler tracking so that a spectral line observed over an
extended time range will line up appropriately.
Therefore the output images have additional spectral frame conversion
layer in LSRK on the top the base frame.
(Note : Even if the input parameters are specified in a frame
other than LSRK, the viewer still displays spectral
axis in LSRK by default because of the conversion frame
layer mentioned above. The viewer can be used to relabel
the spectral axis in any desired frame - via the spectral
reference option under axis label properties in the
data display options window.)
mode='cubedata' : Spectral line imaging with one or more channels
There is no internal software Doppler tracking so
a spectral line observed over an extended time range
may be smeared out in frequency. There is strictly
no valid spectral frame with which to label the output
images, but they will list the frame defined in the MS.
mode='cubesource': Spectral line imaging while
tracking moving source (near field or solar system
objects). The velocity of the source is accounted
and the frequency reported is in the source frame.
As there is not SOURCE frame defined,
the frame reported will be REST (as it may not be
in the rest frame emission region may be
moving w.r.t the systemic velocity frame)
reffreq Reference frequency of the output image coordinate system
Example : reffreq='1.5GHz' as a string with units.
By default, it is calculated as the middle of the selected frequency range.
For deconvolver='mtmfs' the Taylor expansion is also done about
this specified reference frequency.
nchan Number of channels in the output image
For default (=-1), the number of channels will be automatically determined
based on data selected by 'spw' with 'start' and 'width'.
It is often easiest to leave nchan at the default value.
example: nchan=100
start First channel (e.g. start=3,start=\'1.1GHz\',start=\'15343km/s\')
of output cube images specified by data channel number (integer),
velocity (string with a unit), or frequency (string with a unit).
Default:''; The first channel is automatically determined based on
the 'spw' channel selection and 'width'.
When the channel number is used along with the channel selection
in 'spw' (e.g. spw='0:6~100'),
'start' channel number is RELATIVE (zero-based) to the selected
channels in 'spw'. So for the above example,
start=1 means that the first image channel is the second selected
data channel, which is channel 7.
For specmode='cube', when velocity or frequency is used it is
interpreted with the frame defined in outframe. [The parameters of
the desired output cube can be estimated by using the 'transform'
functionality of 'plotms']
examples: start='5.0km/s'; 1st channel, 5.0km/s in outframe
start='22.3GHz'; 1st channel, 22.3GHz in outframe
width Channel width (e.g. width=2,width=\'0.1MHz\',width=\'10km/s\') of output cube images
specified by data channel number (integer), velocity (string with a unit), or
or frequency (string with a unit).
Default:''; data channel width
The sign of width defines the direction of the channels to be incremented.
For width specified in velocity or frequency with '-' in front gives image channels in
decreasing velocity or frequency, respectively.
For specmode='cube', when velocity or frequency is used it is interpreted with
the reference frame defined in outframe.
examples: width='2.0km/s'; results in channels with increasing velocity
width='-2.0km/s'; results in channels with decreasing velocity
width='40kHz'; results in channels with increasing frequency
width=-2; results in channels averaged of 2 data channels incremented from
high to low channel numbers
outframe Spectral reference frame in which to interpret \'start\' and \'width\'
Options: '','LSRK','LSRD','BARY','GEO','TOPO','GALACTO','LGROUP','CMB'
example: outframe='bary' for Barycentric frame
REST -- Rest frequency
LSRD -- Local Standard of Rest (J2000)
-- as the dynamical definition (IAU, [9,12,7] km/s in galactic coordinates)
LSRK -- LSR as a kinematical (radio) definition
-- 20.0 km/s in direction ra,dec = [270,+30] deg (B1900.0)
BARY -- Barycentric (J2000)
GEO --- Geocentric
TOPO -- Topocentric
GALACTO -- Galacto centric (with rotation of 220 km/s in direction l,b = [90,0] deg.
LGROUP -- Local group velocity -- 308km/s towards l,b = [105,-7] deg (F. Ghigo)
CMB -- CMB velocity -- 369.5km/s towards l,b = [264.4, 48.4] deg (F. Ghigo)
DEFAULT = LSRK
veltype Velocity type (radio, z, ratio, beta, gamma, optical)
For start and/or width specified in velocity, specifies the velocity definition
Options: 'radio','optical','z','beta','gamma','optical'
NOTE: the viewer always defaults to displaying the 'radio' frame,
but that can be changed in the position tracking pull down.
The different types (with F = f/f0, the frequency ratio), are:
Z = (-1 + 1/F)
RATIO = (F) *
RADIO = (1 - F)
OPTICAL == Z
BETA = ((1 - F2)/(1 + F2))
GAMMA = ((1 + F2)/2F) *
RELATIVISTIC == BETA (== v/c)
DEFAULT == RADIO
Note that the ones with an '*' have no real interpretation
(although the calculation will proceed) if given as a velocity.
restfreq List of rest frequencies or a rest frequency in a string.
Specify rest frequency to use for output image.
*Currently it uses the first rest frequency in the list for translation of
velocities. The list will be stored in the output images.
Default: []; look for the rest frequency stored in the MS, if not available,
use center frequency of the selected channels
examples: restfreq=['1.42GHz']
restfreq='1.42GHz'
interpolation Spectral interpolation (nearest,linear,cubic)
Interpolation rules to use when binning data channels onto image channels
and evaluating visibility values at the centers of image channels.
Note : 'linear' and 'cubic' interpolation requires data points on both sides of
each image frequency. Errors are therefore possible at edge channels, or near
flagged data channels. When image channel width is much larger than the data
channel width there is nothing much to be gained using linear or cubic thus
not worth the extra computation involved.
gridder Gridding options (standard, wproject, widefield, mosaic, awproject)
The following options choose different gridding convolution
functions for the process of convolutional resampling of the measured
visibilities onto a regular uv-grid prior to an inverse FFT.
Model prediction (degridding) also uses these same functions.
Several wide-field effects can be accounted for via careful choices of
convolution functions. Gridding (degridding) runtime will rise in
proportion to the support size of these convolution functions (in uv-pixels).
standard : Prolate Spheroid with 3x3 uv pixel support size
[ This mode can also be invoked using 'ft' or 'gridft' ]
wproject : W-Projection algorithm to correct for the widefield
non-coplanar baseline effect. [Cornwell et.al 2008]
wprojplanes is the number of distinct w-values at
which to compute and use different gridding convolution
functions (see help for wprojplanes).
Convolution function support size can range
from 5x5 to few 100 x few 100.
[ This mode can also be invoked using 'wprojectft' ]
widefield : Facetted imaging with or without W-Projection per facet.
A set of facets x facets subregions of the specified image
are gridded separately using their respective phase centers
(to minimize max W). Deconvolution is done on the joint
full size image, using a PSF from the first subregion.
wprojplanes=1 : standard prolate spheroid gridder per facet.
wprojplanes > 1 : W-Projection gridder per facet.
nfacets=1, wprojplanes > 1 : Pure W-Projection and no facetting
nfacets=1, wprojplanes=1 : Same as standard,ft,gridft
A combination of facetting and W-Projection is relevant only for
very large fields of view.
mosaic : A-Projection with azimuthally symmetric beams without
sidelobes, beam rotation or squint correction.
Gridding convolution functions per visibility are computed
from FTs of PB models per antenna.
This gridder can be run on single fields as well as mosaics.
VLA : PB polynomial fit model (Napier and Rots, 1982)
EVLA : PB polynomial fit model (Perley, 2015)
ALMA : Airy disks for a 10.7m dish (for 12m dishes) and
6.25m dish (for 7m dishes) each with 0.75m
blockages (Hunter/Brogan 2011). Joint mosaic
imaging supports heterogeneous arrays for ALMA.
Typical gridding convolution function support sizes are
between 7 and 50 depending on the desired
accuracy (given by the uv cell size or image field of view).
[ This mode can also be invoked using 'mosaicft' or 'ftmosaic' ]
awproject : A-Projection with azimuthally asymmetric beams and
including beam rotation, squint correction,
conjugate frequency beams and W-projection.
[Bhatnagar et.al, 2008]
Gridding convolution functions are computed from
aperture illumination models per antenna and optionally
combined with W-Projection kernels and a prolate spheroid.
This gridder can be run on single fields as well as mosaics.
VLA : Uses ray traced model (VLA and EVLA) including feed
leg and subreflector shadows, off-axis feed location
(for beam squint and other polarization effects), and
a Gaussian fit for the feed beams (Ref: Brisken 2009)
ALMA : Similar ray-traced model as above (but the correctness
of its polarization properties remains un-verified).
Typical gridding convolution function support sizes are
between 7 and 50 depending on the desired
accuracy (given by the uv cell size or image field of view).
When combined with W-Projection they can be significantly larger.
[ This mode can also be invoked using 'awprojectft' ]
imagemosaic : (untested implementation)
Grid and iFT each pointing separately and combine the
images as a linear mosaic (weighted by a PB model) in
the image domain before a joint minor cycle.
VLA/ALMA PB models are same as for gridder='mosaicft'
------ Notes on PB models :
(1) Several different sources of PB models are used in the modes
listed above. This is partly for reasons of algorithmic flexibility
and partly due to the current lack of a common beam model
repository or consensus on what beam models are most appropriate.
(2) For ALMA and gridder='mosaic', ray-traced (TICRA) beams
are also available via the vpmanager tool.
For example, call the following before the tclean run.
vp.setpbimage(telescope="ALMA",
compleximage='/home/casa/data/trunk/alma/responses/ALMA_0_DV__0_0_360_0_45_90_348.5_373_373_GHz_ticra2007_VP.im',
antnames=['DV'+'%02d'%k for k in range(25)])
vp.saveastable('mypb.tab')
Then, supply vptable='mypb.tab' to tclean.
( Currently this will work only for non-parallel runs )
------ Note on PB masks :
In tclean, A-Projection gridders (mosaic and awproject) produce a
.pb image and use the 'pblimit' subparameter to decide normalization
cutoffs and construct an internal T/F mask in the .pb and .image images.
However, this T/F mask cannot directly be used during deconvolution
(which needs a 1/0 mask). There are two options for making a pb based
deconvolution mask.
-- Run tclean with niter=0 to produce the .pb, construct a 1/0 image
with the desired threshold (using ia.open('newmask.im');
ia.calc('iif("xxx.pb">0.3,1.0,0.0)');ia.close() for example),
and supply it via the 'mask' parameter in a subsequent run
(with calcres=F and calcpsf=F to restart directly from the minor cycle).
-- Run tclean with usemask='pb' for it to automatically construct
a 1/0 mask from the internal T/F mask from .pb at a fixed 0.2 threshold.
----- Making PBs for gridders other than mosaic,awproject
After the PSF generation, a PB is constructed using the same
models used in gridder='mosaic' but just evaluated in the image
domain without consideration to weights.
facets Number of facets on a side
A set of (facets x facets) subregions of the specified image
are gridded separately using their respective phase centers
(to minimize max W). Deconvolution is done on the joint
full size image, using a PSF from the first subregion/facet.
chanchunks Number of channel chunks to grid separately
For large image cubes, the gridders can run into memory limits
as they loop over all available image planes for each row of data
accessed. To prevent this problem, we can grid subsets of channels
in sequence so that at any given time only part of the image cube
needs to be loaded into memory. This parameter controls the
number of chunks to split the cube into.
Example : chanchunks = 4
[ This feature is experimental and may have restrictions on how
chanchunks is to be chosen. For now, please pick chanchunks so
that nchan/chanchunks is an integer. ]
wprojplanes Number of distinct w-values at which to compute and use different
gridding convolution functions for W-Projection
An appropriate value of wprojplanes depends on the presence/absence
of a bright source far from the phase center, the desired dynamic
range of an image in the presence of a bright far out source,
the maximum w-value in the measurements, and the desired trade off
between accuracy and computing cost.
As a (rough) guide, VLA L-Band D-config may require a
value of 128 for a source 30arcmin away from the phase
center. A-config may require 1024 or more. To converge to an
appropriate value, try starting with 128 and then increasing
it if artifacts persist. W-term artifacts (for the VLA) typically look
like arc-shaped smears in a synthesis image or a shift in source
position between images made at different times. These artifacts
are more pronounced the further the source is from the phase center.
There is no harm in simply always choosing a large value (say, 1024)
but there will be a significant performance cost to doing so, especially
for gridder='awproject' where it is combined with A-Projection.
wprojplanes=-1 is an option for gridder='widefield' or 'wproject'
in which the number of planes is automatically computed.
vptable vpmanager
vptable="" : Choose default beams for different telescopes
ALMA : Airy disks
EVLA : old VLA models.
Other primary beam models can be chosen via the vpmanager tool.
Step 1 : Set up the vpmanager tool and save its state in a table
vp.setpbpoly(telescope='EVLA', coeff=[1.0, -1.529e-3, 8.69e-7, -1.88e-10])
vp.saveastable('myvp.tab')
Step 2 : Supply the name of that table in tclean.
tclean(....., vptable='myvp.tab',....)
Please see the documentation for the vpmanager for more details on how to
choose different beam models. Work is in progress to update the defaults
for EVLA and ALMA.
Note : AWProjection currently does not use this mechanism to choose
beam models. It instead uses ray-traced beams computed from
parameterized aperture illumination functions, which are not
available via the vpmanager. So, gridder='awproject' does not allow
the user to set this parameter.
usepointing Use the pointing table to determine where the beam are for mosaic gridder; if False then phasecenters of the fields selected are used to determine direction of each mosaic pointing.
mosweight When doing Brigg's style weighting (including uniform) to perform the weight density calculation for each field indepedently if True. If False the weight density is calculated from the average uv distribution of all the fields.
aterm Use aperture illumination functions during gridding
This parameter turns on the A-term of the AW-Projection gridder.
Gridding convolution functions are constructed from aperture illumination
function models of each antenna.
psterm Use prolate spheroidal during gridding
wbawp Use frequency dependent A-terms
Scale aperture illumination functions appropriately with frequency
when gridding and combining data from multiple channels.
conjbeams Use conjugate frequency for wideband A-terms
While gridding data from one frequency channel, choose a
convolution function from a 'conjugate' frequency such that
the resulting baseline primary beam is approximately constant
across frequency. For a system in which the primary beam scales
with frequency, this step will eliminate instrumental spectral
structure from the measured data and leave only the sky spectrum
for the minor cycle to model and reconstruct [Bhatnagar et.al,2013].
As a rough guideline for when this is relevant, a source at the half
power point of the PB at the center frequency will see an artificial
spectral index of -1.4 due to the frequency dependence of the PB
[Sault and Wieringa, 1994]. If left uncorrected during gridding, this
spectral structure must be modeled in the minor cycle (using the
mtmfs algorithm) to avoid dynamic range limits (of a few hundred
for a 2:1 bandwidth).
cfcache Convolution function cache directory name
Name of a directory in which to store gridding convolution functions.
This cache is filled at the beginning of an imaging run. This step can be time
consuming but the cache can be reused across multiple imaging runs that
use the same image parameters (cell size, field-of-view, spectral data
selections, etc).
By default, cfcache = imagename + '.cf'
computepastep At what parallactic angle interval to recompute aperture
illumination functions (deg)
This parameter controls the accuracy of the aperture illumination function
used with AProjection for alt-az mount dishes where the AIF rotates on the
sky as the synthesis image is built up.
rotatepastep At what parallactic angle interval to rotate nearest
aperture illumination function (deg)
Instead of recomputing the AIF for every timestep's parallactic angle,
the nearest existing AIF is picked and rotated in steps of this amount.
For example, computepastep=360.0 and rotatepastep=5.0 will compute
the AIFs at only the starting parallactic angle and all other timesteps will
use a rotated version of that AIF at the nearest 5.0 degree point.
pblimit PB gain level at which to cut off normalizations
Divisions by .pb during normalizations have a cut off at a .pb gain
level given by pblimit. Outside this limit, image values are set to zero.
Additionally, by default, an internal T/F mask is applied to the .pb, .image and
.residual images to mask out (T) all invalid pixels outside the pblimit area.
Note : This internal T/F mask cannot be used as a deconvolution mask.
To do so, please follow the steps listed above in the Notes for the
'gridder' parameter.
Note : To prevent the internal T/F mask from appearing in anything other
than the .pb and .image.pbcor images, 'pblimit' can be set to a
negative number. The absolute value will still be used as a valid 'pblimit'.
A tclean restart using existing output images on disk that already
have this T/F mask in the .residual and .image but only pblimit set
to a negative value, will remove this mask after the next major cycle.
normtype Normalization type (flatnoise, flatsky, pbsquare)
Gridded (and FT'd) images represent the PB-weighted sky image.
Qualitatively it can be approximated as two instances of the PB
applied to the sky image (one naturally present in the data
and one introduced during gridding via the convolution functions).
xxx.weight : Weight image approximately equal to sum ( square ( pb ) )
xxx.pb : Primary beam calculated as sqrt ( xxx.weight )
normtype='flatnoise' : Divide the raw image by sqrt(.weight) so that
the input to the minor cycle represents the
product of the sky and PB. The noise is 'flat'
across the region covered by each PB.
normtype='flatsky' : Divide the raw image by .weight so that the input
to the minor cycle represents only the sky.
The noise is higher in the outer regions of the
primary beam where the sensitivity is low.
normtype='pbsquare' : No normalization after gridding and FFT.
The minor cycle sees the sky times pb square
deconvolver Name of minor cycle algorithm (hogbom,clark,multiscale,mtmfs,mem,clarkstokes)
Each of the following algorithms operate on residual images and psfs
from the gridder and produce output model and restored images.
Minor cycles stop and a major cycle is triggered when cyclethreshold
or cycleniter are reached. For all methods, components are picked from
the entire extent of the image or (if specified) within a mask.
hogbom : An adapted version of Hogbom Clean [Hogbom, 1974]
- Find the location of the peak residual
- Add this delta function component to the model image
- Subtract a scaled and shifted PSF of the same size as the image
from regions of the residual image where the two overlap.
- Repeat
clark : An adapted version of Clark Clean [Clark, 1980]
- Find the location of max(I^2+Q^2+U^2+V^2)
- Add delta functions to each stokes plane of the model image
- Subtract a scaled and shifted PSF within a small patch size
from regions of the residual image where the two overlap.
- After several iterations trigger a Clark major cycle to subtract
components from the visibility domain, but without de-gridding.
- Repeat
( Note : 'clark' maps to imagermode='' in the old clean task.
'clark_exp' is another implementation that maps to
imagermode='mosaic' or 'csclean' in the old clean task
but the behavior is not identical. For now, please
use deconvolver='hogbom' if you encounter problems. )
clarkstokes : Clark Clean operating separately per Stokes plane
(Note : 'clarkstokes_exp' is an alternate version. See above.)
multiscale : MultiScale Clean [Cornwell, 2008]
- Smooth the residual image to multiple scale sizes
- Find the location and scale at which the peak occurs
- Add this multiscale component to the model image
- Subtract a scaled,smoothed,shifted PSF (within a small
patch size per scale) from all residual images
- Repeat from step 2
mtmfs : Multi-term (Multi Scale) Multi-Frequency Synthesis [Rau and Cornwell, 2011]
- Smooth each Taylor residual image to multiple scale sizes
- Solve a NTxNT system of equations per scale size to compute
Taylor coefficients for components at all locations
- Compute gradient chi-square and pick the Taylor coefficients
and scale size at the location with maximum reduction in
chi-square
- Add multi-scale components to each Taylor-coefficient
model image
- Subtract scaled,smoothed,shifted PSF (within a small patch size
per scale) from all smoothed Taylor residual images
- Repeat from step 2
mem : Maximum Entropy Method [Cornwell and Evans, 1985]
- Iteratively solve for values at all individual pixels via the
MEM method. It minimizes an objective function of
chi-square plus entropy (here, a measure of difference
between the current model and a flat prior model).
(Note : This MEM implementation is not very robust.
Improvements will be made in the future.)
scales List of scale sizes (in pixels) for multi-scale and mtmfs algorithms.
--> scales=[0,6,20]
This set of scale sizes should represent the sizes
(diameters in units of number of pixels)
of dominant features in the image being reconstructed.
The smallest scale size is recommended to be 0 (point source),
the second the size of the synthesized beam and the third 3-5
times the synthesized beam, etc. For example, if the synthesized
beam is 10" FWHM and cell=2",try scales = [0,5,15].
For numerical stability, the largest scale must be
smaller than the image (or mask) size and smaller than or
comparable to the scale corresponding to the lowest measured
spatial frequency (as a scale size much larger than what the
instrument is sensitive to is unconstrained by the data making
it harder to recovery from errors during the minor cycle).
nterms Number of Taylor coefficients in the spectral model
- nterms=1 : Assume flat spectrum source
- nterms=2 : Spectrum is a straight line with a slope
- nterms=N : A polynomial of order N-1
From a Taylor expansion of the expression of a power law, the
spectral index is derived as alpha = taylorcoeff_1 / taylorcoeff_0
Spectral curvature is similarly derived when possible.
The optimal number of Taylor terms depends on the available
signal to noise ratio, bandwidth ratio, and spectral shape of the
source as seen by the telescope (sky spectrum x PB spectrum).
nterms=2 is a good starting point for wideband EVLA imaging
and the lower frequency bands of ALMA (when fractional bandwidth
is greater than 10%) and if there is at least one bright source for
which a dynamic range of greater than few 100 is desired.
Spectral artifacts for the VLA often look like spokes radiating out from
a bright source (i.e. in the image made with standard mfs imaging).
If increasing the number of terms does not eliminate these artifacts,
check the data for inadequate bandpass calibration. If the source is away
from the pointing center, consider including wide-field corrections too.
(Note : In addition to output Taylor coefficient images .tt0,.tt1,etc
images of spectral index (.alpha), an estimate of error on
spectral index (.alpha.error) and spectral curvature (.beta,
if nterms is greater than 2) are produced.
- These alpha, alpha.error and beta images contain
internal T/F masks based on a threshold computed
as peakresidual/10. Additional masking based on
.alpha/.alpha.error may be desirable.
- .alpha.error is a purely empirical estimate derived
from the propagation of error during the division of
two noisy numbers (alpha = xx.tt1/xx.tt0) where the
'error' on tt1 and tt0 are simply the values picked from
the corresponding residual images. The absolute value
of the error is not always accurate and it is best to interpret
the errors across the image only in a relative sense.)
smallscalebias A numerical control to bias the solution towards smaller scales.
The peak from each scale's smoothed residual is
multiplied by ( 1 - smallscalebias * scale/maxscale )
to increase or decrease the amplitude relative to other scales,
before the scale with the largest peak is chosen.
smallscalebias=0.6 (default) applies a slight bias towards small
scales, ranging from 1.0 for a point source to
0.4 for the largest scale size
Values larger than 0.6 will bias the solution towards smaller scales.
Values smaller than 0.6 will tend towards giving all scales equal weight.
restoration e.
Construct a restored image : imagename.image by convolving the model
image with a clean beam and adding the residual image to the result.
If a restoringbeam is specified, the residual image is also
smoothed to that target resolution before adding it in.
If a .model does not exist, it will make an empty one and create
the restored image from the residuals ( with additional smoothing if needed ).
With algorithm='mtmfs', this will construct Taylor coefficient maps from
the residuals and compute .alpha and .alpha.error.
restoringbeam ize to use.
- restoringbeam='' or ['']
A Gaussian fitted to the PSF main lobe (separately per image plane).
- restoringbeam='10.0arcsec'
Use a circular Gaussian of this width for all planes
- restoringbeam=['8.0arcsec','10.0arcsec','45deg']
Use this elliptical Gaussian for all planes
- restoringbeam='common'
Automatically estimate a common beam shape/size appropriate for
all planes.
Note : For any restoring beam different from the native resolution
the model image is convolved with the beam and added to
residuals that have been convolved to the same target resolution.
pbcor the output restored image
A new image with extension .image.pbcor will be created from
the evaluation of .image / .pb for all pixels above the specified pblimit.
Note : Stand-alone PB-correction can be triggered by re-running
tclean with the appropriate imagename and with
niter=0, calcpsf=False, calcres=False, pbcor=True, vptable='vp.tab'
( where vp.tab is the name of the vpmanager file.
See the inline help for the 'vptable' parameter )
Note : Multi-term PB correction that includes a correction for the
spectral index of the PB has not been enabled for the 4.7 release.
Please use the widebandpbcor task instead.
( Wideband PB corrections are required when the amplitude of the
brightest source is known accurately enough to be sensitive
to the difference in the PB gain between the upper and lower
end of the band at its location. As a guideline, the artificial spectral
index due to the PB is -1.4 at the 0.5 gain level and less than -0.2
at the 0.9 gain level at the middle frequency )
outlierfile Name of outlier-field image definitions
A text file containing sets of parameter=value pairs,
one set per outlier field.
Example : outlierfile='outs.txt'
Contents of outs.txt :
imagename=tst1
nchan=1
imsize=[80,80]
cell=[8.0arcsec,8.0arcsec]
phasecenter=J2000 19:58:40.895 +40.55.58.543
mask=circle[[40pix,40pix],10pix]
imagename=tst2
nchan=1
imsize=[100,100]
cell=[8.0arcsec,8.0arcsec]
phasecenter=J2000 19:58:40.895 +40.56.00.000
mask=circle[[60pix,60pix],20pix]
The following parameters are currently allowed to be different between
the main field and the outlier fields (i.e. they will be recognized if found
in the outlier text file). If a parameter is not listed, the value is picked from
what is defined in the main task input.
imagename, imsize, cell, phasecenter, startmodel, mask
specmode, nchan, start, width, nterms, reffreq,
gridder, deconvolver, wprojplanes
Note : 'specmode' is an option, so combinations of mfs and cube
for different image fields, for example, are supported.
'deconvolver' and 'gridder' are also options that allow different
imaging or deconvolution algorithm per image field.
For example, multiscale with wprojection and 16 w-term planes
on the main field and mtmfs with nterms=3 and wprojection
with 64 planes on a bright outlier source for which the frequency
dependence of the primary beam produces a strong effect that
must be modeled. The traditional alternative to this approach is
to first image the outlier, subtract it out of the data (uvsub) and
then image the main field.
Note : If you encounter a use-case where some other parameter needs
to be allowed in the outlier file (and it is logical to do so), please
send us feedback. The above is an initial list.
weighting Weighting scheme (natural,uniform,briggs,superuniform,radial)
During gridding of the dirty or residual image, each visibility value is
multiplied by a weight before it is accumulated on the uv-grid.
The PSF's uv-grid is generated by gridding only the weights (weightgrid).
weighting='natural' : Gridding weights are identical to the data weights
from the MS. For visibilities with similar data weights,
the weightgrid will follow the sample density
pattern on the uv-plane. This weighting scheme
provides the maximum imaging sensitivity at the
expense of a possibly fat PSF with high sidelobes.
It is most appropriate for detection experiments
where sensitivity is most important.
weighting='uniform' : Gridding weights per visibility data point are the
original data weights divided by the total weight of
all data points that map to the same uv grid cell :
' data_weight / total_wt_per_cell '.
The weightgrid is as close to flat as possible resulting
in a PSF with a narrow main lobe and suppressed
sidelobes. However, since heavily sampled areas of
the uv-plane get down-weighted, the imaging
sensitivity is not as high as with natural weighting.
It is most appropriate for imaging experiments where
a well behaved PSF can help the reconstruction.
weighting='briggs' : Gridding weights per visibility data point are given by
'data_weight / ( A / total_wt_per_cell + B ) ' where
A and B vary according to the 'robust' parameter.
robust = -2.0 maps to A=1,B=0 or uniform weighting.
robust = +2.0 maps to natural weighting.
(robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)
Robust/Briggs weighting generates a PSF that can
vary smoothly between 'natural' and 'uniform' and
allow customized trade-offs between PSF shape and
imaging sensitivity.
weighting='superuniform' : This is similar to uniform weighting except that
the total_wt_per_cell is replaced by the
total_wt_within_NxN_cells around the uv cell of
interest. ( N = subparameter 'npixels' )
This method tends to give a PSF with inner
sidelobes that are suppressed as in uniform
weighting but with far-out sidelobes closer to
natural weighting. The peak sensitivity is also
closer to natural weighting.
weighting='radial' : Gridding weights are given by ' data_weight * uvdistance '
This method approximately minimizes rms sidelobes
for an east-west synthesis array.
For more details on weighting please see Chapter3
of Dan Briggs' thesis (http://www.aoc.nrao.edu/dissertations/dbriggs)
robust Robustness parameter for Briggs weighting.
robust = -2.0 maps to uniform weighting.
robust = +2.0 maps to natural weighting.
(robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)
npixels Number of pixels to determine uv-cell size for super-uniform weighting
(0 defaults to -/+ 3 pixels)
npixels -- uv-box used for weight calculation
a box going from -npixel/2 to +npixel/2 on each side
around a point is used to calculate weight density.
npixels=2 goes from -1 to +1 and covers 3 pixels on a side.
npixels=0 implies a single pixel, which does not make sense for
superuniform weighting. Therefore, if npixels=0 it will
be forced to 6 (or a box of -3pixels to +3pixels) to cover
7 pixels on a side.
uvtaper uv-taper on outer baselines in uv-plane
Apply a Gaussian taper in addition to the weighting scheme specified
via the 'weighting' parameter. Higher spatial frequencies are weighted
down relative to lower spatial frequencies to suppress artifacts
arising from poorly sampled areas of the uv-plane. It is equivalent to
smoothing the PSF obtained by other weighting schemes and can be
specified either as a Gaussian in uv-space (eg. units of lambda)
or as a Gaussian in the image domain (eg. angular units like arcsec).
uvtaper = [bmaj, bmin, bpa]
NOTE: the on-sky FWHM in arcsec is roughly the uv taper/200 (klambda).
default: uvtaper=[]; no Gaussian taper applied
example: uvtaper=['5klambda'] circular taper
FWHM=5 kilo-lambda
uvtaper=['5klambda','3klambda','45.0deg']
uvtaper=['10arcsec'] on-sky FWHM 10 arcseconds
uvtaper=['300.0'] default units are lambda
in aperture plane
niter Maximum number of iterations
A stopping criterion based on total iteration count.
Iterations are typically defined as the selecting one flux component
and partially subtracting it out from the residual image.
niter=0 : Do only the initial major cycle (make dirty image, psf, pb, etc)
niter larger than zero : Run major and minor cycles.
Note : Global stopping criteria vs major-cycle triggers
In addition to global stopping criteria, the following rules are
used to determine when to terminate a set of minor cycle iterations
and trigger major cycles [derived from Cotton-Schwab Clean, 1984]
'cycleniter' : controls the maximum number of iterations per image
plane before triggering a major cycle.
'cyclethreshold' : Automatically computed threshold related to the
max sidelobe level of the PSF and peak residual.
Divergence, detected as an increase of 10% in peak residual from the
minimum so far (during minor cycle iterations)
The first criterion to be satisfied takes precedence.
Note : Iteration counts for cubes or multi-field images :
For images with multiple planes (or image fields) on which the
deconvolver operates in sequence, iterations are counted across
all planes (or image fields). The iteration count is compared with
'niter' only after all channels/planes/fields have completed their
minor cycles and exited either due to 'cycleniter' or 'cyclethreshold'.
Therefore, the actual number of iterations reported in the logger
can sometimes be larger than the user specified value in 'niter'.
For example, with niter=100, cycleniter=20,nchan=10,threshold=0,
a total of 200 iterations will be done in the first set of minor cycles
before the total is compared with niter=100 and it exits.
Note : Additional global stopping criteria include
- no change in peak residual across two major cycles
- a 50% or more increase in peak residual across one major cycle
gain Loop gain
Fraction of the source flux to subtract out of the residual image
for the CLEAN algorithm and its variants.
A low value (0.2 or less) is recommended when the sky brightness
distribution is not well represented by the basis functions used by
the chosen deconvolution algorithm. A higher value can be tried when
there is a good match between the true sky brightness structure and
the basis function shapes. For example, for extended emission,
multiscale clean with an appropriate set of scale sizes will tolerate
a higher loop gain than Clark clean (for example).
threshold Stopping threshold (number in units of Jy, or string)
A global stopping threshold that the peak residual (within clean mask)
across all image planes is compared to.
threshold = 0.005 : 5mJy
threshold = '5.0mJy'
Note : A 'cyclethreshold' is internally computed and used as a major cycle
trigger. It is related what fraction of the PSF can be reliably
used during minor cycle updates of the residual image. By default
the minor cycle iterations terminate once the peak residual reaches
the first sidelobe level of the brightest source.
'cyclethreshold' is computed as follows using the settings in
parameters 'cyclefactor','minpsffraction','maxpsffraction','threshold' :
psf_fraction = max_psf_sidelobe_level * 'cyclefactor'
psf_fraction = max(psf_fraction, 'minpsffraction');
psf_fraction = min(psf_fraction, 'maxpsffraction');
cyclethreshold = peak_residual * psf_fraction
cyclethreshold = max( cyclethreshold, 'threshold' )
If nsigma is set (>0.0), the N-sigma threshold is calculated (see
the description under nsigma), then cyclethreshold is further modified as,
cyclethreshold = max( cyclethreshold, nsgima_threshold )
'cyclethreshold' is made visible and editable only in the
interactive GUI when tclean is run with interactive=True.
nsigma Multiplicative factor for rms-based threshold stopping
N-sigma threshold is calculated as nsigma * rms value per image plane determined
from a robust statistics. For nsigma > 0.0, in a minor cycle, a maximum of the two values,
the N-sigma threshold and cyclethreshold, is used to trigger a major cycle
(see also the descreption under 'threshold').
Set nsigma=0.0 to preserve the previous tclean behavior without this feature.
cycleniter Maximum number of minor-cycle iterations (per plane) before triggering
a major cycle
For example, for a single plane image, if niter=100 and cycleniter=20,
there will be 5 major cycles after the initial one (assuming there is no
threshold based stopping criterion). At each major cycle boundary, if
the number of iterations left over (to reach niter) is less than cycleniter,
it is set to the difference.
Note : cycleniter applies per image plane, even if cycleniter x nplanes
gives a total number of iterations greater than 'niter'. This is to
preserve consistency across image planes within one set of minor
cycle iterations.
cyclefactor Scaling on PSF sidelobe level to compute the minor-cycle stopping threshold.
Please refer to the Note under the documentation for 'threshold' that
discussed the calculation of 'cyclethreshold'
cyclefactor=1.0 results in a cyclethreshold at the first sidelobe level of
the brightest source in the residual image before the minor cycle starts.
cyclefactor=0.5 allows the minor cycle to go deeper.
cyclefactor=2.0 triggers a major cycle sooner.
minpsffraction PSF fraction that marks the max depth of cleaning in the minor cycle
Please refer to the Note under the documentation for 'threshold' that
discussed the calculation of 'cyclethreshold'
For example, minpsffraction=0.5 will stop cleaning at half the height of
the peak residual and trigger a major cycle earlier.
maxpsffraction PSF fraction that marks the minimum depth of cleaning in the minor cycle
Please refer to the Note under the documentation for 'threshold' that
discussed the calculation of 'cyclethreshold'
For example, maxpsffraction=0.8 will ensure that at least the top 20
percent of the source will be subtracted out in the minor cycle even if
the first PSF sidelobe is at the 0.9 level (an extreme example), or if the
cyclefactor is set too high for anything to get cleaned.
interactive Modify masks and parameters at runtime
interactive=True will trigger an interactive GUI at every major cycle
boundary (after the major cycle and before the minor cycle).
The interactive mode is currently not available for parallel cube imaging (please also
refer to the Note under the documentation for 'parallel' below).
Options for runtime parameter modification are :
Interactive clean mask : Draw a 1/0 mask (appears as a contour) by hand.
If a mask is supplied at the task interface or if
automasking is invoked, the current mask is
displayed in the GUI and is available for manual
editing.
Note : If a mask contour is not visible, please
check the cursor display at the bottom of
GUI to see which parts of the mask image
have ones and zeros. If the entire mask=1
no contours will be visible.
Operation buttons : -- Stop execution now (restore current model and exit)
-- Continue on until global stopping criteria are reached
without stopping for any more interaction
-- Continue with minor cycles and return for interaction
after the next major cycle.
Iteration control : -- max cycleniter : Trigger for the next major cycle
The display begins with
[ min( cycleniter, niter - itercount ) ]
and can be edited by hand.
-- iterations left : The display begins with [niter-itercount ]
and can be edited to increase or
decrease the total allowed niter.
-- threshold : Edit global stopping threshold
-- cyclethreshold : The display begins with the
automatically computed value
(see Note in help for 'threshold'),
and can be edited by hand.
All edits will be reflected in the log messages that appear
once minor cycles begin.
[ For scripting purposes, replacing True/False with 1/0 will get tclean to
return an imaging summary dictionary to python ]
usemask Type of mask(s) to be used for deconvolution
user: (default) mask image(s) or user specified region file(s) or string CRTF expression(s)
subparameters: mask, pbmask
pb: primary beam mask
subparameter: pbmask
Example: usemask="pb", pbmask=0.2
Construct a mask at the 0.2 pb gain level.
(Currently, this option will work only with
gridders that produce .pb (i.e. mosaic and awproject)
or if an externally produced .pb image exists on disk)
auto-multithresh : auto-masking by multiple thresholds for deconvolution
subparameters : sidelobethreshold, noisethreshold, lownoisethreshold, negativethrehsold, smoothfactor,
minbeamfrac, cutthreshold, pbmask, growiterations, dogrowprune, minpercentchange, verbose
if pbmask is >0.0, the region outside the specified pb gain level is excluded from
image statistics in determination of the threshold.
Note: By default the intermediate mask generated by automask at each deconvolution cycle
is over-written in the next cycle but one can save them by setting
the environment variable, SAVE_ALL_AUTOMASKS="true".
(e.g. in the CASA prompt, os.environ['SAVE_ALL_AUTOMASKS']="true" )
The saved CASA mask image name will be imagename.mask.autothresh#, where
# is the iteration cycle number.
mask Mask (a list of image name(s) or region file(s) or region string(s)
The name of a CASA image or region file or region string that specifies
a 1/0 mask to be used for deconvolution. Only locations with value 1 will
be considered for the centers of flux components in the minor cycle.
If regions specified fall completely outside of the image, tclean will throw an error.
Manual mask options/examples :
mask='xxx.mask' : Use this CASA image named xxx.mask and containing
ones and zeros as the mask.
If the mask is only different in spatial coordinates from what is being made
it will be resampled to the target coordinate system before being used.
The mask has to have the same shape in velocity and Stokes planes
as the output image. Exceptions are single velocity and/or single
Stokes plane masks. They will be expanded to cover all velocity and/or
Stokes planes of the output cube.
[ Note : If an error occurs during image resampling or
if the expected mask does not appear, please try
using tasks 'imregrid' or 'makemask' to resample
the mask image onto a CASA image with the target
shape and coordinates and supply it via the 'mask'
parameter. ]
mask='xxx.crtf' : A text file with region strings and the following on the first line
( #CRTFv0 CASA Region Text Format version 0 )
This is the format of a file created via the viewer's region
tool when saved in CASA region file format.
mask='circle[[40pix,40pix],10pix]' : A CASA region string.
mask=['xxx.mask','xxx.crtf', 'circle[[40pix,40pix],10pix]'] : a list of masks
Note : Mask images for deconvolution must contain 1 or 0 in each pixel.
Such a mask is different from an internal T/F mask that can be
held within each CASA image. These two types of masks are not
automatically interchangeable, so please use the makemask task
to copy between them if you need to construct a 1/0 based mask
from a T/F one.
Note : Work is in progress to generate more flexible masking options and
enable more controls.
pbmask Sub-parameter for usemask='auto-multithresh': primary beam mask
Examples : pbmask=0.0 (default, no pb mask)
pbmask=0.2 (construct a mask at the 0.2 pb gain level)
sidelobethreshold Sub-parameter for "auto-multithresh": mask threshold based on sidelobe levels: sidelobethreshold * max_sidelobe_level * peak residual
noisethreshold Sub-parameter for "auto-multithresh": mask threshold based on the noise level: noisethreshold * rms
The rms is calculated from MAD with rms = 1.4826*MAD.
lownoisethreshold Sub-parameter for "auto-multithresh": mask threshold to grow previously masked regions via binary dilation: lownoisethreshold * rms in residual image
The rms is calculated from MAD with rms = 1.4826*MAD.
negativethreshold Sub-parameter for "auto-multithresh": mask threshold for negative features: -1.0* negativethreshold * rms
The rms is calculated from MAD with rms = 1.4826*MAD.
smoothfactor Sub-parameter for "auto-multithresh": smoothing factor in a unit of the beam
minbeamfrac Sub-parameter for "auto-multithresh": minimum beam fraction in size to prune masks smaller than mimbeamfrac * beam
<=0.0 : No pruning
cutthreshold Sub-parameter for "auto-multithresh": threshold to cut the smoothed mask to create a final mask: cutthreshold * peak of the smoothed mask
growiterations Sub-parameter for "auto-multithresh": Maximum number of iterations to perform using binary dilation for growing the mask
dogrowprune Experimental sub-parameter for "auto-multithresh": Do pruning on the grow mask
minpercentchange If the change in the mask size in a particular channel is less than minpercentchange, stop masking that channel in subsequent cycles. This check is only applied when noise based threshold is used and when the previous clean major cycle had a cyclethreshold value equal to the clean threshold. Values equal to -1.0 (or any value less than 0.0) will turn off this check (the default). Automask will still stop masking if the current channel mask is an empty mask and the noise threshold was used to determine the mask.
verbose he summary of automasking at the end of each automasking process
is printed in the logger. Following information per channel will be listed in the summary.
chan: channel number
masking?: F - stop updating automask for the subsequent iteration cycles
RMS: robust rms noise
peak: peak in residual image
thresh_type: type of threshold used (noise or sidelobe)
thresh_value: the value of threshold used
N_reg: number of the automask regions
N_pruned: number of the automask regions removed by pruning
N_grow: number of the grow mask regions
N_grow_pruned: number of the grow mask regions removed by pruning
N_neg_pix: number of pixels for negative mask regions
Note that for a large cube, extra logging may slow down the process.
restart images (and start from an existing model image)
or automatically increment the image name and make a new image set.
True : Re-use existing images. If imagename.model exists the subsequent
run will start from this model (i.e. predicting it using current gridder
settings and starting from the residual image). Care must be taken
when combining this option with startmodel. Currently, only one or
the other can be used.
startmodel='', imagename.model exists :
- Start from imagename.model
startmodel='xxx', imagename.model does not exist :
- Start from startmodel
startmodel='xxx', imagename.model exists :
- Exit with an error message requesting the user to pick
only one model. This situation can arise when doing one
run with startmodel='xxx' to produce an output
imagename.model that includes the content of startmodel,
and wanting to restart a second run to continue deconvolution.
Startmodel should be set to '' before continuing.
If any change in the shape or coordinate system of the image is
desired during the restart, please change the image name and
use the startmodel (and mask) parameter(s) so that the old model
(and mask) can be regridded to the new coordinate system before starting.
False : A convenience feature to increment imagename with '_1', '_2',
etc as suffixes so that all runs of tclean are fresh starts (without
having to change the imagename parameter or delete images).
This mode will search the current directory for all existing
imagename extensions, pick the maximum, and adds 1.
For imagename='try' it will make try.psf, try_2.psf, try_3.psf, etc.
This also works if you specify a directory name in the path :
imagename='outdir/try'. If './outdir' does not exist, it will create it.
Then it will search for existing filenames inside that directory.
If outlier fields are specified, the incrementing happens for each
of them (since each has its own 'imagename'). The counters are
synchronized across imagefields, to make it easier to match up sets
of output images. It adds 1 to the 'max id' from all outlier names
on disk. So, if you do two runs with only the main field
(imagename='try'), and in the third run you add an outlier with
imagename='outtry', you will get the following image names
for the third run : 'try_3' and 'outtry_3' even though
'outry' and 'outtry_2' have not been used.
savemodel Options to save model visibilities (none, virtual, modelcolumn)
Often, model visibilities must be created and saved in the MS
to be later used for self-calibration (or to just plot and view them).
none : Do not save any model visibilities in the MS. The MS is opened
in readonly mode.
Model visibilities can be predicted in a separate step by
restarting tclean with niter=0,savemodel=virtual or modelcolumn
and not changing any image names so that it finds the .model on
disk (or by changing imagename and setting startmodel to the
original imagename).
virtual : In the last major cycle, save the image model and state of the
gridder used during imaging within the SOURCE subtable of the
MS. Images required for de-gridding will also be stored internally.
All future references to model visibilities will activate the
(de)gridder to compute them on-the-fly. This mode is useful
when the dataset is large enough that an additional model data
column on disk may be too much extra disk I/O, when the
gridder is simple enough that on-the-fly recomputing of the
model visibilities is quicker than disk I/O.
modelcolumn : In the last major cycle, save predicted model visibilities
in the MODEL_DATA column of the MS. This mode is useful when
the de-gridding cost to produce the model visibilities is higher
than the I/O required to read the model visibilities from disk.
This mode is currently required for gridder='awproject'.
This mode is also required for the ability to later pull out
model visibilities from the MS into a python array for custom
processing.
Note 1 : The imagename.model image on disk will always be constructed
if the minor cycle runs. This savemodel parameter applies only to
model visibilities created by de-gridding the model image.
Note 2 : It is possible for an MS to have both a virtual model
as well as a model_data column, but under normal operation,
the last used mode will get triggered. Use the delmod task to
clear out existing models from an MS if confusion arises.
calcres Calculate initial residual image
This parameter controls what the first major cycle does.
calcres=False with niter greater than 0 will assume that
a .residual image already exists and that the minor cycle can
begin without recomputing it.
calcres=False with niter=0 implies that only the PSF will be made
and no data will be gridded.
calcres=True requires that calcpsf=True or that the .psf and .sumwt
images already exist on disk (for normalization purposes).
Usage example : For large runs (or a pipeline scripts) it may be
useful to first run tclean with niter=0 to create
an initial .residual to look at and perhaps make
a custom mask for. Imaging can be resumed
without recomputing it.
calcpsf Calculate PSF
This parameter controls what the first major cycle does.
calcpsf=False will assume that a .psf image already exists
and that the minor cycle can begin without recomputing it.
parallel Run major cycles in parallel (this feature is experimental)
Parallel tclean will run only if casa has already been started using mpirun.
Please refer to HPC documentation for details on how to start this on your system.
Example : mpirun -n 3 -xterm 0 `which casa`
Continuum Imaging :
- Data are partitioned (in time) into NProc pieces
- Gridding/iFT is done separately per partition
- Images (and weights) are gathered and then normalized
- One non-parallel minor cycle is run
- Model image is scattered to all processes
- Major cycle is done in parallel per partition
Cube Imaging :
- Data and Image coordinates are partitioned (in freq) into NProc pieces
- Each partition is processed independently (major and minor cycles)
- All processes are synchronized at major cycle boundaries for convergence checks
- At the end, cubes from all partitions are concatenated along the spectral axis
Note 1 : Iteration control for cube imaging is independent per partition.
- There is currently no communication between them to synchronize
information such as peak residual and cyclethreshold. Therefore,
different chunks may trigger major cycles at different levels.
- For cube imaging in parallel, there is currently no interactive masking.
(Proper synchronization of iteration control is work in progress.)
[1;42mRETURNS[1;m void
--------- examples -----------------------------------------------------------
This is the first release of our refactored imager code. Although most features have
been used and validated, there are many details that have not been thoroughly tested.
Feedback will be much appreciated.
Usage Examples :
-----------------------
(A) A suite of test programs that demo all usable modes of tclean on small test datasets
https://svn.cv.nrao.edu/svn/casa/branches/release-4_5/gcwrap/python/scripts/tests/test_refimager.py
(B) A set of demo examples for ALMA imaging
https://casaguides.nrao.edu/index.php/TCLEAN_and_ALMA
"""
[docs]
_info_group_ = """imaging"""
[docs]
_info_desc_ = """Parallelized tclean in consecutive time steps"""
[docs]
__schema = {'vis': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'imageprefix': {'type': 'cStr', 'coerce': _coerce.to_str}, 'imagesuffix': {'type': 'cStr', 'coerce': _coerce.to_str}, 'ncpu': {'type': 'cInt'}, 'twidth': {'type': 'cInt'}, 'doreg': {'type': 'cBool'}, 'usephacenter': {'type': 'cBool'}, 'reftime': {'type': 'cStr', 'coerce': _coerce.to_str}, 'toTb': {'type': 'cBool'}, 'sclfactor': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'subregion': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'docompress': {'type': 'cBool'}, 'overwrite': {'type': 'cBool'}, 'selectdata': {'type': 'cBool'}, 'field': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'spw': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'timerange': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'uvrange': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'antenna': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'scan': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'observation': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cInt'}]}, 'intent': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'datacolumn': {'type': 'cStr', 'coerce': _coerce.to_str}, 'imsize': {'anyof': [{'type': 'cInt'}, {'type': 'cIntVec', 'coerce': [_coerce.to_list,_coerce.to_intvec]}]}, 'cell': {'anyof': [{'type': 'cIntVec', 'coerce': [_coerce.to_list,_coerce.to_intvec]}, {'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cFloat', 'coerce': _coerce.to_float}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}, {'type': 'cInt'}, {'type': 'cFloatVec', 'coerce': [_coerce.to_list,_coerce.to_floatvec]}]}, 'phasecenter': {'anyof': [{'type': 'cInt'}, {'type': 'cStr', 'coerce': _coerce.to_str}]}, 'stokes': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'I', 'IQUV', 'UV', 'RRLL', 'IQ', 'V', 'pseudoI', 'QU', 'YY', 'RR', 'Q', 'U', 'IV', 'XX', 'XXYY', 'LL' ]}, 'projection': {'type': 'cStr', 'coerce': _coerce.to_str}, 'startmodel': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'specmode': {'type': 'cVariant', 'coerce': [_coerce.to_variant] # <allowed> IS NOT ALLOWED FOR A PARAMETER OF TYPE any
}, 'reffreq': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'nchan': {'type': 'cInt'}, 'start': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'width': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'outframe': {'type': 'cStr', 'coerce': _coerce.to_str}, 'veltype': {'type': 'cStr', 'coerce': _coerce.to_str}, 'restfreq': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'interpolation': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'nearest', 'linear', 'cubic' ]}, 'gridder': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'widefield', 'wproject', 'imagemosaic', 'standard', 'awproject', 'wprojectft', 'mosaicft', 'ft', 'ftmosaic', 'mosaic', 'awprojectft', 'gridft' ]}, 'facets': {'type': 'cInt'}, 'chanchunks': {'type': 'cInt'}, 'wprojplanes': {'type': 'cInt'}, 'vptable': {'type': 'cStr', 'coerce': _coerce.to_str}, 'usepointing': {'type': 'cBool'}, 'mosweight': {'type': 'cBool'}, 'aterm': {'type': 'cBool'}, 'psterm': {'type': 'cBool'}, 'wbawp': {'type': 'cBool'}, 'conjbeams': {'type': 'cBool'}, 'cfcache': {'type': 'cStr', 'coerce': _coerce.to_str}, 'computepastep': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'rotatepastep': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'pblimit': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'normtype': {'type': 'cStr', 'coerce': _coerce.to_str}, 'deconvolver': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'clarkstokes_exp', 'mtmfs', 'mem', 'clarkstokes', 'hogbom', 'clark_exp', 'clark', 'multiscale' ]}, 'scales': {'anyof': [{'type': 'cIntVec', 'coerce': [_coerce.to_list,_coerce.to_intvec]}, {'type': 'cFloatVec', 'coerce': [_coerce.to_list,_coerce.to_floatvec]}]}, 'nterms': {'type': 'cInt'}, 'smallscalebias': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'restoration': {'type': 'cBool'}, 'restoringbeam': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'pbcor': {'type': 'cBool'}, 'outlierfile': {'type': 'cStr', 'coerce': _coerce.to_str}, 'weighting': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'briggs', 'natural', 'radial', 'superuniform', 'uniform' ]}, 'robust': {'type': 'cFloat', 'coerce': _coerce.to_float, 'min': -2.0, 'max': 2.0}, 'npixels': {'type': 'cInt'}, 'uvtaper': {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}, 'niter': {'type': 'cInt'}, 'gain': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'threshold': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'nsigma': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'cycleniter': {'type': 'cInt'}, 'cyclefactor': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'minpsffraction': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'maxpsffraction': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'interactive': {'anyof': [{'type': 'cBool'}, {'type': 'cInt'}]}, 'usemask': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'user', 'pb', 'auto-multithresh' ]}, 'mask': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'pbmask': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'sidelobethreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'noisethreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'lownoisethreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'negativethreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'smoothfactor': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'minbeamfrac': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'cutthreshold': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'growiterations': {'type': 'cInt'}, 'dogrowprune': {'type': 'cBool'}, 'minpercentchange': {'type': 'cFloat', 'coerce': _coerce.to_float}, 'verbose': {'type': 'cBool'}, 'restart': {'type': 'cBool'}, 'savemodel': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'none', 'virtual', 'modelcolumn' ]}, 'calcres': {'type': 'cBool'}, 'calcpsf': {'type': 'cBool'}, 'parallel': {'type': 'cBool'}}
def __init__(self):
[docs]
self.__root_frame_ = None
[docs]
def __globals_(self):
if self.__root_frame_ is None:
self.__root_frame_ = _find_frame( )
assert self.__root_frame_ is not None, "could not find CASAshell global frame"
return self.__root_frame_
[docs]
def __to_string_(self,value):
if type(value) is str:
return "'%s'" % value
else:
return str(value)
[docs]
def __validate_(self,doc,schema):
return _pc.validate(doc,schema)
[docs]
def __do_inp_output(self,param_prefix,description_str,formatting_chars):
out = self.__stdout or sys.stdout
description = description_str.split( )
prefix_width = 23 + 20 + 4
output = [ ]
addon = ''
first_addon = True
while len(description) > 0:
## starting a new line.....................................................................
if len(output) == 0:
## for first line add parameter information............................................
if len(param_prefix)-formatting_chars > prefix_width - 1:
output.append(param_prefix)
continue
addon = param_prefix + ' #'
first_addon = True
addon_formatting = formatting_chars
else:
## for subsequent lines space over prefix width........................................
addon = (' ' * prefix_width) + '#'
first_addon = False
addon_formatting = 0
## if first word of description puts us over the screen width, bail........................
if len(addon + description[0]) - addon_formatting + 1 > self.term_width:
## if we're doing the first line make sure it's output.................................
if first_addon: output.append(addon)
break
while len(description) > 0:
## if the next description word puts us over break for the next line...................
if len(addon + description[0]) - addon_formatting + 1 > self.term_width: break
addon = addon + ' ' + description[0]
description.pop(0)
output.append(addon)
out.write('\n'.join(output) + '\n')
#--------- return nonsubparam values ----------------------------------------------
[docs]
def __phasecenter_dflt( self, glb ):
return ''
[docs]
def __phasecenter( self, glb ):
if 'phasecenter' in glb: return glb['phasecenter']
return ''
[docs]
def __projection_dflt( self, glb ):
return 'SIN'
[docs]
def __projection( self, glb ):
if 'projection' in glb: return glb['projection']
return 'SIN'
[docs]
def __vis_dflt( self, glb ):
return ''
[docs]
def __vis( self, glb ):
if 'vis' in glb: return glb['vis']
return ''
[docs]
def __imagesuffix_dflt( self, glb ):
return ''
[docs]
def __imagesuffix( self, glb ):
if 'imagesuffix' in glb: return glb['imagesuffix']
return ''
[docs]
def __parallel_dflt( self, glb ):
return False
[docs]
def __parallel( self, glb ):
if 'parallel' in glb: return glb['parallel']
return False
[docs]
def __twidth_dflt( self, glb ):
return int(1)
[docs]
def __twidth( self, glb ):
if 'twidth' in glb: return glb['twidth']
return int(1)
[docs]
def __datacolumn_dflt( self, glb ):
return 'corrected'
[docs]
def __datacolumn( self, glb ):
if 'datacolumn' in glb: return glb['datacolumn']
return 'corrected'
[docs]
def __restart_dflt( self, glb ):
return True
[docs]
def __restart( self, glb ):
if 'restart' in glb: return glb['restart']
return True
[docs]
def __cell_dflt( self, glb ):
return [ ]
[docs]
def __cell( self, glb ):
if 'cell' in glb: return glb['cell']
return [ ]
[docs]
def __startmodel_dflt( self, glb ):
return ''
[docs]
def __startmodel( self, glb ):
if 'startmodel' in glb: return glb['startmodel']
return ''
[docs]
def __deconvolver_dflt( self, glb ):
return 'hogbom'
[docs]
def __deconvolver( self, glb ):
if 'deconvolver' in glb: return glb['deconvolver']
return 'hogbom'
[docs]
def __calcpsf_dflt( self, glb ):
return True
[docs]
def __calcpsf( self, glb ):
if 'calcpsf' in glb: return glb['calcpsf']
return True
[docs]
def __niter_dflt( self, glb ):
return int(0)
[docs]
def __niter( self, glb ):
if 'niter' in glb: return glb['niter']
return int(0)
[docs]
def __selectdata_dflt( self, glb ):
return True
[docs]
def __selectdata( self, glb ):
if 'selectdata' in glb: return glb['selectdata']
return True
[docs]
def __imageprefix_dflt( self, glb ):
return ''
[docs]
def __imageprefix( self, glb ):
if 'imageprefix' in glb: return glb['imageprefix']
return ''
[docs]
def __imsize_dflt( self, glb ):
return [ ]
[docs]
def __imsize( self, glb ):
if 'imsize' in glb: return glb['imsize']
return [ ]
[docs]
def __outlierfile_dflt( self, glb ):
return ''
[docs]
def __outlierfile( self, glb ):
if 'outlierfile' in glb: return glb['outlierfile']
return ''
[docs]
def __calcres_dflt( self, glb ):
return True
[docs]
def __calcres( self, glb ):
if 'calcres' in glb: return glb['calcres']
return True
[docs]
def __ncpu_dflt( self, glb ):
return int(8)
[docs]
def __ncpu( self, glb ):
if 'ncpu' in glb: return glb['ncpu']
return int(8)
[docs]
def __savemodel_dflt( self, glb ):
return 'none'
[docs]
def __savemodel( self, glb ):
if 'savemodel' in glb: return glb['savemodel']
return 'none'
[docs]
def __usemask_dflt( self, glb ):
return 'user'
[docs]
def __usemask( self, glb ):
if 'usemask' in glb: return glb['usemask']
return 'user'
[docs]
def __specmode_dflt( self, glb ):
return 'mfs'
[docs]
def __specmode( self, glb ):
if 'specmode' in glb: return glb['specmode']
return 'mfs'
[docs]
def __restoration_dflt( self, glb ):
return True
[docs]
def __restoration( self, glb ):
if 'restoration' in glb: return glb['restoration']
return True
[docs]
def __stokes_dflt( self, glb ):
return 'I'
[docs]
def __stokes( self, glb ):
if 'stokes' in glb: return glb['stokes']
return 'I'
[docs]
def __weighting_dflt( self, glb ):
return 'natural'
[docs]
def __weighting( self, glb ):
if 'weighting' in glb: return glb['weighting']
return 'natural'
[docs]
def __gridder_dflt( self, glb ):
return 'standard'
[docs]
def __gridder( self, glb ):
if 'gridder' in glb: return glb['gridder']
return 'standard'
[docs]
def __overwrite_dflt( self, glb ):
return False
[docs]
def __overwrite( self, glb ):
if 'overwrite' in glb: return glb['overwrite']
return False
[docs]
def __doreg_dflt( self, glb ):
return False
[docs]
def __doreg( self, glb ):
if 'doreg' in glb: return glb['doreg']
return False
#--------- return inp/go default --------------------------------------------------
[docs]
def __chanchunks_dflt( self, glb ):
if self.__specmode( glb ) == "cube": return int(1)
if self.__specmode( glb ) == "cubesource": return int(1)
if self.__specmode( glb ) == "cubedata": return int(1)
return None
[docs]
def __antenna_dflt( self, glb ):
if self.__selectdata( glb ) == bool(True): return ""
return None
[docs]
def __smoothfactor_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return float(1.0)
return None
[docs]
def __negativethreshold_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return float(0.0)
return None
[docs]
def __minbeamfrac_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return float(0.3)
return None
[docs]
def __mask_dflt( self, glb ):
if self.__usemask( glb ) == "user": return ""
return None
[docs]
def __sclfactor_dflt( self, glb ):
if self.__doreg( glb ) == bool(True): return float(1.0)
return None
[docs]
def __field_dflt( self, glb ):
if self.__selectdata( glb ) == bool(True): return ""
return None
[docs]
def __cutthreshold_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return float(0.01)
return None
[docs]
def __pblimit_dflt( self, glb ):
if self.__gridder( glb ) == "standard": return float(0.2)
if self.__gridder( glb ) == "widefield": return float(0.2)
if self.__gridder( glb ) == "wproject": return float(0.2)
if self.__gridder( glb ) == "wprojectft": return float(0.2)
if self.__gridder( glb ) == "mosaic": return float(0.2)
if self.__gridder( glb ) == "mosaicft": return float(0.2)
if self.__gridder( glb ) == "ftmosaic": return float(0.2)
if self.__gridder( glb ) == "imagemosaic": return float(0.2)
if self.__gridder( glb ) == "awproject": return float(0.2)
if self.__gridder( glb ) == "awprojectft": return float(0.2)
return None
[docs]
def __smallscalebias_dflt( self, glb ):
if self.__deconvolver( glb ) == "multiscale": return float(0.6)
return None
[docs]
def __maxpsffraction_dflt( self, glb ):
if self.__niter( glb ) != int(0): return float(0.8)
return None
[docs]
def __verbose_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return bool(False)
return None
[docs]
def __intent_dflt( self, glb ):
if self.__selectdata( glb ) == bool(True): return ""
return None
[docs]
def __interpolation_dflt( self, glb ):
if self.__specmode( glb ) == "cube": return "linear"
if self.__specmode( glb ) == "cubesource": return "linear"
if self.__specmode( glb ) == "cubedata": return "linear"
return None
[docs]
def __subregion_dflt( self, glb ):
if self.__doreg( glb ) == bool(True): return ""
return None
[docs]
def __nterms_dflt( self, glb ):
if self.__deconvolver( glb ) == "mtmfs": return int(2)
return None
[docs]
def __nchan_dflt( self, glb ):
if self.__specmode( glb ) == "cube": return int(-1)
if self.__specmode( glb ) == "cubesource": return int(-1)
if self.__specmode( glb ) == "cubedata": return int(-1)
return None
[docs]
def __reffreq_dflt( self, glb ):
if self.__specmode( glb ) == "mfs": return ""
return None
[docs]
def __conjbeams_dflt( self, glb ):
if self.__gridder( glb ) == "mosaic": return bool(True)
if self.__gridder( glb ) == "mosaicft": return bool(True)
if self.__gridder( glb ) == "awproject": return bool(False)
if self.__gridder( glb ) == "awprojectft": return bool(False)
return None
[docs]
def __restoringbeam_dflt( self, glb ):
if self.__restoration( glb ) == bool(True): return []
return None
[docs]
def __sidelobethreshold_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return float(3.0)
return None
[docs]
def __reftime_dflt( self, glb ):
if self.__doreg( glb ) == bool(True): return ""
return None
[docs]
def __cycleniter_dflt( self, glb ):
if self.__niter( glb ) != int(0): return int(-1)
return None
[docs]
def __minpsffraction_dflt( self, glb ):
if self.__niter( glb ) != int(0): return float(0.05)
return None
[docs]
def __scan_dflt( self, glb ):
if self.__selectdata( glb ) == bool(True): return ""
return None
[docs]
def __computepastep_dflt( self, glb ):
if self.__gridder( glb ) == "awproject": return float(360.0)
if self.__gridder( glb ) == "awprojectft": return float(360.0)
return None
[docs]
def __minpercentchange_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return float(-1.0)
return None
[docs]
def __wbawp_dflt( self, glb ):
if self.__gridder( glb ) == "awproject": return bool(False)
if self.__gridder( glb ) == "awprojectft": return bool(False)
return None
[docs]
def __docompress_dflt( self, glb ):
if self.__doreg( glb ) == bool(True): return bool(False)
return None
[docs]
def __interactive_dflt( self, glb ):
if self.__niter( glb ) != int(0): return False
return None
[docs]
def __npixels_dflt( self, glb ):
if self.__weighting( glb ) == "briggs": return int(0)
return None
[docs]
def __mosweight_dflt( self, glb ):
if self.__gridder( glb ) == "mosaic": return bool(True)
if self.__gridder( glb ) == "mosaicft": return bool(True)
if self.__gridder( glb ) == "ftmosaic": return bool(True)
return None
[docs]
def __pbcor_dflt( self, glb ):
if self.__restoration( glb ) == bool(True): return bool(False)
return None
[docs]
def __normtype_dflt( self, glb ):
if self.__gridder( glb ) == "mosaic": return "flatnoise"
if self.__gridder( glb ) == "mosaicft": return "flatnoise"
if self.__gridder( glb ) == "ftmosaic": return "flatnoise"
if self.__gridder( glb ) == "imagemosaic": return "flatnoise"
if self.__gridder( glb ) == "awproject": return "flatnoise"
if self.__gridder( glb ) == "awprojectft": return "flatnoise"
return None
[docs]
def __uvtaper_dflt( self, glb ):
if self.__weighting( glb ) == "natural": return []
if self.__weighting( glb ) == "briggs": return []
return None
[docs]
def __cyclefactor_dflt( self, glb ):
if self.__niter( glb ) != int(0): return float(1.0)
return None
[docs]
def __toTb_dflt( self, glb ):
if self.__doreg( glb ) == bool(True): return bool(False)
return None
[docs]
def __restfreq_dflt( self, glb ):
if self.__specmode( glb ) == "cube": return []
if self.__specmode( glb ) == "cubesource": return []
if self.__specmode( glb ) == "cubedata": return []
return None
[docs]
def __pbmask_dflt( self, glb ):
if self.__usemask( glb ) == "user": return float(0.0)
if self.__usemask( glb ) == "pb": return float(0.2)
if self.__usemask( glb ) == "auto-multithresh": return float(0.2)
return None
[docs]
def __growiterations_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return int(75)
return None
[docs]
def __gain_dflt( self, glb ):
if self.__niter( glb ) != int(0): return float(0.1)
return None
[docs]
def __scales_dflt( self, glb ):
if self.__deconvolver( glb ) == "multiscale": return []
if self.__deconvolver( glb ) == "mtmfs": return []
return None
[docs]
def __robust_dflt( self, glb ):
if self.__weighting( glb ) == "briggs": return float(0.5)
return None
[docs]
def __vptable_dflt( self, glb ):
if self.__gridder( glb ) == "standard": return ""
if self.__gridder( glb ) == "widefield": return ""
if self.__gridder( glb ) == "wproject": return ""
if self.__gridder( glb ) == "wprojectft": return ""
if self.__gridder( glb ) == "mosaic": return ""
if self.__gridder( glb ) == "mosaicft": return ""
if self.__gridder( glb ) == "ftmosaic": return ""
if self.__gridder( glb ) == "imagemosaic": return ""
return None
[docs]
def __aterm_dflt( self, glb ):
if self.__gridder( glb ) == "awproject": return bool(True)
if self.__gridder( glb ) == "awprojectft": return bool(True)
return None
[docs]
def __usephacenter_dflt( self, glb ):
if self.__doreg( glb ) == bool(True): return bool(True)
return None
[docs]
def __usepointing_dflt( self, glb ):
if self.__gridder( glb ) == "mosaic": return bool(False)
if self.__gridder( glb ) == "mosaicft": return bool(False)
if self.__gridder( glb ) == "ftmosaic": return bool(False)
return None
[docs]
def __rotatepastep_dflt( self, glb ):
if self.__gridder( glb ) == "awproject": return float(360.0)
if self.__gridder( glb ) == "awprojectft": return float(360.0)
return None
[docs]
def __threshold_dflt( self, glb ):
if self.__niter( glb ) != int(0): return 0.0
return None
[docs]
def __veltype_dflt( self, glb ):
if self.__specmode( glb ) == "cube": return "radio"
if self.__specmode( glb ) == "cubesource": return "radio"
if self.__specmode( glb ) == "cubedata": return "radio"
return None
[docs]
def __outframe_dflt( self, glb ):
if self.__specmode( glb ) == "cube": return ""
if self.__specmode( glb ) == "cubesource": return "REST"
return None
[docs]
def __dogrowprune_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return bool(True)
return None
[docs]
def __uvrange_dflt( self, glb ):
if self.__selectdata( glb ) == bool(True): return ""
return None
[docs]
def __psterm_dflt( self, glb ):
if self.__gridder( glb ) == "awproject": return bool(False)
if self.__gridder( glb ) == "awprojectft": return bool(False)
return None
[docs]
def __start_dflt( self, glb ):
if self.__specmode( glb ) == "cube": return ""
if self.__specmode( glb ) == "cubesource": return ""
if self.__specmode( glb ) == "cubedata": return ""
return None
[docs]
def __observation_dflt( self, glb ):
if self.__selectdata( glb ) == bool(True): return ""
return None
[docs]
def __lownoisethreshold_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return float(1.5)
return None
[docs]
def __facets_dflt( self, glb ):
if self.__gridder( glb ) == "widefield": return int(1)
return None
[docs]
def __noisethreshold_dflt( self, glb ):
if self.__usemask( glb ) == "auto-multithresh": return float(5.0)
return None
[docs]
def __width_dflt( self, glb ):
if self.__specmode( glb ) == "cube": return ""
if self.__specmode( glb ) == "cubesource": return ""
if self.__specmode( glb ) == "cubedata": return ""
return None
[docs]
def __spw_dflt( self, glb ):
if self.__selectdata( glb ) == bool(True): return ""
return None
[docs]
def __timerange_dflt( self, glb ):
if self.__selectdata( glb ) == bool(True): return ""
return None
[docs]
def __nsigma_dflt( self, glb ):
if self.__niter( glb ) != int(0): return float(0.0)
return None
[docs]
def __cfcache_dflt( self, glb ):
if self.__gridder( glb ) == "awproject": return ""
if self.__gridder( glb ) == "awprojectft": return ""
return None
[docs]
def __wprojplanes_dflt( self, glb ):
if self.__gridder( glb ) == "widefield": return int(1)
if self.__gridder( glb ) == "wproject": return int(1)
if self.__gridder( glb ) == "wprojectft": return int(1)
if self.__gridder( glb ) == "imagemosaic": return int(1)
if self.__gridder( glb ) == "awproject": return int(1)
if self.__gridder( glb ) == "awprojectft": return int(1)
return None
#--------- return subparam values -------------------------------------------------
[docs]
def __usephacenter( self, glb ):
if 'usephacenter' in glb: return glb['usephacenter']
dflt = self.__usephacenter_dflt( glb )
if dflt is not None: return dflt
return True
[docs]
def __reftime( self, glb ):
if 'reftime' in glb: return glb['reftime']
dflt = self.__reftime_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __toTb( self, glb ):
if 'toTb' in glb: return glb['toTb']
dflt = self.__toTb_dflt( glb )
if dflt is not None: return dflt
return False
[docs]
def __sclfactor( self, glb ):
if 'sclfactor' in glb: return glb['sclfactor']
dflt = self.__sclfactor_dflt( glb )
if dflt is not None: return dflt
return float(1.0)
[docs]
def __subregion( self, glb ):
if 'subregion' in glb: return glb['subregion']
dflt = self.__subregion_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __docompress( self, glb ):
if 'docompress' in glb: return glb['docompress']
dflt = self.__docompress_dflt( glb )
if dflt is not None: return dflt
return False
[docs]
def __field( self, glb ):
if 'field' in glb: return glb['field']
dflt = self.__field_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __spw( self, glb ):
if 'spw' in glb: return glb['spw']
dflt = self.__spw_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __timerange( self, glb ):
if 'timerange' in glb: return glb['timerange']
dflt = self.__timerange_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __uvrange( self, glb ):
if 'uvrange' in glb: return glb['uvrange']
dflt = self.__uvrange_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __antenna( self, glb ):
if 'antenna' in glb: return glb['antenna']
dflt = self.__antenna_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __scan( self, glb ):
if 'scan' in glb: return glb['scan']
dflt = self.__scan_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __observation( self, glb ):
if 'observation' in glb: return glb['observation']
dflt = self.__observation_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __intent( self, glb ):
if 'intent' in glb: return glb['intent']
dflt = self.__intent_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __reffreq( self, glb ):
if 'reffreq' in glb: return glb['reffreq']
dflt = self.__reffreq_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __nchan( self, glb ):
if 'nchan' in glb: return glb['nchan']
dflt = self.__nchan_dflt( glb )
if dflt is not None: return dflt
return int(-1)
[docs]
def __start( self, glb ):
if 'start' in glb: return glb['start']
dflt = self.__start_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __width( self, glb ):
if 'width' in glb: return glb['width']
dflt = self.__width_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __outframe( self, glb ):
if 'outframe' in glb: return glb['outframe']
dflt = self.__outframe_dflt( glb )
if dflt is not None: return dflt
return 'LSRK'
[docs]
def __veltype( self, glb ):
if 'veltype' in glb: return glb['veltype']
dflt = self.__veltype_dflt( glb )
if dflt is not None: return dflt
return 'radio'
[docs]
def __restfreq( self, glb ):
if 'restfreq' in glb: return glb['restfreq']
dflt = self.__restfreq_dflt( glb )
if dflt is not None: return dflt
return [ ]
[docs]
def __interpolation( self, glb ):
if 'interpolation' in glb: return glb['interpolation']
dflt = self.__interpolation_dflt( glb )
if dflt is not None: return dflt
return 'linear'
[docs]
def __facets( self, glb ):
if 'facets' in glb: return glb['facets']
dflt = self.__facets_dflt( glb )
if dflt is not None: return dflt
return int(1)
[docs]
def __chanchunks( self, glb ):
if 'chanchunks' in glb: return glb['chanchunks']
dflt = self.__chanchunks_dflt( glb )
if dflt is not None: return dflt
return int(1)
[docs]
def __wprojplanes( self, glb ):
if 'wprojplanes' in glb: return glb['wprojplanes']
dflt = self.__wprojplanes_dflt( glb )
if dflt is not None: return dflt
return int(1)
[docs]
def __vptable( self, glb ):
if 'vptable' in glb: return glb['vptable']
dflt = self.__vptable_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __usepointing( self, glb ):
if 'usepointing' in glb: return glb['usepointing']
dflt = self.__usepointing_dflt( glb )
if dflt is not None: return dflt
return False
[docs]
def __mosweight( self, glb ):
if 'mosweight' in glb: return glb['mosweight']
dflt = self.__mosweight_dflt( glb )
if dflt is not None: return dflt
return True
[docs]
def __aterm( self, glb ):
if 'aterm' in glb: return glb['aterm']
dflt = self.__aterm_dflt( glb )
if dflt is not None: return dflt
return True
[docs]
def __psterm( self, glb ):
if 'psterm' in glb: return glb['psterm']
dflt = self.__psterm_dflt( glb )
if dflt is not None: return dflt
return False
[docs]
def __wbawp( self, glb ):
if 'wbawp' in glb: return glb['wbawp']
dflt = self.__wbawp_dflt( glb )
if dflt is not None: return dflt
return True
[docs]
def __conjbeams( self, glb ):
if 'conjbeams' in glb: return glb['conjbeams']
dflt = self.__conjbeams_dflt( glb )
if dflt is not None: return dflt
return True
[docs]
def __cfcache( self, glb ):
if 'cfcache' in glb: return glb['cfcache']
dflt = self.__cfcache_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __computepastep( self, glb ):
if 'computepastep' in glb: return glb['computepastep']
dflt = self.__computepastep_dflt( glb )
if dflt is not None: return dflt
return float(360.0)
[docs]
def __rotatepastep( self, glb ):
if 'rotatepastep' in glb: return glb['rotatepastep']
dflt = self.__rotatepastep_dflt( glb )
if dflt is not None: return dflt
return float(360.0)
[docs]
def __pblimit( self, glb ):
if 'pblimit' in glb: return glb['pblimit']
dflt = self.__pblimit_dflt( glb )
if dflt is not None: return dflt
return float(0.2)
[docs]
def __normtype( self, glb ):
if 'normtype' in glb: return glb['normtype']
dflt = self.__normtype_dflt( glb )
if dflt is not None: return dflt
return 'flatnoise'
[docs]
def __scales( self, glb ):
if 'scales' in glb: return glb['scales']
dflt = self.__scales_dflt( glb )
if dflt is not None: return dflt
return [ ]
[docs]
def __nterms( self, glb ):
if 'nterms' in glb: return glb['nterms']
dflt = self.__nterms_dflt( glb )
if dflt is not None: return dflt
return int(2)
[docs]
def __smallscalebias( self, glb ):
if 'smallscalebias' in glb: return glb['smallscalebias']
dflt = self.__smallscalebias_dflt( glb )
if dflt is not None: return dflt
return float(0.6)
[docs]
def __restoringbeam( self, glb ):
if 'restoringbeam' in glb: return glb['restoringbeam']
dflt = self.__restoringbeam_dflt( glb )
if dflt is not None: return dflt
return [ ]
[docs]
def __pbcor( self, glb ):
if 'pbcor' in glb: return glb['pbcor']
dflt = self.__pbcor_dflt( glb )
if dflt is not None: return dflt
return False
[docs]
def __robust( self, glb ):
if 'robust' in glb: return glb['robust']
dflt = self.__robust_dflt( glb )
if dflt is not None: return dflt
return float(0.5)
[docs]
def __npixels( self, glb ):
if 'npixels' in glb: return glb['npixels']
dflt = self.__npixels_dflt( glb )
if dflt is not None: return dflt
return int(0)
[docs]
def __uvtaper( self, glb ):
if 'uvtaper' in glb: return glb['uvtaper']
dflt = self.__uvtaper_dflt( glb )
if dflt is not None: return dflt
return [ '' ]
[docs]
def __gain( self, glb ):
if 'gain' in glb: return glb['gain']
dflt = self.__gain_dflt( glb )
if dflt is not None: return dflt
return float(0.1)
[docs]
def __threshold( self, glb ):
if 'threshold' in glb: return glb['threshold']
dflt = self.__threshold_dflt( glb )
if dflt is not None: return dflt
return float(0.0)
[docs]
def __nsigma( self, glb ):
if 'nsigma' in glb: return glb['nsigma']
dflt = self.__nsigma_dflt( glb )
if dflt is not None: return dflt
return float(0.0)
[docs]
def __cycleniter( self, glb ):
if 'cycleniter' in glb: return glb['cycleniter']
dflt = self.__cycleniter_dflt( glb )
if dflt is not None: return dflt
return int(-1)
[docs]
def __cyclefactor( self, glb ):
if 'cyclefactor' in glb: return glb['cyclefactor']
dflt = self.__cyclefactor_dflt( glb )
if dflt is not None: return dflt
return float(1.0)
[docs]
def __minpsffraction( self, glb ):
if 'minpsffraction' in glb: return glb['minpsffraction']
dflt = self.__minpsffraction_dflt( glb )
if dflt is not None: return dflt
return float(0.05)
[docs]
def __maxpsffraction( self, glb ):
if 'maxpsffraction' in glb: return glb['maxpsffraction']
dflt = self.__maxpsffraction_dflt( glb )
if dflt is not None: return dflt
return float(0.8)
[docs]
def __interactive( self, glb ):
if 'interactive' in glb: return glb['interactive']
dflt = self.__interactive_dflt( glb )
if dflt is not None: return dflt
return False
[docs]
def __mask( self, glb ):
if 'mask' in glb: return glb['mask']
dflt = self.__mask_dflt( glb )
if dflt is not None: return dflt
return ''
[docs]
def __pbmask( self, glb ):
if 'pbmask' in glb: return glb['pbmask']
dflt = self.__pbmask_dflt( glb )
if dflt is not None: return dflt
return float(0.0)
[docs]
def __sidelobethreshold( self, glb ):
if 'sidelobethreshold' in glb: return glb['sidelobethreshold']
dflt = self.__sidelobethreshold_dflt( glb )
if dflt is not None: return dflt
return float(3.0)
[docs]
def __noisethreshold( self, glb ):
if 'noisethreshold' in glb: return glb['noisethreshold']
dflt = self.__noisethreshold_dflt( glb )
if dflt is not None: return dflt
return float(5.0)
[docs]
def __lownoisethreshold( self, glb ):
if 'lownoisethreshold' in glb: return glb['lownoisethreshold']
dflt = self.__lownoisethreshold_dflt( glb )
if dflt is not None: return dflt
return float(1.5)
[docs]
def __negativethreshold( self, glb ):
if 'negativethreshold' in glb: return glb['negativethreshold']
dflt = self.__negativethreshold_dflt( glb )
if dflt is not None: return dflt
return float(0.0)
[docs]
def __smoothfactor( self, glb ):
if 'smoothfactor' in glb: return glb['smoothfactor']
dflt = self.__smoothfactor_dflt( glb )
if dflt is not None: return dflt
return float(1.0)
[docs]
def __minbeamfrac( self, glb ):
if 'minbeamfrac' in glb: return glb['minbeamfrac']
dflt = self.__minbeamfrac_dflt( glb )
if dflt is not None: return dflt
return float(0.3)
[docs]
def __cutthreshold( self, glb ):
if 'cutthreshold' in glb: return glb['cutthreshold']
dflt = self.__cutthreshold_dflt( glb )
if dflt is not None: return dflt
return float(0.01)
[docs]
def __growiterations( self, glb ):
if 'growiterations' in glb: return glb['growiterations']
dflt = self.__growiterations_dflt( glb )
if dflt is not None: return dflt
return int(75)
[docs]
def __dogrowprune( self, glb ):
if 'dogrowprune' in glb: return glb['dogrowprune']
dflt = self.__dogrowprune_dflt( glb )
if dflt is not None: return dflt
return True
[docs]
def __minpercentchange( self, glb ):
if 'minpercentchange' in glb: return glb['minpercentchange']
dflt = self.__minpercentchange_dflt( glb )
if dflt is not None: return dflt
return float(-1.0)
[docs]
def __verbose( self, glb ):
if 'verbose' in glb: return glb['verbose']
dflt = self.__verbose_dflt( glb )
if dflt is not None: return dflt
return False
#--------- subparam inp output ----------------------------------------------------
[docs]
def __vis_inp(self):
description = 'Name of input visibility file(s)'
value = self.__vis( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'vis': value},{'vis': self.__schema['vis']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('vis',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __imageprefix_inp(self):
description = ''
value = self.__imageprefix( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'imageprefix': value},{'imageprefix': self.__schema['imageprefix']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('imageprefix',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __imagesuffix_inp(self):
description = ''
value = self.__imagesuffix( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'imagesuffix': value},{'imagesuffix': self.__schema['imagesuffix']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('imagesuffix',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __ncpu_inp(self):
description = ''
value = self.__ncpu( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'ncpu': value},{'ncpu': self.__schema['ncpu']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('ncpu',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __twidth_inp(self):
description = ''
value = self.__twidth( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'twidth': value},{'twidth': self.__schema['twidth']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('twidth',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __doreg_inp(self):
description = ''
value = self.__doreg( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'doreg': value},{'doreg': self.__schema['doreg']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('doreg',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __usephacenter_inp(self):
if self.__usephacenter_dflt( self.__globals_( ) ) is not None:
description = ''
value = self.__usephacenter( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'usephacenter': value},{'usephacenter': self.__schema['usephacenter']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('usephacenter',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __reftime_inp(self):
if self.__reftime_dflt( self.__globals_( ) ) is not None:
description = ''
value = self.__reftime( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'reftime': value},{'reftime': self.__schema['reftime']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('reftime',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __toTb_inp(self):
if self.__toTb_dflt( self.__globals_( ) ) is not None:
description = ''
value = self.__toTb( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'toTb': value},{'toTb': self.__schema['toTb']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('toTb',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __sclfactor_inp(self):
if self.__sclfactor_dflt( self.__globals_( ) ) is not None:
description = ''
value = self.__sclfactor( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'sclfactor': value},{'sclfactor': self.__schema['sclfactor']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('sclfactor',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __subregion_inp(self):
if self.__subregion_dflt( self.__globals_( ) ) is not None:
description = 'The name of a CASA region string'
value = self.__subregion( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'subregion': value},{'subregion': self.__schema['subregion']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('subregion',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __docompress_inp(self):
if self.__docompress_dflt( self.__globals_( ) ) is not None:
description = ''
value = self.__docompress( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'docompress': value},{'docompress': self.__schema['docompress']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('docompress',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __overwrite_inp(self):
description = ''
value = self.__overwrite( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'overwrite': value},{'overwrite': self.__schema['overwrite']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('overwrite',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __selectdata_inp(self):
description = 'Enable data selection parameters'
value = self.__selectdata( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'selectdata': value},{'selectdata': self.__schema['selectdata']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('selectdata',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __field_inp(self):
if self.__field_dflt( self.__globals_( ) ) is not None:
description = 'field(s) to select'
value = self.__field( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'field': value},{'field': self.__schema['field']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('field',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __spw_inp(self):
if self.__spw_dflt( self.__globals_( ) ) is not None:
description = 'spw(s)/channels to select'
value = self.__spw( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'spw': value},{'spw': self.__schema['spw']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('spw',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __timerange_inp(self):
if self.__timerange_dflt( self.__globals_( ) ) is not None:
description = 'Range of time to select from data'
value = self.__timerange( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'timerange': value},{'timerange': self.__schema['timerange']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('timerange',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __uvrange_inp(self):
if self.__uvrange_dflt( self.__globals_( ) ) is not None:
description = 'Select data within uvrange'
value = self.__uvrange( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'uvrange': value},{'uvrange': self.__schema['uvrange']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('uvrange',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __antenna_inp(self):
if self.__antenna_dflt( self.__globals_( ) ) is not None:
description = 'Select data based on antenna/baseline'
value = self.__antenna( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'antenna': value},{'antenna': self.__schema['antenna']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('antenna',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __scan_inp(self):
if self.__scan_dflt( self.__globals_( ) ) is not None:
description = 'Scan number range'
value = self.__scan( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'scan': value},{'scan': self.__schema['scan']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('scan',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __observation_inp(self):
if self.__observation_dflt( self.__globals_( ) ) is not None:
description = 'Observation ID range'
value = self.__observation( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'observation': value},{'observation': self.__schema['observation']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('observation',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __intent_inp(self):
if self.__intent_dflt( self.__globals_( ) ) is not None:
description = 'Scan Intent(s)'
value = self.__intent( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'intent': value},{'intent': self.__schema['intent']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('intent',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __datacolumn_inp(self):
description = 'Data column to image(data,corrected)'
value = self.__datacolumn( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'datacolumn': value},{'datacolumn': self.__schema['datacolumn']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('datacolumn',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __imsize_inp(self):
description = 'Number of pixels'
value = self.__imsize( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'imsize': value},{'imsize': self.__schema['imsize']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('imsize',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __cell_inp(self):
description = 'Cell size'
value = self.__cell( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'cell': value},{'cell': self.__schema['cell']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('cell',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __phasecenter_inp(self):
description = 'Phase center of the image'
value = self.__phasecenter( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'phasecenter': value},{'phasecenter': self.__schema['phasecenter']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('phasecenter',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __stokes_inp(self):
description = 'Stokes Planes to make'
value = self.__stokes( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'stokes': value},{'stokes': self.__schema['stokes']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('stokes',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __projection_inp(self):
description = 'Coordinate projection (SIN, HPX)'
value = self.__projection( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'projection': value},{'projection': self.__schema['projection']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('projection',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __startmodel_inp(self):
description = 'Name of starting model image'
value = self.__startmodel( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'startmodel': value},{'startmodel': self.__schema['startmodel']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('startmodel',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __specmode_inp(self):
description = 'Spectral definition mode (mfs,cube,cubedata, cubesource)'
value = self.__specmode( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'specmode': value},{'specmode': self.__schema['specmode']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('specmode',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __reffreq_inp(self):
if self.__reffreq_dflt( self.__globals_( ) ) is not None:
description = 'Reference frequency'
value = self.__reffreq( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'reffreq': value},{'reffreq': self.__schema['reffreq']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('reffreq',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __nchan_inp(self):
if self.__nchan_dflt( self.__globals_( ) ) is not None:
description = 'Number of channels in the output image'
value = self.__nchan( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'nchan': value},{'nchan': self.__schema['nchan']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('nchan',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __start_inp(self):
if self.__start_dflt( self.__globals_( ) ) is not None:
description = 'First channel (e.g. start=3,start=\'1.1GHz\',start=\'15343km/s\')'
value = self.__start( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'start': value},{'start': self.__schema['start']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('start',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __width_inp(self):
if self.__width_dflt( self.__globals_( ) ) is not None:
description = 'Channel width (e.g. width=2,width=\'0.1MHz\',width=\'10km/s\')'
value = self.__width( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'width': value},{'width': self.__schema['width']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('width',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __outframe_inp(self):
if self.__outframe_dflt( self.__globals_( ) ) is not None:
description = 'Spectral reference frame in which to interpret \'start\' and \'width\''
value = self.__outframe( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'outframe': value},{'outframe': self.__schema['outframe']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('outframe',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __veltype_inp(self):
if self.__veltype_dflt( self.__globals_( ) ) is not None:
description = 'Velocity type (radio, z, ratio, beta, gamma, optical)'
value = self.__veltype( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'veltype': value},{'veltype': self.__schema['veltype']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('veltype',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __restfreq_inp(self):
if self.__restfreq_dflt( self.__globals_( ) ) is not None:
description = 'List of rest frequencies'
value = self.__restfreq( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'restfreq': value},{'restfreq': self.__schema['restfreq']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('restfreq',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __interpolation_inp(self):
if self.__interpolation_dflt( self.__globals_( ) ) is not None:
description = 'Spectral interpolation (nearest,linear,cubic)'
value = self.__interpolation( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'interpolation': value},{'interpolation': self.__schema['interpolation']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('interpolation',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __gridder_inp(self):
description = 'Gridding options (standard, wproject, widefield, mosaic, awproject)'
value = self.__gridder( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'gridder': value},{'gridder': self.__schema['gridder']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('gridder',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __facets_inp(self):
if self.__facets_dflt( self.__globals_( ) ) is not None:
description = 'Number of facets on a side'
value = self.__facets( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'facets': value},{'facets': self.__schema['facets']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('facets',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __chanchunks_inp(self):
if self.__chanchunks_dflt( self.__globals_( ) ) is not None:
description = 'Number of channel chunks'
value = self.__chanchunks( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'chanchunks': value},{'chanchunks': self.__schema['chanchunks']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('chanchunks',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __wprojplanes_inp(self):
if self.__wprojplanes_dflt( self.__globals_( ) ) is not None:
description = 'Number of distinct w-values for convolution functions'
value = self.__wprojplanes( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'wprojplanes': value},{'wprojplanes': self.__schema['wprojplanes']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('wprojplanes',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __vptable_inp(self):
if self.__vptable_dflt( self.__globals_( ) ) is not None:
description = 'Name of Voltage Pattern table'
value = self.__vptable( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'vptable': value},{'vptable': self.__schema['vptable']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('vptable',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __usepointing_inp(self):
if self.__usepointing_dflt( self.__globals_( ) ) is not None:
description = 'Use Pointing table if available for mosaic pointing'
value = self.__usepointing( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'usepointing': value},{'usepointing': self.__schema['usepointing']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('usepointing',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __mosweight_inp(self):
if self.__mosweight_dflt( self.__globals_( ) ) is not None:
description = 'Indepently weight each field in a mosaic'
value = self.__mosweight( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'mosweight': value},{'mosweight': self.__schema['mosweight']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('mosweight',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __aterm_inp(self):
if self.__aterm_dflt( self.__globals_( ) ) is not None:
description = 'Use aperture illumination functions during gridding'
value = self.__aterm( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'aterm': value},{'aterm': self.__schema['aterm']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('aterm',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __psterm_inp(self):
if self.__psterm_dflt( self.__globals_( ) ) is not None:
description = 'Use prolate spheroidal during gridding'
value = self.__psterm( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'psterm': value},{'psterm': self.__schema['psterm']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('psterm',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __wbawp_inp(self):
if self.__wbawp_dflt( self.__globals_( ) ) is not None:
description = 'Use wideband A-terms'
value = self.__wbawp( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'wbawp': value},{'wbawp': self.__schema['wbawp']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('wbawp',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __conjbeams_inp(self):
if self.__conjbeams_dflt( self.__globals_( ) ) is not None:
description = 'Use conjugate frequency for wideband A-terms'
value = self.__conjbeams( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'conjbeams': value},{'conjbeams': self.__schema['conjbeams']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('conjbeams',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __cfcache_inp(self):
if self.__cfcache_dflt( self.__globals_( ) ) is not None:
description = '>Convolution function cache directory name'
value = self.__cfcache( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'cfcache': value},{'cfcache': self.__schema['cfcache']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('cfcache',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __computepastep_inp(self):
if self.__computepastep_dflt( self.__globals_( ) ) is not None:
description = 'At what parallactic angle interval to recompute AIFs (deg)'
value = self.__computepastep( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'computepastep': value},{'computepastep': self.__schema['computepastep']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('computepastep',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __rotatepastep_inp(self):
if self.__rotatepastep_dflt( self.__globals_( ) ) is not None:
description = 'At what parallactic angle interval to rotate nearest AIF (deg)'
value = self.__rotatepastep( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'rotatepastep': value},{'rotatepastep': self.__schema['rotatepastep']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('rotatepastep',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __pblimit_inp(self):
if self.__pblimit_dflt( self.__globals_( ) ) is not None:
description = '>PB gain level at which to cut off normalizations'
value = self.__pblimit( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'pblimit': value},{'pblimit': self.__schema['pblimit']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('pblimit',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __normtype_inp(self):
if self.__normtype_dflt( self.__globals_( ) ) is not None:
description = 'Normalization type (flatnoise, flatsky,pbsquare)'
value = self.__normtype( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'normtype': value},{'normtype': self.__schema['normtype']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('normtype',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __deconvolver_inp(self):
description = 'Minor cycle algorithm (hogbom,clark,multiscale,mtmfs,mem,clarkstokes)'
value = self.__deconvolver( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'deconvolver': value},{'deconvolver': self.__schema['deconvolver']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('deconvolver',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __scales_inp(self):
if self.__scales_dflt( self.__globals_( ) ) is not None:
description = 'List of scale sizes (in pixels) for multi-scale algorithms'
value = self.__scales( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'scales': value},{'scales': self.__schema['scales']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('scales',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __nterms_inp(self):
if self.__nterms_dflt( self.__globals_( ) ) is not None:
description = 'Number of Taylor coefficients in the spectral model'
value = self.__nterms( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'nterms': value},{'nterms': self.__schema['nterms']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('nterms',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __smallscalebias_inp(self):
if self.__smallscalebias_dflt( self.__globals_( ) ) is not None:
description = 'A bias towards smaller scale sizes'
value = self.__smallscalebias( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'smallscalebias': value},{'smallscalebias': self.__schema['smallscalebias']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('smallscalebias',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __restoration_inp(self):
description = 'Do restoration steps (or not)'
value = self.__restoration( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'restoration': value},{'restoration': self.__schema['restoration']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('restoration',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __restoringbeam_inp(self):
if self.__restoringbeam_dflt( self.__globals_( ) ) is not None:
description = 'Restoring beam shape to use. Default is the PSF main lobe'
value = self.__restoringbeam( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'restoringbeam': value},{'restoringbeam': self.__schema['restoringbeam']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('restoringbeam',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __pbcor_inp(self):
if self.__pbcor_dflt( self.__globals_( ) ) is not None:
description = 'Apply PB correction on the output restored image'
value = self.__pbcor( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'pbcor': value},{'pbcor': self.__schema['pbcor']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('pbcor',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __outlierfile_inp(self):
description = 'Name of outlier-field image definitions'
value = self.__outlierfile( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'outlierfile': value},{'outlierfile': self.__schema['outlierfile']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('outlierfile',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __weighting_inp(self):
description = 'Weighting scheme (natural,uniform,briggs)'
value = self.__weighting( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'weighting': value},{'weighting': self.__schema['weighting']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('weighting',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __robust_inp(self):
if self.__robust_dflt( self.__globals_( ) ) is not None:
description = 'Robustness parameter'
value = self.__robust( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'robust': value},{'robust': self.__schema['robust']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('robust',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __npixels_inp(self):
if self.__npixels_dflt( self.__globals_( ) ) is not None:
description = 'Number of pixels to determine uv-cell size (0 : -/+ 3 pixels)'
value = self.__npixels( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'npixels': value},{'npixels': self.__schema['npixels']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('npixels',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __uvtaper_inp(self):
if self.__uvtaper_dflt( self.__globals_( ) ) is not None:
description = 'uv-taper on outer baselines in uv-plane'
value = self.__uvtaper( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'uvtaper': value},{'uvtaper': self.__schema['uvtaper']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('uvtaper',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __niter_inp(self):
description = 'Maximum number of iterations'
value = self.__niter( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'niter': value},{'niter': self.__schema['niter']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('niter',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __gain_inp(self):
if self.__gain_dflt( self.__globals_( ) ) is not None:
description = 'Loop gain'
value = self.__gain( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'gain': value},{'gain': self.__schema['gain']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('gain',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __threshold_inp(self):
if self.__threshold_dflt( self.__globals_( ) ) is not None:
description = 'Stopping threshold'
value = self.__threshold( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'threshold': value},{'threshold': self.__schema['threshold']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('threshold',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __nsigma_inp(self):
if self.__nsigma_dflt( self.__globals_( ) ) is not None:
description = 'Multiplicative factor for rms-based threshold stopping'
value = self.__nsigma( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'nsigma': value},{'nsigma': self.__schema['nsigma']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('nsigma',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __cycleniter_inp(self):
if self.__cycleniter_dflt( self.__globals_( ) ) is not None:
description = 'Maximum number of minor-cycle iterations'
value = self.__cycleniter( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'cycleniter': value},{'cycleniter': self.__schema['cycleniter']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('cycleniter',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __cyclefactor_inp(self):
if self.__cyclefactor_dflt( self.__globals_( ) ) is not None:
description = 'Scaling on PSF sidelobe level to compute the minor-cycle stopping threshold.'
value = self.__cyclefactor( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'cyclefactor': value},{'cyclefactor': self.__schema['cyclefactor']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('cyclefactor',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __minpsffraction_inp(self):
if self.__minpsffraction_dflt( self.__globals_( ) ) is not None:
description = 'PSF fraction that marks the max depth of cleaning in the minor cycle'
value = self.__minpsffraction( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'minpsffraction': value},{'minpsffraction': self.__schema['minpsffraction']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('minpsffraction',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __maxpsffraction_inp(self):
if self.__maxpsffraction_dflt( self.__globals_( ) ) is not None:
description = 'PSF fraction that marks the minimum depth of cleaning in the minor cycle'
value = self.__maxpsffraction( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'maxpsffraction': value},{'maxpsffraction': self.__schema['maxpsffraction']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('maxpsffraction',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __interactive_inp(self):
if self.__interactive_dflt( self.__globals_( ) ) is not None:
description = 'Modify masks and parameters at runtime'
value = self.__interactive( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'interactive': value},{'interactive': self.__schema['interactive']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('interactive',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __usemask_inp(self):
description = 'Type of mask(s) for deconvolution: user, pb, or auto-multithresh'
value = self.__usemask( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'usemask': value},{'usemask': self.__schema['usemask']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('\x1B[1m\x1B[47m%-20.20s =\x1B[0m %s%-23s%s' % ('usemask',pre,self.__to_string_(value),post),description,13+len(pre)+len(post))
[docs]
def __mask_inp(self):
if self.__mask_dflt( self.__globals_( ) ) is not None:
description = 'Mask (a list of image name(s) or region file(s) or region string(s) )'
value = self.__mask( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'mask': value},{'mask': self.__schema['mask']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('mask',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __pbmask_inp(self):
if self.__pbmask_dflt( self.__globals_( ) ) is not None:
description = 'primary beam mask'
value = self.__pbmask( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'pbmask': value},{'pbmask': self.__schema['pbmask']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('pbmask',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __sidelobethreshold_inp(self):
if self.__sidelobethreshold_dflt( self.__globals_( ) ) is not None:
description = 'sidelobethreshold * the max sidelobe level * peak residual'
value = self.__sidelobethreshold( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'sidelobethreshold': value},{'sidelobethreshold': self.__schema['sidelobethreshold']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('sidelobethreshold',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __noisethreshold_inp(self):
if self.__noisethreshold_dflt( self.__globals_( ) ) is not None:
description = 'noisethreshold * rms in residual image'
value = self.__noisethreshold( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'noisethreshold': value},{'noisethreshold': self.__schema['noisethreshold']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('noisethreshold',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __lownoisethreshold_inp(self):
if self.__lownoisethreshold_dflt( self.__globals_( ) ) is not None:
description = 'lownoisethreshold * rms in residual image'
value = self.__lownoisethreshold( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'lownoisethreshold': value},{'lownoisethreshold': self.__schema['lownoisethreshold']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('lownoisethreshold',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __negativethreshold_inp(self):
if self.__negativethreshold_dflt( self.__globals_( ) ) is not None:
description = 'negativethreshold * rms in residual image'
value = self.__negativethreshold( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'negativethreshold': value},{'negativethreshold': self.__schema['negativethreshold']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('negativethreshold',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __smoothfactor_inp(self):
if self.__smoothfactor_dflt( self.__globals_( ) ) is not None:
description = 'smoothing factor in a unit of the beam'
value = self.__smoothfactor( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'smoothfactor': value},{'smoothfactor': self.__schema['smoothfactor']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('smoothfactor',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __minbeamfrac_inp(self):
if self.__minbeamfrac_dflt( self.__globals_( ) ) is not None:
description = 'minimum beam fraction for pruning'
value = self.__minbeamfrac( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'minbeamfrac': value},{'minbeamfrac': self.__schema['minbeamfrac']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('minbeamfrac',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __cutthreshold_inp(self):
if self.__cutthreshold_dflt( self.__globals_( ) ) is not None:
description = 'threshold to cut the smoothed mask to create a final mask'
value = self.__cutthreshold( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'cutthreshold': value},{'cutthreshold': self.__schema['cutthreshold']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('cutthreshold',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __growiterations_inp(self):
if self.__growiterations_dflt( self.__globals_( ) ) is not None:
description = 'number of binary dilation iterations for growing the mask'
value = self.__growiterations( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'growiterations': value},{'growiterations': self.__schema['growiterations']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('growiterations',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __dogrowprune_inp(self):
if self.__dogrowprune_dflt( self.__globals_( ) ) is not None:
description = 'Do pruning on the grow mask'
value = self.__dogrowprune( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'dogrowprune': value},{'dogrowprune': self.__schema['dogrowprune']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('dogrowprune',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __minpercentchange_inp(self):
if self.__minpercentchange_dflt( self.__globals_( ) ) is not None:
description = 'minimum percentage change in mask size (per channel plane) to trigger updating of mask by automask'
value = self.__minpercentchange( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'minpercentchange': value},{'minpercentchange': self.__schema['minpercentchange']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('minpercentchange',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __verbose_inp(self):
if self.__verbose_dflt( self.__globals_( ) ) is not None:
description = 'True: print more automasking information in the logger'
value = self.__verbose( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'verbose': value},{'verbose': self.__schema['verbose']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output(' \x1B[92m%-17.17s =\x1B[0m %s%-23s%s' % ('verbose',pre,self.__to_string_(value),post),description,9+len(pre)+len(post))
[docs]
def __restart_inp(self):
description = 'True : Re-use existing images. False : Increment imagename'
value = self.__restart( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'restart': value},{'restart': self.__schema['restart']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('restart',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __savemodel_inp(self):
description = 'Options to save model visibilities (none, virtual, modelcolumn)'
value = self.__savemodel( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'savemodel': value},{'savemodel': self.__schema['savemodel']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('savemodel',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __calcres_inp(self):
description = 'Calculate initial residual image'
value = self.__calcres( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'calcres': value},{'calcres': self.__schema['calcres']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('calcres',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __calcpsf_inp(self):
description = 'Calculate PSF'
value = self.__calcpsf( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'calcpsf': value},{'calcpsf': self.__schema['calcpsf']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('calcpsf',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
[docs]
def __parallel_inp(self):
description = 'Run major cycles in parallel'
value = self.__parallel( self.__globals_( ) )
(pre,post) = ('','') if self.__validate_({'parallel': value},{'parallel': self.__schema['parallel']}) else ('\x1B[91m','\x1B[0m')
self.__do_inp_output('%-20.20s = %s%-23s%s' % ('parallel',pre,self.__to_string_(value),post),description,0+len(pre)+len(post))
#--------- global default implementation-------------------------------------------
@static_var('state', __sf__('casa_inp_go_state'))
[docs]
def set_global_defaults(self):
self.set_global_defaults.state['last'] = self
glb = self.__globals_( )
if 'chanchunks' in glb: del glb['chanchunks']
if 'antenna' in glb: del glb['antenna']
if 'smoothfactor' in glb: del glb['smoothfactor']
if 'stokes' in glb: del glb['stokes']
if 'negativethreshold' in glb: del glb['negativethreshold']
if 'deconvolver' in glb: del glb['deconvolver']
if 'minbeamfrac' in glb: del glb['minbeamfrac']
if 'doreg' in glb: del glb['doreg']
if 'savemodel' in glb: del glb['savemodel']
if 'mask' in glb: del glb['mask']
if 'sclfactor' in glb: del glb['sclfactor']
if 'field' in glb: del glb['field']
if 'cutthreshold' in glb: del glb['cutthreshold']
if 'projection' in glb: del glb['projection']
if 'pblimit' in glb: del glb['pblimit']
if 'smallscalebias' in glb: del glb['smallscalebias']
if 'maxpsffraction' in glb: del glb['maxpsffraction']
if 'datacolumn' in glb: del glb['datacolumn']
if 'verbose' in glb: del glb['verbose']
if 'weighting' in glb: del glb['weighting']
if 'intent' in glb: del glb['intent']
if 'interpolation' in glb: del glb['interpolation']
if 'subregion' in glb: del glb['subregion']
if 'nterms' in glb: del glb['nterms']
if 'nchan' in glb: del glb['nchan']
if 'reffreq' in glb: del glb['reffreq']
if 'conjbeams' in glb: del glb['conjbeams']
if 'restoringbeam' in glb: del glb['restoringbeam']
if 'sidelobethreshold' in glb: del glb['sidelobethreshold']
if 'reftime' in glb: del glb['reftime']
if 'gridder' in glb: del glb['gridder']
if 'cycleniter' in glb: del glb['cycleniter']
if 'minpsffraction' in glb: del glb['minpsffraction']
if 'imsize' in glb: del glb['imsize']
if 'scan' in glb: del glb['scan']
if 'vis' in glb: del glb['vis']
if 'outlierfile' in glb: del glb['outlierfile']
if 'computepastep' in glb: del glb['computepastep']
if 'minpercentchange' in glb: del glb['minpercentchange']
if 'wbawp' in glb: del glb['wbawp']
if 'docompress' in glb: del glb['docompress']
if 'interactive' in glb: del glb['interactive']
if 'specmode' in glb: del glb['specmode']
if 'npixels' in glb: del glb['npixels']
if 'mosweight' in glb: del glb['mosweight']
if 'pbcor' in glb: del glb['pbcor']
if 'calcres' in glb: del glb['calcres']
if 'normtype' in glb: del glb['normtype']
if 'uvtaper' in glb: del glb['uvtaper']
if 'cyclefactor' in glb: del glb['cyclefactor']
if 'toTb' in glb: del glb['toTb']
if 'restfreq' in glb: del glb['restfreq']
if 'imageprefix' in glb: del glb['imageprefix']
if 'pbmask' in glb: del glb['pbmask']
if 'growiterations' in glb: del glb['growiterations']
if 'gain' in glb: del glb['gain']
if 'scales' in glb: del glb['scales']
if 'twidth' in glb: del glb['twidth']
if 'robust' in glb: del glb['robust']
if 'vptable' in glb: del glb['vptable']
if 'aterm' in glb: del glb['aterm']
if 'imagesuffix' in glb: del glb['imagesuffix']
if 'usephacenter' in glb: del glb['usephacenter']
if 'usepointing' in glb: del glb['usepointing']
if 'rotatepastep' in glb: del glb['rotatepastep']
if 'threshold' in glb: del glb['threshold']
if 'ncpu' in glb: del glb['ncpu']
if 'veltype' in glb: del glb['veltype']
if 'calcpsf' in glb: del glb['calcpsf']
if 'usemask' in glb: del glb['usemask']
if 'restoration' in glb: del glb['restoration']
if 'niter' in glb: del glb['niter']
if 'outframe' in glb: del glb['outframe']
if 'dogrowprune' in glb: del glb['dogrowprune']
if 'cell' in glb: del glb['cell']
if 'uvrange' in glb: del glb['uvrange']
if 'psterm' in glb: del glb['psterm']
if 'phasecenter' in glb: del glb['phasecenter']
if 'overwrite' in glb: del glb['overwrite']
if 'restart' in glb: del glb['restart']
if 'start' in glb: del glb['start']
if 'observation' in glb: del glb['observation']
if 'lownoisethreshold' in glb: del glb['lownoisethreshold']
if 'facets' in glb: del glb['facets']
if 'noisethreshold' in glb: del glb['noisethreshold']
if 'width' in glb: del glb['width']
if 'spw' in glb: del glb['spw']
if 'selectdata' in glb: del glb['selectdata']
if 'timerange' in glb: del glb['timerange']
if 'parallel' in glb: del glb['parallel']
if 'nsigma' in glb: del glb['nsigma']
if 'cfcache' in glb: del glb['cfcache']
if 'wprojplanes' in glb: del glb['wprojplanes']
if 'startmodel' in glb: del glb['startmodel']
#--------- inp function -----------------------------------------------------------
[docs]
def inp(self):
print("# ptclean -- %s" % self._info_desc_)
self.term_width, self.term_height = shutil.get_terminal_size(fallback=(80, 24))
self.__vis_inp( )
self.__imageprefix_inp( )
self.__imagesuffix_inp( )
self.__ncpu_inp( )
self.__twidth_inp( )
self.__doreg_inp( )
self.__usephacenter_inp( )
self.__reftime_inp( )
self.__toTb_inp( )
self.__sclfactor_inp( )
self.__subregion_inp( )
self.__docompress_inp( )
self.__overwrite_inp( )
self.__selectdata_inp( )
self.__field_inp( )
self.__spw_inp( )
self.__timerange_inp( )
self.__uvrange_inp( )
self.__antenna_inp( )
self.__scan_inp( )
self.__observation_inp( )
self.__intent_inp( )
self.__datacolumn_inp( )
self.__imsize_inp( )
self.__cell_inp( )
self.__phasecenter_inp( )
self.__stokes_inp( )
self.__projection_inp( )
self.__startmodel_inp( )
self.__specmode_inp( )
self.__reffreq_inp( )
self.__nchan_inp( )
self.__start_inp( )
self.__width_inp( )
self.__outframe_inp( )
self.__veltype_inp( )
self.__restfreq_inp( )
self.__interpolation_inp( )
self.__gridder_inp( )
self.__facets_inp( )
self.__chanchunks_inp( )
self.__wprojplanes_inp( )
self.__vptable_inp( )
self.__usepointing_inp( )
self.__mosweight_inp( )
self.__aterm_inp( )
self.__psterm_inp( )
self.__wbawp_inp( )
self.__conjbeams_inp( )
self.__cfcache_inp( )
self.__computepastep_inp( )
self.__rotatepastep_inp( )
self.__pblimit_inp( )
self.__normtype_inp( )
self.__deconvolver_inp( )
self.__scales_inp( )
self.__nterms_inp( )
self.__smallscalebias_inp( )
self.__restoration_inp( )
self.__restoringbeam_inp( )
self.__pbcor_inp( )
self.__outlierfile_inp( )
self.__weighting_inp( )
self.__robust_inp( )
self.__npixels_inp( )
self.__uvtaper_inp( )
self.__niter_inp( )
self.__gain_inp( )
self.__threshold_inp( )
self.__nsigma_inp( )
self.__cycleniter_inp( )
self.__cyclefactor_inp( )
self.__minpsffraction_inp( )
self.__maxpsffraction_inp( )
self.__interactive_inp( )
self.__usemask_inp( )
self.__mask_inp( )
self.__pbmask_inp( )
self.__sidelobethreshold_inp( )
self.__noisethreshold_inp( )
self.__lownoisethreshold_inp( )
self.__negativethreshold_inp( )
self.__smoothfactor_inp( )
self.__minbeamfrac_inp( )
self.__cutthreshold_inp( )
self.__growiterations_inp( )
self.__dogrowprune_inp( )
self.__minpercentchange_inp( )
self.__verbose_inp( )
self.__restart_inp( )
self.__savemodel_inp( )
self.__calcres_inp( )
self.__calcpsf_inp( )
self.__parallel_inp( )
#--------- tget function ----------------------------------------------------------
@static_var('state', __sf__('casa_inp_go_state'))
[docs]
def tget(self,file=None):
from casashell.private.stack_manip import find_frame
from runpy import run_path
filename = None
if file is None:
if os.path.isfile("ptclean.last"):
filename = "ptclean.last"
elif isinstance(file, str):
if os.path.isfile(file):
filename = file
if filename is not None:
glob = find_frame( )
newglob = run_path( filename, init_globals={ } )
for i in newglob:
glob[i] = newglob[i]
self.tget.state['last'] = self
else:
print("could not find last file, setting defaults instead...")
self.set_global_defaults( )
[docs]
def __call__( self, vis=None, imageprefix=None, imagesuffix=None, ncpu=None, twidth=None, doreg=None, usephacenter=None, reftime=None, toTb=None, sclfactor=None, subregion=None, docompress=None, overwrite=None, selectdata=None, field=None, spw=None, timerange=None, uvrange=None, antenna=None, scan=None, observation=None, intent=None, datacolumn=None, imsize=None, cell=None, phasecenter=None, stokes=None, projection=None, startmodel=None, specmode=None, reffreq=None, nchan=None, start=None, width=None, outframe=None, veltype=None, restfreq=None, interpolation=None, gridder=None, facets=None, chanchunks=None, wprojplanes=None, vptable=None, usepointing=None, mosweight=None, aterm=None, psterm=None, wbawp=None, conjbeams=None, cfcache=None, computepastep=None, rotatepastep=None, pblimit=None, normtype=None, deconvolver=None, scales=None, nterms=None, smallscalebias=None, restoration=None, restoringbeam=None, pbcor=None, outlierfile=None, weighting=None, robust=None, npixels=None, uvtaper=None, niter=None, gain=None, threshold=None, nsigma=None, cycleniter=None, cyclefactor=None, minpsffraction=None, maxpsffraction=None, interactive=None, usemask=None, mask=None, pbmask=None, sidelobethreshold=None, noisethreshold=None, lownoisethreshold=None, negativethreshold=None, smoothfactor=None, minbeamfrac=None, cutthreshold=None, growiterations=None, dogrowprune=None, minpercentchange=None, verbose=None, restart=None, savemodel=None, calcres=None, calcpsf=None, parallel=None ):
def noobj(s):
if s.startswith('<') and s.endswith('>'):
return "None"
else:
return s
_prefile = os.path.realpath('ptclean.pre')
_postfile = os.path.realpath('ptclean.last')
_return_result_ = None
_arguments = [vis,imageprefix,imagesuffix,ncpu,twidth,doreg,usephacenter,reftime,toTb,sclfactor,subregion,docompress,overwrite,selectdata,field,spw,timerange,uvrange,antenna,scan,observation,intent,datacolumn,imsize,cell,phasecenter,stokes,projection,startmodel,specmode,reffreq,nchan,start,width,outframe,veltype,restfreq,interpolation,gridder,facets,chanchunks,wprojplanes,vptable,usepointing,mosweight,aterm,psterm,wbawp,conjbeams,cfcache,computepastep,rotatepastep,pblimit,normtype,deconvolver,scales,nterms,smallscalebias,restoration,restoringbeam,pbcor,outlierfile,weighting,robust,npixels,uvtaper,niter,gain,threshold,nsigma,cycleniter,cyclefactor,minpsffraction,maxpsffraction,interactive,usemask,mask,pbmask,sidelobethreshold,noisethreshold,lownoisethreshold,negativethreshold,smoothfactor,minbeamfrac,cutthreshold,growiterations,dogrowprune,minpercentchange,verbose,restart,savemodel,calcres,calcpsf,parallel]
_invocation_parameters = OrderedDict( )
if any(map(lambda x: x is not None,_arguments)):
# invoke python style
# set the non sub-parameters that are not None
local_global = { }
if vis is not None: local_global['vis'] = vis
if imageprefix is not None: local_global['imageprefix'] = imageprefix
if imagesuffix is not None: local_global['imagesuffix'] = imagesuffix
if ncpu is not None: local_global['ncpu'] = ncpu
if twidth is not None: local_global['twidth'] = twidth
if doreg is not None: local_global['doreg'] = doreg
if overwrite is not None: local_global['overwrite'] = overwrite
if selectdata is not None: local_global['selectdata'] = selectdata
if datacolumn is not None: local_global['datacolumn'] = datacolumn
if imsize is not None: local_global['imsize'] = imsize
if cell is not None: local_global['cell'] = cell
if phasecenter is not None: local_global['phasecenter'] = phasecenter
if stokes is not None: local_global['stokes'] = stokes
if projection is not None: local_global['projection'] = projection
if startmodel is not None: local_global['startmodel'] = startmodel
if specmode is not None: local_global['specmode'] = specmode
if gridder is not None: local_global['gridder'] = gridder
if deconvolver is not None: local_global['deconvolver'] = deconvolver
if restoration is not None: local_global['restoration'] = restoration
if outlierfile is not None: local_global['outlierfile'] = outlierfile
if weighting is not None: local_global['weighting'] = weighting
if niter is not None: local_global['niter'] = niter
if usemask is not None: local_global['usemask'] = usemask
if restart is not None: local_global['restart'] = restart
if savemodel is not None: local_global['savemodel'] = savemodel
if calcres is not None: local_global['calcres'] = calcres
if calcpsf is not None: local_global['calcpsf'] = calcpsf
if parallel is not None: local_global['parallel'] = parallel
# the invocation parameters for the non-subparameters can now be set - this picks up those defaults
_invocation_parameters['vis'] = self.__vis( local_global )
_invocation_parameters['imageprefix'] = self.__imageprefix( local_global )
_invocation_parameters['imagesuffix'] = self.__imagesuffix( local_global )
_invocation_parameters['ncpu'] = self.__ncpu( local_global )
_invocation_parameters['twidth'] = self.__twidth( local_global )
_invocation_parameters['doreg'] = self.__doreg( local_global )
_invocation_parameters['overwrite'] = self.__overwrite( local_global )
_invocation_parameters['selectdata'] = self.__selectdata( local_global )
_invocation_parameters['datacolumn'] = self.__datacolumn( local_global )
_invocation_parameters['imsize'] = self.__imsize( local_global )
_invocation_parameters['cell'] = self.__cell( local_global )
_invocation_parameters['phasecenter'] = self.__phasecenter( local_global )
_invocation_parameters['stokes'] = self.__stokes( local_global )
_invocation_parameters['projection'] = self.__projection( local_global )
_invocation_parameters['startmodel'] = self.__startmodel( local_global )
_invocation_parameters['specmode'] = self.__specmode( local_global )
_invocation_parameters['gridder'] = self.__gridder( local_global )
_invocation_parameters['deconvolver'] = self.__deconvolver( local_global )
_invocation_parameters['restoration'] = self.__restoration( local_global )
_invocation_parameters['outlierfile'] = self.__outlierfile( local_global )
_invocation_parameters['weighting'] = self.__weighting( local_global )
_invocation_parameters['niter'] = self.__niter( local_global )
_invocation_parameters['usemask'] = self.__usemask( local_global )
_invocation_parameters['restart'] = self.__restart( local_global )
_invocation_parameters['savemodel'] = self.__savemodel( local_global )
_invocation_parameters['calcres'] = self.__calcres( local_global )
_invocation_parameters['calcpsf'] = self.__calcpsf( local_global )
_invocation_parameters['parallel'] = self.__parallel( local_global )
# the sub-parameters can then be set. Use the supplied value if not None, else the function, which gets the appropriate default
_invocation_parameters['usephacenter'] = self.__usephacenter( _invocation_parameters ) if usephacenter is None else usephacenter
_invocation_parameters['reftime'] = self.__reftime( _invocation_parameters ) if reftime is None else reftime
_invocation_parameters['toTb'] = self.__toTb( _invocation_parameters ) if toTb is None else toTb
_invocation_parameters['sclfactor'] = self.__sclfactor( _invocation_parameters ) if sclfactor is None else sclfactor
_invocation_parameters['subregion'] = self.__subregion( _invocation_parameters ) if subregion is None else subregion
_invocation_parameters['docompress'] = self.__docompress( _invocation_parameters ) if docompress is None else docompress
_invocation_parameters['field'] = self.__field( _invocation_parameters ) if field is None else field
_invocation_parameters['spw'] = self.__spw( _invocation_parameters ) if spw is None else spw
_invocation_parameters['timerange'] = self.__timerange( _invocation_parameters ) if timerange is None else timerange
_invocation_parameters['uvrange'] = self.__uvrange( _invocation_parameters ) if uvrange is None else uvrange
_invocation_parameters['antenna'] = self.__antenna( _invocation_parameters ) if antenna is None else antenna
_invocation_parameters['scan'] = self.__scan( _invocation_parameters ) if scan is None else scan
_invocation_parameters['observation'] = self.__observation( _invocation_parameters ) if observation is None else observation
_invocation_parameters['intent'] = self.__intent( _invocation_parameters ) if intent is None else intent
_invocation_parameters['reffreq'] = self.__reffreq( _invocation_parameters ) if reffreq is None else reffreq
_invocation_parameters['nchan'] = self.__nchan( _invocation_parameters ) if nchan is None else nchan
_invocation_parameters['start'] = self.__start( _invocation_parameters ) if start is None else start
_invocation_parameters['width'] = self.__width( _invocation_parameters ) if width is None else width
_invocation_parameters['outframe'] = self.__outframe( _invocation_parameters ) if outframe is None else outframe
_invocation_parameters['veltype'] = self.__veltype( _invocation_parameters ) if veltype is None else veltype
_invocation_parameters['restfreq'] = self.__restfreq( _invocation_parameters ) if restfreq is None else restfreq
_invocation_parameters['interpolation'] = self.__interpolation( _invocation_parameters ) if interpolation is None else interpolation
_invocation_parameters['facets'] = self.__facets( _invocation_parameters ) if facets is None else facets
_invocation_parameters['chanchunks'] = self.__chanchunks( _invocation_parameters ) if chanchunks is None else chanchunks
_invocation_parameters['wprojplanes'] = self.__wprojplanes( _invocation_parameters ) if wprojplanes is None else wprojplanes
_invocation_parameters['vptable'] = self.__vptable( _invocation_parameters ) if vptable is None else vptable
_invocation_parameters['usepointing'] = self.__usepointing( _invocation_parameters ) if usepointing is None else usepointing
_invocation_parameters['mosweight'] = self.__mosweight( _invocation_parameters ) if mosweight is None else mosweight
_invocation_parameters['aterm'] = self.__aterm( _invocation_parameters ) if aterm is None else aterm
_invocation_parameters['psterm'] = self.__psterm( _invocation_parameters ) if psterm is None else psterm
_invocation_parameters['wbawp'] = self.__wbawp( _invocation_parameters ) if wbawp is None else wbawp
_invocation_parameters['conjbeams'] = self.__conjbeams( _invocation_parameters ) if conjbeams is None else conjbeams
_invocation_parameters['cfcache'] = self.__cfcache( _invocation_parameters ) if cfcache is None else cfcache
_invocation_parameters['computepastep'] = self.__computepastep( _invocation_parameters ) if computepastep is None else computepastep
_invocation_parameters['rotatepastep'] = self.__rotatepastep( _invocation_parameters ) if rotatepastep is None else rotatepastep
_invocation_parameters['pblimit'] = self.__pblimit( _invocation_parameters ) if pblimit is None else pblimit
_invocation_parameters['normtype'] = self.__normtype( _invocation_parameters ) if normtype is None else normtype
_invocation_parameters['scales'] = self.__scales( _invocation_parameters ) if scales is None else scales
_invocation_parameters['nterms'] = self.__nterms( _invocation_parameters ) if nterms is None else nterms
_invocation_parameters['smallscalebias'] = self.__smallscalebias( _invocation_parameters ) if smallscalebias is None else smallscalebias
_invocation_parameters['restoringbeam'] = self.__restoringbeam( _invocation_parameters ) if restoringbeam is None else restoringbeam
_invocation_parameters['pbcor'] = self.__pbcor( _invocation_parameters ) if pbcor is None else pbcor
_invocation_parameters['robust'] = self.__robust( _invocation_parameters ) if robust is None else robust
_invocation_parameters['npixels'] = self.__npixels( _invocation_parameters ) if npixels is None else npixels
_invocation_parameters['uvtaper'] = self.__uvtaper( _invocation_parameters ) if uvtaper is None else uvtaper
_invocation_parameters['gain'] = self.__gain( _invocation_parameters ) if gain is None else gain
_invocation_parameters['threshold'] = self.__threshold( _invocation_parameters ) if threshold is None else threshold
_invocation_parameters['nsigma'] = self.__nsigma( _invocation_parameters ) if nsigma is None else nsigma
_invocation_parameters['cycleniter'] = self.__cycleniter( _invocation_parameters ) if cycleniter is None else cycleniter
_invocation_parameters['cyclefactor'] = self.__cyclefactor( _invocation_parameters ) if cyclefactor is None else cyclefactor
_invocation_parameters['minpsffraction'] = self.__minpsffraction( _invocation_parameters ) if minpsffraction is None else minpsffraction
_invocation_parameters['maxpsffraction'] = self.__maxpsffraction( _invocation_parameters ) if maxpsffraction is None else maxpsffraction
_invocation_parameters['interactive'] = self.__interactive( _invocation_parameters ) if interactive is None else interactive
_invocation_parameters['mask'] = self.__mask( _invocation_parameters ) if mask is None else mask
_invocation_parameters['pbmask'] = self.__pbmask( _invocation_parameters ) if pbmask is None else pbmask
_invocation_parameters['sidelobethreshold'] = self.__sidelobethreshold( _invocation_parameters ) if sidelobethreshold is None else sidelobethreshold
_invocation_parameters['noisethreshold'] = self.__noisethreshold( _invocation_parameters ) if noisethreshold is None else noisethreshold
_invocation_parameters['lownoisethreshold'] = self.__lownoisethreshold( _invocation_parameters ) if lownoisethreshold is None else lownoisethreshold
_invocation_parameters['negativethreshold'] = self.__negativethreshold( _invocation_parameters ) if negativethreshold is None else negativethreshold
_invocation_parameters['smoothfactor'] = self.__smoothfactor( _invocation_parameters ) if smoothfactor is None else smoothfactor
_invocation_parameters['minbeamfrac'] = self.__minbeamfrac( _invocation_parameters ) if minbeamfrac is None else minbeamfrac
_invocation_parameters['cutthreshold'] = self.__cutthreshold( _invocation_parameters ) if cutthreshold is None else cutthreshold
_invocation_parameters['growiterations'] = self.__growiterations( _invocation_parameters ) if growiterations is None else growiterations
_invocation_parameters['dogrowprune'] = self.__dogrowprune( _invocation_parameters ) if dogrowprune is None else dogrowprune
_invocation_parameters['minpercentchange'] = self.__minpercentchange( _invocation_parameters ) if minpercentchange is None else minpercentchange
_invocation_parameters['verbose'] = self.__verbose( _invocation_parameters ) if verbose is None else verbose
else:
# invoke with inp/go semantics
_invocation_parameters['vis'] = self.__vis( self.__globals_( ) )
_invocation_parameters['imageprefix'] = self.__imageprefix( self.__globals_( ) )
_invocation_parameters['imagesuffix'] = self.__imagesuffix( self.__globals_( ) )
_invocation_parameters['ncpu'] = self.__ncpu( self.__globals_( ) )
_invocation_parameters['twidth'] = self.__twidth( self.__globals_( ) )
_invocation_parameters['doreg'] = self.__doreg( self.__globals_( ) )
_invocation_parameters['usephacenter'] = self.__usephacenter( self.__globals_( ) )
_invocation_parameters['reftime'] = self.__reftime( self.__globals_( ) )
_invocation_parameters['toTb'] = self.__toTb( self.__globals_( ) )
_invocation_parameters['sclfactor'] = self.__sclfactor( self.__globals_( ) )
_invocation_parameters['subregion'] = self.__subregion( self.__globals_( ) )
_invocation_parameters['docompress'] = self.__docompress( self.__globals_( ) )
_invocation_parameters['overwrite'] = self.__overwrite( self.__globals_( ) )
_invocation_parameters['selectdata'] = self.__selectdata( self.__globals_( ) )
_invocation_parameters['field'] = self.__field( self.__globals_( ) )
_invocation_parameters['spw'] = self.__spw( self.__globals_( ) )
_invocation_parameters['timerange'] = self.__timerange( self.__globals_( ) )
_invocation_parameters['uvrange'] = self.__uvrange( self.__globals_( ) )
_invocation_parameters['antenna'] = self.__antenna( self.__globals_( ) )
_invocation_parameters['scan'] = self.__scan( self.__globals_( ) )
_invocation_parameters['observation'] = self.__observation( self.__globals_( ) )
_invocation_parameters['intent'] = self.__intent( self.__globals_( ) )
_invocation_parameters['datacolumn'] = self.__datacolumn( self.__globals_( ) )
_invocation_parameters['imsize'] = self.__imsize( self.__globals_( ) )
_invocation_parameters['cell'] = self.__cell( self.__globals_( ) )
_invocation_parameters['phasecenter'] = self.__phasecenter( self.__globals_( ) )
_invocation_parameters['stokes'] = self.__stokes( self.__globals_( ) )
_invocation_parameters['projection'] = self.__projection( self.__globals_( ) )
_invocation_parameters['startmodel'] = self.__startmodel( self.__globals_( ) )
_invocation_parameters['specmode'] = self.__specmode( self.__globals_( ) )
_invocation_parameters['reffreq'] = self.__reffreq( self.__globals_( ) )
_invocation_parameters['nchan'] = self.__nchan( self.__globals_( ) )
_invocation_parameters['start'] = self.__start( self.__globals_( ) )
_invocation_parameters['width'] = self.__width( self.__globals_( ) )
_invocation_parameters['outframe'] = self.__outframe( self.__globals_( ) )
_invocation_parameters['veltype'] = self.__veltype( self.__globals_( ) )
_invocation_parameters['restfreq'] = self.__restfreq( self.__globals_( ) )
_invocation_parameters['interpolation'] = self.__interpolation( self.__globals_( ) )
_invocation_parameters['gridder'] = self.__gridder( self.__globals_( ) )
_invocation_parameters['facets'] = self.__facets( self.__globals_( ) )
_invocation_parameters['chanchunks'] = self.__chanchunks( self.__globals_( ) )
_invocation_parameters['wprojplanes'] = self.__wprojplanes( self.__globals_( ) )
_invocation_parameters['vptable'] = self.__vptable( self.__globals_( ) )
_invocation_parameters['usepointing'] = self.__usepointing( self.__globals_( ) )
_invocation_parameters['mosweight'] = self.__mosweight( self.__globals_( ) )
_invocation_parameters['aterm'] = self.__aterm( self.__globals_( ) )
_invocation_parameters['psterm'] = self.__psterm( self.__globals_( ) )
_invocation_parameters['wbawp'] = self.__wbawp( self.__globals_( ) )
_invocation_parameters['conjbeams'] = self.__conjbeams( self.__globals_( ) )
_invocation_parameters['cfcache'] = self.__cfcache( self.__globals_( ) )
_invocation_parameters['computepastep'] = self.__computepastep( self.__globals_( ) )
_invocation_parameters['rotatepastep'] = self.__rotatepastep( self.__globals_( ) )
_invocation_parameters['pblimit'] = self.__pblimit( self.__globals_( ) )
_invocation_parameters['normtype'] = self.__normtype( self.__globals_( ) )
_invocation_parameters['deconvolver'] = self.__deconvolver( self.__globals_( ) )
_invocation_parameters['scales'] = self.__scales( self.__globals_( ) )
_invocation_parameters['nterms'] = self.__nterms( self.__globals_( ) )
_invocation_parameters['smallscalebias'] = self.__smallscalebias( self.__globals_( ) )
_invocation_parameters['restoration'] = self.__restoration( self.__globals_( ) )
_invocation_parameters['restoringbeam'] = self.__restoringbeam( self.__globals_( ) )
_invocation_parameters['pbcor'] = self.__pbcor( self.__globals_( ) )
_invocation_parameters['outlierfile'] = self.__outlierfile( self.__globals_( ) )
_invocation_parameters['weighting'] = self.__weighting( self.__globals_( ) )
_invocation_parameters['robust'] = self.__robust( self.__globals_( ) )
_invocation_parameters['npixels'] = self.__npixels( self.__globals_( ) )
_invocation_parameters['uvtaper'] = self.__uvtaper( self.__globals_( ) )
_invocation_parameters['niter'] = self.__niter( self.__globals_( ) )
_invocation_parameters['gain'] = self.__gain( self.__globals_( ) )
_invocation_parameters['threshold'] = self.__threshold( self.__globals_( ) )
_invocation_parameters['nsigma'] = self.__nsigma( self.__globals_( ) )
_invocation_parameters['cycleniter'] = self.__cycleniter( self.__globals_( ) )
_invocation_parameters['cyclefactor'] = self.__cyclefactor( self.__globals_( ) )
_invocation_parameters['minpsffraction'] = self.__minpsffraction( self.__globals_( ) )
_invocation_parameters['maxpsffraction'] = self.__maxpsffraction( self.__globals_( ) )
_invocation_parameters['interactive'] = self.__interactive( self.__globals_( ) )
_invocation_parameters['usemask'] = self.__usemask( self.__globals_( ) )
_invocation_parameters['mask'] = self.__mask( self.__globals_( ) )
_invocation_parameters['pbmask'] = self.__pbmask( self.__globals_( ) )
_invocation_parameters['sidelobethreshold'] = self.__sidelobethreshold( self.__globals_( ) )
_invocation_parameters['noisethreshold'] = self.__noisethreshold( self.__globals_( ) )
_invocation_parameters['lownoisethreshold'] = self.__lownoisethreshold( self.__globals_( ) )
_invocation_parameters['negativethreshold'] = self.__negativethreshold( self.__globals_( ) )
_invocation_parameters['smoothfactor'] = self.__smoothfactor( self.__globals_( ) )
_invocation_parameters['minbeamfrac'] = self.__minbeamfrac( self.__globals_( ) )
_invocation_parameters['cutthreshold'] = self.__cutthreshold( self.__globals_( ) )
_invocation_parameters['growiterations'] = self.__growiterations( self.__globals_( ) )
_invocation_parameters['dogrowprune'] = self.__dogrowprune( self.__globals_( ) )
_invocation_parameters['minpercentchange'] = self.__minpercentchange( self.__globals_( ) )
_invocation_parameters['verbose'] = self.__verbose( self.__globals_( ) )
_invocation_parameters['restart'] = self.__restart( self.__globals_( ) )
_invocation_parameters['savemodel'] = self.__savemodel( self.__globals_( ) )
_invocation_parameters['calcres'] = self.__calcres( self.__globals_( ) )
_invocation_parameters['calcpsf'] = self.__calcpsf( self.__globals_( ) )
_invocation_parameters['parallel'] = self.__parallel( self.__globals_( ) )
try:
with open(_prefile,'w') as _f:
for _i in _invocation_parameters:
_f.write("%-17s = %s\n" % (_i,noobj(repr(_invocation_parameters[_i]))))
_f.write("#ptclean( ")
count = 0
for _i in _invocation_parameters:
_f.write("%s=%s" % (_i,noobj(repr(_invocation_parameters[_i]))))
count += 1
if count < len(_invocation_parameters): _f.write(",")
_f.write(" )\n")
except: pass
try:
_return_result_ = _ptclean_t( _invocation_parameters['vis'],_invocation_parameters['imageprefix'],_invocation_parameters['imagesuffix'],_invocation_parameters['ncpu'],_invocation_parameters['twidth'],_invocation_parameters['doreg'],_invocation_parameters['usephacenter'],_invocation_parameters['reftime'],_invocation_parameters['toTb'],_invocation_parameters['sclfactor'],_invocation_parameters['subregion'],_invocation_parameters['docompress'],_invocation_parameters['overwrite'],_invocation_parameters['selectdata'],_invocation_parameters['field'],_invocation_parameters['spw'],_invocation_parameters['timerange'],_invocation_parameters['uvrange'],_invocation_parameters['antenna'],_invocation_parameters['scan'],_invocation_parameters['observation'],_invocation_parameters['intent'],_invocation_parameters['datacolumn'],_invocation_parameters['imsize'],_invocation_parameters['cell'],_invocation_parameters['phasecenter'],_invocation_parameters['stokes'],_invocation_parameters['projection'],_invocation_parameters['startmodel'],_invocation_parameters['specmode'],_invocation_parameters['reffreq'],_invocation_parameters['nchan'],_invocation_parameters['start'],_invocation_parameters['width'],_invocation_parameters['outframe'],_invocation_parameters['veltype'],_invocation_parameters['restfreq'],_invocation_parameters['interpolation'],_invocation_parameters['gridder'],_invocation_parameters['facets'],_invocation_parameters['chanchunks'],_invocation_parameters['wprojplanes'],_invocation_parameters['vptable'],_invocation_parameters['usepointing'],_invocation_parameters['mosweight'],_invocation_parameters['aterm'],_invocation_parameters['psterm'],_invocation_parameters['wbawp'],_invocation_parameters['conjbeams'],_invocation_parameters['cfcache'],_invocation_parameters['computepastep'],_invocation_parameters['rotatepastep'],_invocation_parameters['pblimit'],_invocation_parameters['normtype'],_invocation_parameters['deconvolver'],_invocation_parameters['scales'],_invocation_parameters['nterms'],_invocation_parameters['smallscalebias'],_invocation_parameters['restoration'],_invocation_parameters['restoringbeam'],_invocation_parameters['pbcor'],_invocation_parameters['outlierfile'],_invocation_parameters['weighting'],_invocation_parameters['robust'],_invocation_parameters['npixels'],_invocation_parameters['uvtaper'],_invocation_parameters['niter'],_invocation_parameters['gain'],_invocation_parameters['threshold'],_invocation_parameters['nsigma'],_invocation_parameters['cycleniter'],_invocation_parameters['cyclefactor'],_invocation_parameters['minpsffraction'],_invocation_parameters['maxpsffraction'],_invocation_parameters['interactive'],_invocation_parameters['usemask'],_invocation_parameters['mask'],_invocation_parameters['pbmask'],_invocation_parameters['sidelobethreshold'],_invocation_parameters['noisethreshold'],_invocation_parameters['lownoisethreshold'],_invocation_parameters['negativethreshold'],_invocation_parameters['smoothfactor'],_invocation_parameters['minbeamfrac'],_invocation_parameters['cutthreshold'],_invocation_parameters['growiterations'],_invocation_parameters['dogrowprune'],_invocation_parameters['minpercentchange'],_invocation_parameters['verbose'],_invocation_parameters['restart'],_invocation_parameters['savemodel'],_invocation_parameters['calcres'],_invocation_parameters['calcpsf'],_invocation_parameters['parallel'] )
except Exception as e:
from traceback import format_exc
from casatasks import casalog
casalog.origin('ptclean')
casalog.post("Exception Reported: Error in ptclean: %s" % str(e),'SEVERE')
casalog.post(format_exc( ))
_return_result_ = False
try:
os.rename(_prefile,_postfile)
except: pass
return _return_result_