def upload(self, token_ids, token_type, file_name):
pc = get_picas_credentials.picas_cred()
if self.pc_database:
pc.database = self.pc_database
client = CouchDB(pc.user,
pc.password,
url='https://picas-lofar.grid.surfsara.nl:6984',
connect=True)
db = client[pc.database]
tl = TokenList(token_type=token_type, database=db)
for t_id in token_ids:
tl.append(
caToken(database=db, token_type=token_type, token_id=t_id))
tl.fetch()
tl.add_attachment(file_name, file_name.split('/')[-1])
tok.build(TokenJsonBuilder(jsonfile))
tok.save()
tok.add_attachment(attachment_name='srm.txt', filename='temp_srm.txt')
tl.append(tok)
tl.save()
for tok in tl:
tok['OBSID'] = obsid
tok['PIPELINE_STEP'] = 'lb_sub6asec_cal1'
tok['status'] = 'queued'
tok.save()
tl.add_attachment(
attachment_name='step1_subtract_lotss.parset',
filename=
'/project/sksp/Software/lofar-highres-widefield/testdir/test_with_GRID_LRT/step1_subtract_lotss.parset'
)
print('5) Adding status views.')
view_cal = TokenView('step1_subtract_lotss',
condition='doc.PIPELINE_STEP=="lb_sub6asec_cal1"',
emit_values=('doc._id', 'doc.status'))
tl.add_token_views()
tl.add_view(view_cal)
tl.save()
print('6) Create and launch the jobs.')
j = submit.SpiderLauncher(numjobs=len(d.keys()),
token_type=tok_type,
if (tok_type + '_scal_' + source + str(cal_obsid)) not in token_ids:
tok = caToken(database=db,
token_type=tok_type,
token_id=tok_type + '_scal_' + source + str(cal_obsid))
with open('temp_srm_{:s}.txt'.format(source), 'w') as f:
f.write('\n'.join(v))
tok.build(TokenJsonBuilder(jsonfile))
tok.save()
tok.add_attachment(attachment_name='srm.txt',
filename='temp_srm_{:s}.txt'.format(source))
tl.append(tok)
else:
continue
tl.add_attachment(
attachment_name='step3_selfcal_calibrators.parset',
filename=
'/project/sksp/Software/lofar-highres-widefield/testdir/test_with_GRID_LRT/step3_selfcal_calibrators.parset'
)
tl.save()
for tok in tl:
tok['OBSID'] = obsid
tok['PIPELINE_STEP'] = 'lb_selfcal_cal1'
tok['status'] = 'queued'
tok.save()
print('5) Adding status views.')
view_cal = TokenView('step3_selfcal_cals',
condition='doc.PIPELINE_STEP=="lb_selfcal_cal1"',
emit_values=('doc._id', 'doc.status'))
tl.add_token_views()