# realtime
#========================
realtimedir = config.get('general', 'realtimedir')
#========================
# calibration
#========================
calibrationdir = config.get('general', 'calibrationdir')
stride = config.getint('calibration', 'stride')
delay = config.getint('calibration', 'delay')
calibration_cache = dict(
(classifier,
idq.Cachefile(
idq.cache(calibrationdir, classifier, tag='_calibration%s' %
usertag))) for classifier in classifiers)
kde_cache = dict(
(classifier,
idq.Cachefile(
idq.cache(
calibrationdir, classifier, tag='_calibration-kde%s' % usertag)))
for classifier in classifiers)
min_num_gch = config.getfloat('calibration', 'min_num_gch')
min_num_cln = config.getfloat('calibration', 'min_num_cln')
emaillist = config.get('warnings', 'calibration')
errorthr = config.getfloat('warnings', 'calibration_errorthr')
uroc_nsamples = config.getint('calibration', 'urank_nsamples')
os.makedirs(realtimedir)
samples_header = config.get('realtime',
'dat_columns').split() ### headers for dat files
### slave the realtime job to the kw stride
stride = int(float(kwconfig['stride'])
) ### this is given as a decimal, so we must cast as float first
delay = config.getint('realtime', 'delay') ### buffer to let jobs finish
#========================
# train cache
#========================
traindir = config.get('general', 'traindir')
train_cache = dict(
(classifier, idq.Cachefile(idq.cache(traindir, classifier, tag='train')))
for classifier in classifiers)
for cache in train_cache.values():
cache.time = 0
#========================
# calibration cache
#========================
calibrationdir = config.get('general', 'calibrationdir')
calibration_cache = dict(
(classifier,
idq.Cachefile(idq.cache(calibrationdir, classifier, tag='calibration')))
for classifier in classifiers)
for cache in calibration_cache.values():
cache.time = 0
#========================
# train
#========================
traindir = config.get('general', 'traindir')
if ovl: ### need snglchandir
snglchndir = config.get('general', 'snglchndir')
stride = config.getint('train', 'stride')
delay = config.getint('train', 'delay')
#train_script = config.get('condor', 'train')
train_cache = dict(
(classifier,
idq.Cachefile(idq.cache(traindir, classifier, tag='_train%s' % usertag)))
for classifier in classifiers)
build_auxmvc_vectors = mla and (
not os.path.exists(realtimedir)
) ### if realtimedir does not exist, we cannot rely on patfiles from the realtime job
### we need to build our own auxmvc_vectors
max_gch_samples = config.getint("train", "max-glitch-samples")
max_cln_samples = config.getint("train", "max-clean-samples")
#========================
# data discovery
#========================
if not opts.ignore_science_segments:
### load settings for accessing dmt segment files
clean_window = config.getfloat('realtime', 'clean_window')
clean_threshold = config.getfloat('realtime', 'clean_threshold')
#========================
# train
#========================
traindir = config.get('general', 'traindir')
if ovl: ### need snglchandir
snglchndir = config.get('general', 'snglchndir')
stride = config.getint('train', 'stride')
delay = config.getint('train', 'delay')
#train_script = config.get('condor', 'train')
train_cache = dict( (classifier, idq.Cachefile(idq.cache(traindir, classifier, tag='_train%s'%usertag))) for classifier in classifiers )
build_auxmvc_vectors = mla and (not os.path.exists(realtimedir)) ### if realtimedir does not exist, we cannot rely on patfiles from the realtime job
### we need to build our own auxmvc_vectors
max_gch_samples = config.getint("train", "max-glitch-samples")
max_cln_samples = config.getint("train", "max-clean-samples")
#========================
# data discovery
#========================
if not opts.ignore_science_segments:
### load settings for accessing dmt segment files
# dmt_segments_location = config.get('get_science_segments', 'xmlurl')
dq_name = config.get('get_science_segments', 'include')
# dq_name = config.get('get_science_segments', 'include').split(':')[1]