def run(job_name, supplied_mon_coords=[]):
pipe_config = initialize_pipeline_config(
os.path.join(os.getcwd(), "pipeline.cfg"),
job_name)
# get parallelise props. Defaults to multiproc with autodetect num cores
parallelise = pipe_config.get('parallelise', {})
distributor = os.environ.get('TKP_PARALLELISE', parallelise.get('method',
'multiproc'))
runner = Runner(distributor=distributor,
cores=parallelise.get('cores', 0))
debug = pipe_config.logging.debug
#Setup logfile before we do anything else
log_dir = pipe_config.logging.log_dir
setup_log_file(log_dir, debug)
job_dir = pipe_config.DEFAULT.job_directory
if not os.access(job_dir, os.X_OK):
msg = "can't access job folder %s" % job_dir
logger.error(msg)
raise IOError(msg)
logger.info("Job dir: %s", job_dir)
db_config = get_database_config(pipe_config.database, apply=True)
dump_database_backup(db_config, job_dir)
job_config = load_job_config(pipe_config)
se_parset = job_config.source_extraction
deruiter_radius = job_config.association.deruiter_radius
beamwidths_limit = job_config.association.beamwidths_limit
new_src_sigma = job_config.transient_search.new_source_sigma_margin
all_images = imp.load_source('images_to_process',
os.path.join(job_dir,
'images_to_process.py')).images
logger.info("dataset %s contains %s images" % (job_name, len(all_images)))
logger.info("performing database consistency check")
if not dbconsistency.check():
logger.error("Inconsistent database found; aborting")
return 1
dataset_id = create_dataset(job_config.persistence.dataset_id,
job_config.persistence.description)
if job_config.persistence.dataset_id == -1:
store_config(job_config, dataset_id) # new data set
if supplied_mon_coords:
dbgen.insert_monitor_positions(dataset_id,supplied_mon_coords)
else:
job_config_from_db = fetch_config(dataset_id) # existing data set
if check_job_configs_match(job_config, job_config_from_db):
logger.debug("Job configs from file / database match OK.")
else:
logger.warn("Job config file has changed since dataset was "
"first loaded into database. ")
logger.warn("Using job config settings loaded from database, see "
"log dir for details")
job_config = job_config_from_db
if supplied_mon_coords:
logger.warn("Monitor positions supplied will be ignored. "
"(Previous dataset specified)")
dump_configs_to_logdir(log_dir, job_config, pipe_config)
logger.info("performing persistence step")
image_cache_params = pipe_config.image_cache
imgs = [[img] for img in all_images]
rms_est_sigma = job_config.persistence.rms_est_sigma
rms_est_fraction = job_config.persistence.rms_est_fraction
metadatas = runner.map("persistence_node_step", imgs,
[image_cache_params, rms_est_sigma, rms_est_fraction])
metadatas = [m[0] for m in metadatas if m]
logger.info("Storing images")
image_ids = store_images(metadatas,
job_config.source_extraction.extraction_radius_pix,
dataset_id)
db_images = [Image(id=image_id) for image_id in image_ids]
logger.info("performing quality check")
urls = [img.url for img in db_images]
arguments = [job_config]
rejecteds = runner.map("quality_reject_check", urls, arguments)
good_images = []
for image, rejected in zip(db_images, rejecteds):
if rejected:
reason, comment = rejected
steps.quality.reject_image(image.id, reason, comment)
else:
good_images.append(image)
if not good_images:
logger.warn("No good images under these quality checking criteria")
return
grouped_images = group_per_timestep(good_images)
timestep_num = len(grouped_images)
for n, (timestep, images) in enumerate(grouped_images):
msg = "processing %s images in timestep %s (%s/%s)"
logger.info(msg % (len(images), timestep, n+1, timestep_num))
logger.info("performing source extraction")
urls = [img.url for img in images]
arguments = [se_parset]
extraction_results = runner.map("extract_sources", urls, arguments)
logger.info("storing extracted sources to database")
# we also set the image max,min RMS values which calculated during
# source extraction
for image, results in zip(images, extraction_results):
image.update(rms_min=results.rms_min, rms_max=results.rms_max,
detection_thresh=se_parset['detection_threshold'],
analysis_thresh=se_parset['analysis_threshold'])
dbgen.insert_extracted_sources(image.id, results.sources, 'blind')
logger.info("performing database operations")
for image in images:
logger.info("performing DB operations for image %s" % image.id)
logger.info("performing source association")
dbass.associate_extracted_sources(image.id,
deRuiter_r=deruiter_radius,
new_source_sigma_margin=new_src_sigma)
expiration = job_config.source_extraction.expiration
all_fit_posns, all_fit_ids = steps_ff.get_forced_fit_requests(image,
expiration)
if all_fit_posns:
successful_fits, successful_ids = steps_ff.perform_forced_fits(
all_fit_posns, all_fit_ids, image.url, se_parset)
steps_ff.insert_and_associate_forced_fits(image.id,successful_fits,
successful_ids)
dbgen.update_dataset_process_end_ts(dataset_id)
logger.info("calculating variability metrics")
execute_store_varmetric(dataset_id)
def varmetric(dataset_id):
logger.info("calculating variability metrics")
execute_store_varmetric(dataset_id)
def test_execute_store_varmetric_twice(self):
session = self.db.Session()
execute_store_varmetric(session=session, dataset_id=self.dataset.id)
self.session.flush()
execute_store_varmetric(session=session, dataset_id=self.dataset.id)
self.session.flush()
def test_execute_store_varmetric_twice(self):
session = self.db.Session()
execute_store_varmetric(session=session, dataset_id=self.dataset.id)
self.session.flush()
execute_store_varmetric(session=session, dataset_id=self.dataset.id)
self.session.flush()
def test_execute_store_varmetric(self):
session = self.db.Session()
execute_store_varmetric(session=session, dataset_id=self.dataset.id)
def varmetric(dataset_id):
logger.info("calculating variability metrics")
execute_store_varmetric(dataset_id)