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Commit c7a66e8c authored by Alexander Kutkin's avatar Alexander Kutkin
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fixed wrong mask file usage

parent 4abda057
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cluster.py
+ 11
− 9
View file @ c7a66e8c
......@@ -378,12 +378,6 @@ def auto_clustering(fig, ax, df, wcs, resid_data, pix_arcmin_scale, nbright,
small_resid = resid_data[py-boxsize:py+boxsize, px-boxsize:px+boxsize]
ellipse_mean, ecc, amaj, numpix = ellipses_coh(small_resid, amin=20, amax=boxsize-1, dr=1.0)
# print ellipse_mean, ecc, amaj, numpix
# if abs(ring_mean/resid_mean) > 1.7e5:
# if abs(ring_mean/small_resid_mean) > 1e4:
if nclusters=='auto':
if abs(ellipse_mean/rms) > 1.4:
......@@ -451,12 +445,20 @@ def auto_clustering(fig, ax, df, wcs, resid_data, pix_arcmin_scale, nbright,
return final_clusters
# TODO
def voronoy_clustering():
"""
Use Voronoy clustering instead of fixed radius around sources
"""
pass
def main(img, resid, model, auto=True, add_manual=False, nclusters=5, boxsize=250,
nbright=80, cluster_radius=5, cluster_overlap=1.6):
path = os.path.split(os.path.abspath(img))[0]
output = os.path.splitext(img)[0]+'-clustered.txt'
imgbase = os.path.splitext(img)[0]
output = imgbase + '-clustered.txt'
df = pd.read_csv(model, skipinitialspace=True)
df['ra'] = df.Ra.apply(ra2deg)
......@@ -491,7 +493,7 @@ def main(img, resid, model, auto=True, add_manual=False, nclusters=5, boxsize=25
if clusters:
write_df(df, clusters, output=output)
fig.tight_layout()
fig.savefig(path+'/clustering.png')
fig.savefig(imgbase+'-clustering.png')
return output
......
imcal.py
+ 14
− 13
View file @ c7a66e8c
......@@ -326,13 +326,13 @@ def main(msin, outbase=None, cfgfile='imcal.yml'):
img1 = outbase + '_1'
img2 = outbase + '_2'
img3 = outbase + '_3'
img_final = outbase
img_final = outbase + '-dical'
img_ddsub = outbase + '-ddsub'
img_ddcal = outbase + '-ddcal'
mask0 = 'mask0.fits'
mask1 = 'mask1.fits'
mask2 = 'mask2.fits'
mask0 = outbase + '-mask0.fits'
mask1 = outbase + '-mask1.fits'
mask2 = outbase + '-mask2.fits'
model1 = outbase + '_model1.sourcedb'
model2 = outbase + '_model2.sourcedb'
......@@ -364,45 +364,46 @@ def main(msin, outbase=None, cfgfile='imcal.yml'):
threshold = img_max/cfg['clean0']['max_over_thresh']
wsclean(ms_split, outname=img0, automask=None, save_source_list=False, multifreq=False, mgain=None,
kwstring=f'-threshold {threshold}')
if not os.path.exists('mask0.fits') :
create_mask(img0 +'-image.fits', img0 +'-residual.fits', clipval=20, outname='mask0.fits')
create_mask(img0 +'-image.fits', img0 +'-residual.fits', clipval=20, outname=mask0)
# clean1
if not os.path.exists(img1 +'-image.fits') and (not os.path.exists(img1 +'-MFS-image.fits')):
wsclean(ms_split, outname=img1, **cfg['clean1']) # fast shallow clean
wsclean(ms_split, fitsmask=mask0, outname=img1, **cfg['clean1']) # fast shallow clean
if not os.path.exists(model1):
makesourcedb(img1+'-sources.txt', out=model1)
# dical1
if not os.path.exists(dical1):
dical1 = dical(ms_split, model1, msout=dical1, h5out=h5_1, **cfg['dical1'])
view_sols(h5_1, outname='dical1')
# clean2
if (not os.path.exists(img2 +'-image.fits')) and (not os.path.exists(img2 +'-MFS-image.fits')):
wsclean(dical1, outname=img2, **cfg['clean2'])
create_mask(img2 +'-image.fits', img2 +'-residual.fits', clipval=7, outname='mask1.fits')
wsclean(dical1, fitsmask=mask0, outname=img2, **cfg['clean2'])
create_mask(img2 +'-image.fits', img2 +'-residual.fits', clipval=7, outname=mask1)
if not os.path.exists(model2):
makesourcedb(img2+'-sources.txt', out=model2)
# dical2
if not os.path.exists(dical2):
dical2 = dical(dical1, model2, msout=dical2, h5out=h5_2, **cfg['dical2'])
view_sols(h5_2, outname='dical2')
# clean3
if (not os.path.exists(img3 +'-image.fits')) and (not os.path.exists(img3 +'-MFS-image.fits')):
wsclean(dical2, outname=img3, **cfg['clean3'])
create_mask(img3 +'-image.fits', img3 +'-residual.fits', clipval=5, outname='mask2.fits')
wsclean(dical2, fitsmask=mask1, outname=img3, **cfg['clean3'])
create_mask(img3 +'-image.fits', img3 +'-residual.fits', clipval=5, outname=mask2)
if not os.path.exists(model3):
makesourcedb(img3+'-sources.txt', out=model3)
# dical3
if not os.path.exists(dical3):
dical3 = dical(dical2, model3, msout=dical3, h5out=h5_3, **cfg['dical3'])
view_sols(h5_3, outname='dical3')
# clean4
if (not os.path.exists(img_final +'-image.fits')) and (not os.path.exists(img_final +'-MFS-image.fits')):
wsclean(dical3, outname=img_final, **cfg['clean4'])
wsclean(dical3, fitsmask=mask2, outname=img_final, **cfg['clean4'])
# Cluster
......
imcal.yml
+ 5
− 9
View file @ c7a66e8c
......@@ -24,7 +24,6 @@ clean1: # wsclean setup
mgain: 0
automask: 20
autothresh: 5
fitsmask: 'mask0.fits'
multiscale: False
kwstring: '-use-wgridder -parallel-deconvolution 1400' # use this for additional wsclean options, e.g. '-weight uniform -use-idg'
......@@ -32,7 +31,7 @@ dical1: # DPPP setup for direction independent calibration
solint: 20
mode: 'phaseonly'
uvlambdamin: 500 # Ignore baselines / channels with UV < uvlambdamin wavelengths.
# cal_nchan: 31 # number of chans with the same solutions
cal_nchan: 30 # number of chans with the same solutions
clean2:
imagesize: 3072
......@@ -40,7 +39,6 @@ clean2:
multifreq: 8
automask: 10
autothresh: 5
fitsmask: 'mask0.fits'
multiscale: True
kwstring: '-use-wgridder -parallel-deconvolution 1400 -parallel-gridding 8 -deconvolution-channels 3'
......@@ -48,7 +46,7 @@ dical2:
solint: 1
mode: 'phaseonly'
uvlambdamin: 500 # Ignore baselines / channels with UV < uvlambdamin wavelengths.
cal_nchan: 31 # number of chans with the same solutions
cal_nchan: 30 # number of chans with the same solutions
clean3:
imagesize: 3072
......@@ -56,7 +54,6 @@ clean3:
multifreq: 8
automask: 7
autothresh: 3.5
fitsmask: 'mask1.fits'
multiscale: True
kwstring: '-use-wgridder -parallel-deconvolution 1400 -parallel-gridding 8 -deconvolution-channels 3'
......@@ -64,7 +61,7 @@ dical3:
solint: 800
mode: 'diagonal'
uvlambdamin: 500 # Ignore baselines / channels with UV < uvlambdamin wavelengths.
cal_nchan: 31 # number of chans with the same solutions
cal_nchan: 30 # number of chans with the same solutions.
clean4:
imagesize: 3072
......@@ -73,7 +70,6 @@ clean4:
automask: 4.5
autothresh: 0.5
multiscale: True
fitsmask: 'mask2.fits'
kwstring: '-use-wgridder -parallel-deconvolution 1400 -parallel-gridding 8 -deconvolution-channels 3 -weight briggs 0.0'
####################### CLUSTERING #######################
......@@ -88,9 +84,9 @@ cluster:
######################## DD CALIBRATION #######################
ddcal: # see DPPP/DDECal documentation
solint: 120 # Solution interval in timesteps (1 ~ 30sec for Apertif).
solint: 120 # Solution interval in timesteps (1 corresponds to 30 seconds for Apertif).
mode: 'diagonal' # Type of constraint to apply.
nfreq: 15 # Number of channels in each channel block, for which the solution is assumed to be constant.
nfreq: 30 # Number of channels in each channel block, for which the solution is assumed to be constant.
startchan: 0
nchan: 0
uvlambdamin: 500 # Ignore baselines / channels with UV < uvlambdamin wavelengths.
......
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