def populate_annos():
from protcur import document as ptcdoc
from protcur.core import annoSync
from protcur.analysis import Hybrid, protc
from hyputils.hypothesis import HypothesisHelper, group_to_memfile
from hyputils import hypothesis as hyp
ProtcurData.protc = protc
group = auth.get('hypothesis-group')
get_annos, annos, stream_thread, exit_loop = annoSync(
group_to_memfile(group + 'sparcur'),
helpers=(HypothesisHelper, Hybrid, protc),
group=group,
sync=False)
# FIXME hack to workaround bad api key init for hyutils until we can integrate orthauth
get_annos.api_token = auth.get('hypothesis-api-key')
annos.clear()
annos.extend([ptcdoc.Annotation(a) for a in get_annos()])
# reset classes in case some other class has populated them
# (e.g. during testing) FIXME this is a bad hack
protc.reset()
Hybrid.reset()
# FIXME this is expensive and slow to continually recompute
[protc(a, annos) for a in annos]
[Hybrid(a, annos) for a in annos]
def populate_annos(group_name='sparc-curation'):
from hyputils import hypothesis as hyp
if hyp.api_token == 'TOKEN': # FIXME does not work
hyp.api_token = auth.get('hypothesis-api-key')
group = auth.get('hypothesis-group')
get_annos, annos, stream_thread, exit_loop = annoSync(group_to_memfile(group + 'sparcur'),
helpers=(HypothesisHelper, Hybrid, protc),
group=group,
sync=False)
[protc(a, annos) for a in annos]
[Hybrid(a, annos) for a in annos]
def latest_ir(org_id=None):
if org_id is None:
org_id = auth.get('blackfynn-organization')
export = Export(auth.get_path('export-path'),
None,
None,
None,
latest=True,
org_id=org_id)
return export.latest_ir
def populate_annos():
from protcur.core import annoSync
from protcur.analysis import Hybrid, protc
from hyputils.hypothesis import HypothesisHelper, group_to_memfile
from hyputils import hypothesis as hyp
ProtcurData.protc = protc
group = auth.get('hypothesis-group')
get_annos, annos, stream_thread, exit_loop = annoSync(
group_to_memfile(group + 'sparcur'),
helpers=(HypothesisHelper, Hybrid, protc),
group=group,
sync=False)
# FIXME hack to workaround bad api key init for hyutils until we can integrate orthauth
get_annos.api_token = auth.get('hypothesis-api-key')
annos.clear()
annos.extend(get_annos())
# FIXME this is expensive and slow to continually recompute
[protc(a, annos) for a in annos]
[Hybrid(a, annos) for a in annos]
def populate_annos():
from protcur import document as ptcdoc
from protcur.core import annoSync
from protcur.analysis import Hybrid, protc
from hyputils.hypothesis import HypothesisHelper, group_to_memfile, AnnoReader
from hyputils import hypothesis as hyp
ProtcurData.protc = protc
group = auth.get('hypothesis-group')
memfile = group_to_memfile(group + 'sparcur')
#ar = AnnoReader(memfile, group)
#annos = ar.get_annos()
#ud = [a.updated for a in annos]
#lud_before = max(ud) if ud else None
#del ud
get_annos, annos, stream_thread, exit_loop = annoSync(
memfile,
helpers=(HypothesisHelper, Hybrid, protc),
group=group,
sync=False)
# FIXME hack to workaround bad api key init for hyutils until we can integrate orthauth
get_annos.api_token = auth.get('hypothesis-api-key')
annos.clear()
annos.extend([ptcdoc.Annotation(a) for a in get_annos()])
#lud_after = max([a.updated for a in annos])
# reset classes in case some other class has populated them
# (e.g. during testing) FIXME this is a bad hack
protc.reset()
Hybrid.reset()
#if lud_before == lud_after: # no new annos
# yeah we can use pickled ... but how :/
#return ???
# FIXME this is expensive and slow to continually recompute
[protc(a, annos) for a in annos]
[Hybrid(a, annos) for a in annos]
def _sparse_materialize(self, *args, sparse_limit=None):
""" use data from the remote mark or clear datasets as sparse """
if sparse_limit is None:
sparse_limit = auth.get(
'sparse-limit') # yay for yaml having int type
if self.is_dataset():
package_count = self._package_count()
sparse_remote = (False if sparse_limit is None else
package_count >= sparse_limit)
sparse_cache = self.is_sparse()
if sparse_remote:
if not sparse_cache:
self._mark_sparse()
elif sparse_cache: # strange case where number of packages decreases
self._clear_sparse()
else:
msg = 'at the moment only datasets can be marked as sparse'
raise NotImplementedError(msg)
def setUpClass(cls):
from sparcur.paths import BlackfynnCache, Path
from sparcur.config import auth
from sparcur.backends import BlackfynnRemote
from sparcur.simple import pull, fetch_metadata_files, fetch_files
test_datasets_real = auth.get_list('datasets-test')
nosparse = [
'N:dataset:bec4d335-9377-4863-9017-ecd01170f354', # mbf headers
]
test_datasets_real.extend(nosparse)
slot = auth._pathit('{:user-cache-path}/sparcur/objects-temp')
# FIXME slot needs to be handled transparently as an LRU cache
# that has multiple folder levels and stores only by uuid
# and probably lives in '{:user-cache-path}/sparcur/objects'
slot = slot if slot.exists() else None
cls.organization_id = auth.get('blackfynn-organization')
cls.BlackfynnRemote = BlackfynnRemote._new(Path, BlackfynnCache)
cls.BlackfynnRemote.init(cls.organization_id)
cls.anchor = cls.BlackfynnRemote.smartAnchor(path_project_container)
cls.anchor.local_data_dir_init(symlink_objects_to=slot)
cls.project_path = cls.anchor.local
list(cls.anchor.children) # side effect to retrieve top level folders
datasets = list(cls.project_path.children)
cls.test_datasets = [
d for d in datasets if d.cache_id in test_datasets_real
]
[d.rmdir() for d in datasets
if d.cache_id not in test_datasets_real] # for sanity
if not RealDataHelper._fetched:
RealDataHelper._fetched = True # if we fail we aren't going to try again
[
d._mark_sparse() for d in cls.test_datasets
if d.cache_id not in nosparse
] # keep pulls fastish
pull.from_path_dataset_file_structure_all(cls.project_path,
paths=cls.test_datasets)
fetch_metadata_files.main(cls.project_path)
fetch_files.main(cls.project_path)
self.last_export_updated_datetime = None
self.queued = False
self.fetching = False
self.exporting = False
self.last_export_failed = None
defaults = {o.name:o.value if o.argcount else None
for o in parse_defaults(clidoc)}
args = {**defaults, 'export': True, '--jobs': 1, 'schemas': False, 'protcur': False,
'--no-network': True, # XXX FIXME we need a way to fetch the data once and then reuse
'--i-know-what-i-am-doing': True,
'report': False, 'protocols': False,} # FIXME separate args for protcur export
options = Options(args, defaults)
project_id = auth.get('remote-organization')
path_source_dir = Path('~/files/sparc-datasets-test').expanduser().resolve() # FIXME hardcoded XXX resolve required to avoid mismatches
if not path_source_dir.exists():
path_source_dir.mkdir(parents=True)
cel = Celery('sparcur-cron',)
cel.conf.worker_hijack_root_logger = False
cel.conf.worker_prefetch_multiplier = 1
log.info(f'STATUS sparcur :id {project_id} :path {path_source_dir}')
# FIXME needed a dedicated worker for the cron queue
cel.conf.task_queues = (
Queue('cron', Exchange('cron'), routing_key='task.cron',
#max_priority=100,
