def make_analysis_forms(
basepath="/orange/adamginsburg/web/secure/ALMA-IMF/October31Release/",
base_form_url="https://docs.google.com/forms/d/e/1FAIpQLSczsBdB3Am4znOio2Ky5GZqAnRYDrYTD704gspNu7fAMm2-NQ/viewform?embedded=true",
dontskip_noresid=False):
import glob
from diagnostic_images import load_images, show as show_images
from astropy import visualization
import pylab as pl
savepath = f'{basepath}/quicklooks'
try:
os.mkdir(savepath)
except:
pass
filedict = {
(field, band, config, robust, selfcal): glob.glob(
f"{field}/B{band}/{imtype}{field}*_B{band}_*_{config}_robust{robust}*selfcal{selfcal}*.image.tt0*.fits"
)
for field in
"G008.67 G337.92 W43-MM3 G328.25 G351.77 G012.80 G327.29 W43-MM1 G010.62 W51-IRS2 W43-MM2 G333.60 G338.93 W51-E G353.41"
.split() for band in (3, 6)
#for config in ('7M12M', '12M')
for config in ('12M', )
#for robust in (-2, 0, 2)
for robust in (0, ) for selfcal in ("", ) + tuple(range(0, 9))
for imtype in (('', ) if 'October31' in basepath else ('cleanest/',
'bsens/'))
}
badfiledict = {key: val for key, val in filedict.items() if len(val) == 1}
print(f"Bad files: {badfiledict}")
filedict = {key: val for key, val in filedict.items() if len(val) > 1}
filelist = [key + (fn, ) for key, val in filedict.items() for fn in val]
prev = 'index.html'
flist = []
#for field in "G008.67 G337.92 W43-MM3 G328.25 G351.77 G012.80 G327.29 W43-MM1 G010.62 W51-IRS2 W43-MM2 G333.60 G338.93 W51-E G353.41".split():
##for field in ("G333.60",):
# for band in (3,6):
# for config in ('7M12M', '12M'):
# for robust in (-2, 0, 2):
# # for not all-in-the-same-place stuff
# fns = [x for x in glob.glob(f"{field}/B{band}/{field}*_B{band}_*_{config}_robust{robust}*selfcal[0-9]*.image.tt0*.fits") ]
# for fn in fns:
for ii, (field, band, config, robust, selfcal, fn) in enumerate(filelist):
image = fn
basename, suffix = image.split(".image.tt0")
if 'diff' in suffix or 'bsens-cleanest' in suffix:
continue
outname = basename.split("/")[-1]
if prev == outname + ".html":
print(
f"{ii}: {(field, band, config, robust, fn)} yielded the same prev "
f"{prev} as last time, skipping.")
continue
jj = 1
while jj < len(filelist):
if ii + jj < len(filelist):
next_ = filelist[ii + jj][5].split(".image.tt0")[0].split(
"/")[-1] + ".html"
else:
next_ = "index.html"
if next_ == outname + ".html":
jj = jj + 1
else:
break
assert next_ != outname + ".html"
try:
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
print(f"{ii}: {(field, band, config, robust, fn, selfcal)}"
f" basename='{basename}', suffix='{suffix}'")
imgs, cubes = load_images(basename, suffix=suffix)
except KeyError as ex:
print(ex)
raise
except Exception as ex:
print(f"EXCEPTION: {type(ex)}: {str(ex)}")
raise
continue
norm = visualization.ImageNormalize(
stretch=visualization.AsinhStretch(),
interval=visualization.PercentileInterval(99.95))
# set the scaling based on one of these...
# (this call inplace-modifies logn, according to the docs)
if 'residual' in imgs:
norm(imgs['residual'][imgs['residual'] == imgs['residual']])
imnames_toplot = ('mask', 'model', 'image', 'residual')
elif 'image' in imgs and dontskip_noresid:
imnames_toplot = (
'image',
'mask',
)
norm(imgs['image'][imgs['image'] == imgs['image']])
else:
print(
f"Skipped {fn} because no image OR residual was found. imgs.keys={imgs.keys()}"
)
continue
pl.close(1)
pl.figure(1, figsize=(14, 6))
show_images(imgs, norm=norm, imnames_toplot=imnames_toplot)
pl.savefig(f"{savepath}/{outname}.png", dpi=150, bbox_inches='tight')
metadata = {
'field': field,
'band': band,
'selfcal': selfcal, #get_selfcal_number(basename),
'array': config,
'robust': robust,
'finaliter': 'finaliter' in fn,
}
make_quicklook_analysis_form(filename=outname,
metadata=metadata,
savepath=savepath,
prev=prev,
next_=next_,
base_form_url=base_form_url)
metadata['outname'] = outname
metadata['suffix'] = suffix
if robust == 0:
# only keep robust=0 for simplicity
flist.append(metadata)
prev = outname + ".html"
#make_rand_html(savepath)
make_index(savepath, flist)
return flist
import pylab as pl
from diagnostic_images import load_images, show
imgs_before_b6, cubes_before_b6 = load_images('W51-E_B6_uid___A001_X1296_X215_continuum_merged_12M_robust0')
imgs_after_b6, cubes_after_b6 = load_images('W51-E_B6_uid___A001_X1296_X215_continuum_merged_12M_robust0_selfcal1')
show(imgs_before_b6, vmin=-0.001, vmax=0.01)
pl.savefig("W51-E_B6_before_selfcal.png", bbox_inches='tight')
show(imgs_after_b6, vmin=-0.001, vmax=0.01)
pl.savefig("W51-E_B6_after_selfcal.png", bbox_inches='tight')
show(imgs_before_b6, vmin=-0.001, vmax=0.3, zoom=[slice(380,900), slice(480,760)])
pl.savefig("W51-E_B6_before_selfcal_zoom.png", bbox_inches='tight')
show(imgs_after_b6, vmin=-0.001, vmax=0.3, zoom=[slice(380,900), slice(480,760)])
pl.savefig("W51-E_B6_after_selfcal_zoom.png", bbox_inches='tight')
imgs_before_b3, cubes_before_b3 = load_images('W51-E_B3_uid___A001_X1296_X10b_continuum_merged_12M_robust0')
imgs_after_b3, cubes_after_b3 = load_images('W51-E_B3_uid___A001_X1296_X10b_continuum_merged_12M_robust0_selfcal1')
show(imgs_before_b3, vmin=-0.001, vmax=0.01)
pl.savefig("W51-E_B3_before_selfcal.png", bbox_inches='tight')
show(imgs_before_b3, vmin=-0.001, vmax=0.1, zoom=[slice(680,1200),slice(850,1200)])
pl.savefig("W51-E_B3_before_selfcal_zoom.png", bbox_inches='tight')
show(imgs_after_b3, vmin=-0.001, vmax=0.01)
pl.savefig("W51-E_B3_after_selfcal.png", bbox_inches='tight')