def image_vlow(wd=None):
if wd is not None:
os.chdir(wd)
update_status(None, 'Running')
run('CleanSHM.py')
run('DDF.py --Output-Name=image_full_vlow_nocut --Data-MS=big-mslist.txt --Deconv-PeakFactor 0.001000 --Data-ColName DATA --Parallel-NCPU=%i --Beam-CenterNorm=1 --Deconv-CycleFactor=0 --Deconv-MaxMinorIter=1000000 --Deconv-MaxMajorIter=2 --Deconv-Mode SSD --Beam-Model=LOFAR --Beam-LOFARBeamMode=A --Weight-Robust -0.20000 --Image-NPix=2000 --CF-wmax 50000 --CF-Nw 100 --Output-Also onNeds --Image-Cell 15.00000 --Facets-NFacets=11 --SSDClean-NEnlargeData 0 --Freq-NDegridBand 1 --Beam-NBand 1 --Facets-DiamMax 1.5 --Facets-DiamMin 0.1 --Deconv-RMSFactor=3.000000 --SSDClean-ConvFFTSwitch 10000 --Data-Sort 1 --Cache-Dir=. --Log-Memory 1 --GAClean-RMSFactorInitHMP 1.000000 --GAClean-MaxMinorIterInitHMP 10000.000000 --GAClean-AllowNegativeInitHMP True --DDESolutions-SolsDir=SOLSDIR --Cache-Weight=reset --Output-Mode=Clean --Output-RestoringBeam 60.000000 --Weight-ColName="IMAGING_WEIGHT" --Freq-NBand=2 --RIME-DecorrMode=FT --SSDClean-SSDSolvePars [S,Alpha] --SSDClean-BICFactor 0 --Mask-Auto=1 --Mask-SigTh=4.00 --DDESolutions-GlobalNorm=None --DDESolutions-DDModeGrid=AP --DDESolutions-DDModeDeGrid=AP --DDESolutions-DDSols=[DDS3_full_smoothed,DDS3_full_slow] --Selection-UVRangeKm=[0.000000,7.0] --GAClean-MinSizeInit=10 --Beam-Smooth=1 --Debug-Pdb=never'
% getcpus())
vlowmask = make_mask('image_full_vlow_nocut.app.restored.fits', 3.0)
run('DDF.py --Output-Name=image_full_vlow_nocut_m --Data-MS=big-mslist.txt --Deconv-PeakFactor 0.001000 --Data-ColName DATA --Parallel-NCPU=%i --Beam-CenterNorm=1 --Deconv-CycleFactor=0 --Deconv-MaxMinorIter=1000000 --Deconv-MaxMajorIter=2 --Deconv-Mode SSD --Beam-Model=LOFAR --Beam-LOFARBeamMode=A --Weight-Robust -0.20000 --Image-NPix=2000 --CF-wmax 50000 --CF-Nw 100 --Output-Also onNeds --Image-Cell 15.00000 --Facets-NFacets=11 --SSDClean-NEnlargeData 0 --Freq-NDegridBand 1 --Beam-NBand 1 --Facets-DiamMax 1.5 --Facets-DiamMin 0.1 --Deconv-RMSFactor=3.000000 --SSDClean-ConvFFTSwitch 10000 --Data-Sort 1 --Cache-Dir=. --Log-Memory 1 --GAClean-RMSFactorInitHMP 1.000000 --GAClean-MaxMinorIterInitHMP 10000.000000 --GAClean-AllowNegativeInitHMP True --DDESolutions-SolsDir=SOLSDIR --Cache-Weight=reset --Output-Mode=Clean --Output-RestoringBeam 60.000000 --Weight-ColName="IMAGING_WEIGHT" --Freq-NBand=2 --RIME-DecorrMode=FT --SSDClean-SSDSolvePars [S,Alpha] --SSDClean-BICFactor 0 --Mask-Auto=1 --Mask-SigTh=3.00 --DDESolutions-GlobalNorm=None --DDESolutions-DDModeGrid=AP --DDESolutions-DDModeDeGrid=AP --DDESolutions-DDSols=[DDS3_full_smoothed,DDS3_full_slow] --Selection-UVRangeKm=[0.000000,7.0] --GAClean-MinSizeInit=10 --Beam-Smooth=1 --Debug-Pdb=never --Predict-InitDicoModel=image_full_vlow_nocut.DicoModel --Mask-External=%s'
% (getcpus(), vlowmask))
update_status(None, 'Complete')
from getcpus import getcpus
option_list = (('machine', 'NCPU', int, getcpus()),
('image', 'pbimage', str,
'image_full_ampphase1m_shift.int.facetRestored.fits',
'PB-corrected image to use for source finding'),
('image', 'nonpbimage', str,
'image_full_ampphase1m_shift.app.facetRestored.fits',
'PB-uncorrected image to use for source finding'),
('image', 'catprefix', str, 'image_full_ampphase1m',
'Prefix to use for output catalogues'), ('control', 'sfind',
bool, True,
'Do source finding?'),
('control', 'sfind_pixel_fraction', float, 1.0,
'Source find over what fraction of the image?'),
('control', 'quiet', bool,
False), ('control', 'logging', str,
'logs'), ('control', 'dryrun', bool,
False), ('control', 'restart', bool, True),
('pybdsm', 'atrous', bool,
True), ('comparison_cats', 'list', list,
None), ('comparison_cats', 'filenames', list, None),
('comparison_cats', 'radii', list,
None), ('comparison_cats', 'fluxfactor', list,
None), ('comparison_cats', 'TGSS', str,
None), ('comparison_cats', 'TGSS_matchrad',
float, 10.0),
('comparison_cats', 'TGSS_match_majkey1', float,
'Maj_1'), ('comparison_cats', 'TGSS_match_majkey2', float,
'Maj_2'), ('comparison_cats', 'TGSS_filtersize',
float, 40.0),
from __future__ import absolute_import
from getcpus import getcpus
option_list = (
('machine', 'NCPU_DDF', int, getcpus(),
'Number of CPUS to use for DDF'), ('machine', 'NCPU_killms', int,
getcpus(),
'Number of CPUS to use for KillMS'),
('data', 'mslist', str, None,
'Initial measurement set list to use -- must be specified'),
('data', 'full_mslist', str, None,
'Full-bandwidth measurement set to use for final step, if any'),
('data', 'colname', str, 'CORRECTED_DATA', 'MS column to use'),
('solutions', 'ndir', int, 45,
'Number of directions'), ('solutions', 'SolsDir', str, "SOLSDIR",
'Directory for solutions'),
('solutions', 'NChanSols', int, 1,
'NChanSols for killMS'), ('solutions', 'dt_slow', float, 1.,
'Time interval for killMS (minutes)'),
('solutions', 'do_very_slow', bool, True,
'Enable very slow amplitude smoothing'),
('solutions', 'sigma_clip', float, 5.0,
'Sigma clip for amplitude outliers'),
('solutions', 'dt_very_slow', float, 43.63,
'Time interval for killMS (minutes)'),
('solutions', 'dt_di', float, 0.5,
'Time interval for DI killMS (minutes)'),
('solutions', 'dt_fast', float, 0.5,
'Time interval for full-bandwidth killMS (minutes)'),
('solutions', 'LambdaKF', float, 0.5,
'Kalman filter lambda for killMS'), ('solutions', 'NIterKF', list, [
'--column',
help='Input column for the ms, default=DATA',
default='DATA') #DATA_DI_CORRECTED
parser.add_argument('-f',
'--freqavg',
help='channel averaging, default=4',
default=4,
type=int)
parser.add_argument('-t',
'--timeavg',
help='timesample averaging, default=2',
default=2,
type=int)
parser.add_argument('-n',
'--ncpu',
help='number of cpu to use, default=%i' % getcpus(),
default=getcpus(),
type=int)
parser.add_argument('-p',
'--prefixname',
help='prefixname for output ms, default=object',
default='object') #, type=str)
parser.add_argument('--nodysco',
help='Do not dysco compress output',
action='store_false')
parser.add_argument('--split',
help='Do not concat but keep 10 SB blocks',
action='store_true')
parser.add_argument('--aoflaggerbefore',
help='Do an extra round of AOflagger on input data',
action='store_true')
from getcpus import getcpus
option_list = ( ( 'machine', 'NCPU_DDF', int, getcpus(),
'Number of CPUS to use for DDF'),
( 'machine', 'NCPU_killms', int, getcpus(),
'Number of CPUS to use for KillMS' ),
( 'data', 'mslist', str, None,
'Initial measurement set list to use -- must be specified' ),
( 'data', 'full_mslist', str, None,
'Full-bandwidth measurement set to use for final step, if any' ),
( 'data', 'colname', str, 'CORRECTED_DATA', 'MS column to use' ),
( 'solutions', 'ndir', int, 45, 'Number of directions' ),
( 'solutions', 'SolsDir', str, "SOLSDIR", 'Directory for solutions' ),
( 'solutions', 'NChanSols', int, 1, 'NChanSols for killMS' ),
( 'solutions', 'dt_slow', float, 1., 'Time interval for killMS (minutes)' ),
( 'solutions', 'do_very_slow', bool, True, 'Enable very slow amplitude smoothing'),
( 'solutions', 'sigma_clip', float, 5.0, 'Sigma clip for amplitude outliers'),
( 'solutions', 'dt_very_slow', float, 43.63, 'Time interval for killMS (minutes)' ),
( 'solutions', 'dt_di', float, 0.5, 'Time interval for DI killMS (minutes)' ),
( 'solutions', 'dt_fast', float, 0.5, 'Time interval for full-bandwidth killMS (minutes)' ),
( 'solutions', 'LambdaKF', float, 0.5, 'Kalman filter lambda for killMS' ),
( 'solutions', 'NIterKF', list, [1, 1, 1, 1, 6, 1, 6], 'Kalman filter iterations for killMS for the 7 killMS steps' ),
( 'solutions', 'normalize', list, ['BLBased', 'BLBased', 'SumBLBased'], 'How to normalize solutions for the three self-cal steps' ),
( 'solutions', 'uvmin', float, None, 'Minimum baseline length to use in self-calibration (km)' ),
( 'solutions', 'uvmin_very_slow', float, 0.5, 'Minimum baseline length to use in slow smoothing of self-cal solutions (km)' ),
( 'solutions', 'wtuv', float, None, 'Factor to apply to fitting weights of data below uvmin. None implies, effectively, zero.'),
( 'solutions', 'robust', float, None, 'Briggs robustness to use in killMS. If None, natural weighting is used.'),
( 'solutions', 'smoothing', bool, True, 'Smooth the solutions.'),
( 'solutions', 'smoothingtype', str, 'TEC,PolyAmp', 'Type of smoothing to do on the solutions.'),
( 'image', 'do_wide', bool, False, 'do widefield image and subtract sources in annulus' ),
( 'image', 'wide_imsize', int, 10000, 'Widefield image size in pixels' ),
from getcpus import getcpus
option_list = ( ( 'machine', 'NCPU_DDF', int, getcpus(),
'Number of CPUS to use for DDF'),
( 'machine', 'NCPU_killms', int, getcpus(),
'Number of CPUS to use for KillMS' ),
( 'data', 'mslist', str, None,
'Initial measurement set list to use -- must be specified' ),
( 'data', 'full_mslist', str, None,
'Full-bandwidth measurement set to use for final step, if any' ),
( 'data', 'colname', str, 'CORRECTED_DATA', 'MS column to use' ),
( 'solutions', 'ndir', int, 45, 'Number of directions' ),
( 'solutions', 'SolsDir', str, "SOLSDIR", 'Directory for solutions' ),
( 'solutions', 'NChanSols', int, 1, 'NChanSols for killMS' ),
( 'solutions', 'dt_slow', float, 1., 'Time interval for killMS (minutes)' ),
( 'solutions', 'do_very_slow', bool, True, 'Enable very slow amplitude smoothing'),
( 'solutions', 'sigma_clip', float, 5.0, 'Sigma clip for amplitude outliers'),
( 'solutions', 'dt_very_slow', float, 43.63, 'Time interval for killMS (minutes)' ),
( 'solutions', 'dt_di', float, 0.5, 'Time interval for DI killMS (minutes)' ),
( 'solutions', 'dt_fast', float, 0.5, 'Time interval for full-bandwidth killMS (minutes)' ),
( 'solutions', 'LambdaKF', float, 0.5, 'Kalman filter lambda for killMS' ),
( 'solutions', 'NIterKF', list, [1, 1, 1, 1, 6, 1, 6], 'Kalman filter iterations for killMS for the 7 killMS steps' ),
( 'solutions', 'normalize', list, ['BLBased', 'BLBased', 'SumBLBased'], 'How to normalize solutions for the three self-cal steps' ),
( 'solutions', 'uvmin', float, None, 'Minimum baseline length to use in self-calibration (km)' ),
( 'solutions', 'uvmin_very_slow', float, 0.5, 'Minimum baseline length to use in slow smoothing of self-cal solutions (km)' ),
( 'solutions', 'wtuv', float, None, 'Factor to apply to fitting weights of data below uvmin. None implies, effectively, zero.'),
( 'solutions', 'robust', float, None, 'Briggs robustness to use in killMS. If None, natural weighting is used.'),
( 'solutions', 'smoothing', bool, True, 'Smooth the solutions.'),
( 'solutions', 'smoothingtype', str, 'TEC,PolyAmp', 'Type of smoothing to do on the solutions.'),
( 'image', 'do_wide', bool, False, 'do widefield image and subtract sources in annulus' ),
( 'image', 'wide_imsize', int, 10000, 'Widefield image size in pixels' ),
from getcpus import getcpus
option_list = ( ( 'machine', 'NCPU', int, getcpus() ),
( 'image', 'pbimage', str, 'image_full_ampphase_di_m.NS_shift.app.facetRestored.fits', 'PB-corrected image to use for source finding' ),
( 'image', 'nonpbimage', str, 'image_full_ampphase_di_m.NS_shift.app.facetRestored.fits', 'PB-uncorrected image to use for source finding' ),
( 'image', 'catprefix', str, 'image_full_ampphase_di_m.NS', 'Prefix to use for output catalogues' ),
( 'control', 'sfind_pixel_fraction', float, 1.0, 'Source find over what fraction of the image?' ),
( 'control', 'fit_sourcecounts', bool, True),
( 'control', 'quiet', bool, False ),
( 'control', 'logging', str, 'logs' ),
( 'control', 'dryrun', bool, False ),
( 'control', 'restart', bool, True ),
( 'pybdsm', 'atrous', bool, True ),
( 'comparison_cats', 'dir', str, '.' ),
( 'comparison_cats', 'list', list, None ),
( 'comparison_cats', 'filenames', list, None),
( 'comparison_cats', 'radii', list, None),
( 'comparison_cats', 'fluxfactor', list, None),
( 'comparison_cats', 'TGSS', str, None ),
( 'comparison_cats', 'TGSS_matchrad', float, 10.0 ),
( 'comparison_cats', 'TGSS_match_majkey1', float, 'Maj_1' ),
( 'comparison_cats', 'TGSS_match_majkey2', float, 'Maj_2' ),
( 'comparison_cats', 'TGSS_filtersize', float, 40.0 ),
( 'comparison_cats', 'TGSS_fluxfactor', float, 1000.0 ),
( 'comparison_cats', 'FIRST', str, None ),
( 'comparison_cats', 'FIRST_matchrad', float, 10.0 ),
( 'comparison_cats', 'FIRST_match_majkey1', float, 'Maj' ),
( 'comparison_cats', 'FIRST_match_majkey2', float, 'MAJOR' ),
( 'comparison_cats', 'FIRST_filtersize', float, 10.0 ),
( 'comparison_cats', 'FIRST_fluxfactor', float, 1.0 ) )
