def _get_protocol_json(self, uri):
#juri = uri + '.json'
logd.info(uri)
pi = get_right_id(uri)
if 'protocols.io' in pi:
pioid = pi.slug # FIXME normalize before we ever get here ...
log.info(pioid)
else:
msg = f'protocol uri is not from protocols.io {pi} {self.id}'
logd.error(msg)
self.addError(msg)
return
#uri_path = uri.rsplit('/', 1)[-1]
apiuri = 'https://protocols.io/api/v3/protocols/' + pioid
#'https://www.protocols.io/api/v3/groups/sparc/protocols'
#apiuri = 'https://www.protocols.io/api/v3/filemanager/folders?top'
#print(apiuri, header)
log.debug('going to network for protocols')
resp = requests.get(apiuri, headers=self._pio_header)
#log.info(str(resp.request.headers))
if resp.ok:
try:
j = resp.json() # the api is reasonably consistent
except BaseException as e:
log.exception(e)
breakpoint()
raise e
return j
else:
try:
j = resp.json()
sc = j['status_code']
em = j['error_message']
msg = f'protocol issue {uri} {resp.status_code} {sc} {em} {self.id!r}'
logd.error(msg)
self.addError(msg)
# can't return here because of the cache
except BaseException as e:
log.exception(e)
logd.error(f'protocol no access {uri} {self.id!r}')
def triples_gen(self):
rm = self._source
# FIXME there doesn't seem to be a section that tells me the name
# of top level model so I have to know its name beforhand
# the id is in the model, having the id in the resource map
# prevents issues if these things get sent decoupled
id = rm['id']
mid = id.replace(' ', '-')
links = rm[id]['links']
#linknodes = [n for n in rm[id]['nodes'] if n['class'] == 'Link'] # visible confusion
st = []
from_to = []
ot = None
yield from self.apinatbase()
for link in links:
if 'conveyingType' in link:
if link['conveyingType'] == 'ADVECTIVE':
p_is = TEMP.isAdvectivelyConnectedTo
p_from = TEMP.advectivelyConnectsFrom
p_to = TEMP.advectivelyConnectsTo
p_cmat = TEMP.advectivelyConnectsMaterial
diffusive = False
elif link['conveyingType'] == 'DIFFUSIVE':
p_is = TEMP.isDiffusivelyConnectedTo
p_from = TEMP.diffusivelyConnectsFrom
p_to = TEMP.diffusivelyConnectsTo
p_cmat = TEMP.diffusivelyConnectsMaterial
diffusive = True
else:
log.critical(f'unhandled conveying type {link}')
continue
source = link['source']
target = link['target']
ok = True
if len(from_to) == 2: # otherwise
st = []
from_to = []
for i, e in enumerate((source, target)):
ed = rm[e]
if 'external' not in ed:
if not i and from_to:
# TODO make sure the intermediate ids match
pass
else:
ok = False
break
else:
st.append(e)
from_to.append(OntId(ed['external'][0]))
conveying = link['conveyingLyph']
cd = rm[conveying]
if 'external' in cd:
old_ot = ot
ot = OntTerm(cd['external'][0])
yield ot.u, rdf.type, owl.Class
yield ot.u, TEMP.internalId, rdflib.Literal(conveying)
yield ot.u, rdfs.label, rdflib.Literal(ot.label)
yield from self.materialTriples(
ot.u, link, p_cmat) # FIXME locate this correctly
if ok:
u, d = from_to
if st[0] == source:
yield u, rdfs.label, rdflib.Literal(
OntTerm(u).label)
yield u, rdf.type, owl.Class
yield from cmb.restriction.serialize(
ot.u, p_from, u)
if st[1] == target:
yield d, rdfs.label, rdflib.Literal(
OntTerm(d).label)
yield d, rdf.type, owl.Class
yield from cmb.restriction.serialize(ot.u, p_to, d)
if old_ot is not None and old_ot != ot:
yield from cmb.restriction.serialize(
ot.u, p_from, old_ot.u)
if diffusive:
# we can try to hack this using named individuals
# but it is not going to do exactly what is desired
s_link = TEMP[f'ApiNATOMY/{mid}/{link["id"]}']
s_cd = TEMP[f'ApiNATOMY/{mid}/{cd["id"]}']
yield s_link, rdf.type, owl.NamedIndividual
yield s_link, rdf.type, TEMP.diffusiveLink # FIXME I'm not sure these go in the model ...
yield s_cd, rdf.type, owl.NamedIndividual
if 'external' in cd and cd['external']:
oid = OntId(cd['external'][0])
yield s_cd, rdf.type, oid.u
ot = oid.asTerm()
if ot.label:
yield oid.u, rdfs.label, ot.label
else:
yield s_cd, rdf.type, TEMP.conveyingLyph
for icd in cd['inCoalescences']:
dcd = rm[icd]
log.info(lj(dcd))
s_icd = TEMP[f'ApiNATOMY/{mid}/{dcd["id"]}']
yield s_cd, TEMP.partOfCoalescence, s_icd
yield s_icd, rdf.type, owl.NamedIndividual
yield s_icd, rdf.type, TEMP[
'ApiNATOMY/Coalescence']
if 'external' in dcd and dcd['external']:
oid = OntId(dcd['external'][0])
yield s_icd, rdf.type, oid.u
ot = oid.asTerm()
if ot.label:
yield oid.u, rdfs.label, ot.label
for lyphid in dcd['lyphs']:
ild = rm[lyphid]
log.info(lj(ild))
if 'external' in ild and ild['external']:
yield s_icd, TEMP.hasLyphWithMaterial, OntId(
ild['external'][0])
if not ok:
logd.info(f'{source} {target} issue')
continue
for inid, e in zip(st, from_to):
yield e.u, rdf.type, owl.Class
yield e.u, rdfs.label, rdflib.Literal(OntTerm(e).label)
yield e.u, TEMP.internalId, rdflib.Literal(inid)
f, t = from_to
yield from cmb.restriction.serialize(f.u, p_is, t.u)
def route_reports_errors_id(id, ext=wrap_tables):
tables, formatted_title, title = report.errors(id=id)
log.info(id)
if tables is None:
return 'Not found', 404
return wrap_tables(formatted_title, *tables, title=title)
def datame(d,
ca,
timestamp,
helpers=None,
log_level=logging.INFO,
dp=_p,
evil=[False],
dumb=False):
""" sigh, pickles """
log_names = ('sparcur', 'idlib', 'protcur', 'orthauth', 'ontquery',
'augpathlib', 'pyontutils')
for log_name in log_names:
log = logging.getLogger(log_name)
if not log.handlers:
log = makeSimpleLogger(log_name)
log.setLevel(log_level)
log.info(f'{log_name} had no handler')
else:
if log.level != log_level:
log.setLevel(log_level)
rc = d.path._cache_class._remote_class
if not hasattr(rc, '_cache_anchor'):
rc._setup()
rc.anchorTo(ca)
if not hasattr(BlackfynnCache, '_anchor'):
# the fact that we only needed this much later in time
# tells me that we had actually done an excellent job
# of firewalling the validation pipeline from anything
# related to the cache beyond the xatter data
# can't use ca.__class__ because it is the posix variant of # _cache_class
BlackfynnCache._anchor = ca
prp = d.path.project_relative_path
if helpers is not None:
d.add_helpers(helpers)
out_path = (dp / d.id).with_suffix('.json')
if out_path.exists() and dumb:
if not evil[0]: # FIXME this is SO DUMB to do in here, but ...
from pysercomb.pyr import units as pyru
[register_type(c, c.tag) for c in (pyru._Quant, pyru.Range)]
pyru.Term._OntTerm = OntTerm # the tangled web grows ever deeper :x
evil[0] = True
log.warning(f'loading from path {out_path}')
# FIXME this is _idiotically_ slow with joblib
# multiple orders of magnitude faster just using listcomp
with open(out_path, 'rt') as f:
return fromJson(json.load(f))
blob_dataset = d.data_for_export(timestamp)
with open(out_path.with_suffix('.raw.json'),
'wt') as f: # FIXME XXXXXXXXXXXXXXXXXXXXXXXXXXXX
json.dump(blob_dataset, f, sort_keys=True, indent=2, cls=JEncode)
try:
pipe = pipes.IrToExportJsonPipeline(
blob_dataset) # FIXME network sandbox violation
blob_export = pipe.data
with open(out_path, 'wt') as f: # FIXME XXXXXXXXXXXXXXXXXXXXXXXXXXXX
json.dump(blob_export, f, sort_keys=True, indent=2, cls=JEncode)
except Exception as e:
log.exception(e)
log.critical(f'error during fancy json export, see previous log entry')
return blob_dataset
def validate_path_json_metadata(cls, path_meta_blob):
from sparcur.core import HasErrors # FIXME
he = HasErrors(pipeline_stage=cls.__name__ +
'.validate_path_json_metadata')
mimetypes, suffixes = cls._file_type_status_lookup(
) # SIGH this overhead is 2 function calls and a branch
for i, path_meta in enumerate(path_meta_blob['data']):
if path_meta['basename'] in cls._banned_basenames:
msg = f'illegal file detect {path_meta["basename"]}'
dsrp = path_meta['dataset_relative_path']
if he.addError(msg, path=dsrp, json_path=('data', i)):
logd.error(msg)
status = 'banned'
path_meta['status'] = status
continue
if 'magic_mimetype' in path_meta and 'mimetype' in path_meta:
# FIXME NOT clear whether magic_mimetype should be used by itself
# usually magic and file extension together work, magic by itself
# can give some completely bonkers results
source = 'magic_mimetype'
mimetype = path_meta['magic_mimetype']
muggle_mimetype = path_meta['mimetype']
if mimetype != muggle_mimetype:
msg = f'mime types do not match {mimetype} != {muggle_mimetype}'
dsrp = path_meta['dataset_relative_path']
if he.addError(msg, path=dsrp, json_path=('data', i)):
log.error(msg)
elif 'magic_mimetype' in path_meta:
source = 'magic_mimetype'
mimetype = path_meta['magic_mimetype']
elif 'mimetype' in path_meta:
source = 'mimetype'
mimetype = path_meta['mimetype']
else:
mimetype = None
if mimetype is not None:
try:
status = mimetypes[mimetype]
if status == 'banned':
msg = f'banned mimetype detected {mimetype}'
dsrp = path_meta['dataset_relative_path']
if he.addError(msg,
path=dsrp,
json_path=('data', i, source)):
logd.error(msg)
except KeyError as e:
status = 'known'
if mimetype not in cls._unclassified_mimes:
cls._unclassified_mimes.add(mimetype)
log.info(f'unclassified mimetype {mimetype}')
else:
status = 'unknown'
dsrp = path_meta['dataset_relative_path']
if isinstance(dsrp, str):
if not dsrp:
msg = f'FIXME top level folder needs a mimetype!'
else:
msg = f'unknown mimetype {path_meta["basename"]}'
else:
msg = f'unknown mimetype {"".join(dsrp.suffixes)}'
cls._unknown_suffixes.add(tuple(dsrp.suffixes))
if he.addError(msg, path=dsrp, json_path=('data', i)):
logd.warning(msg)
path_meta['status'] = status
if he._errors_set:
he.embedErrors(path_meta_blob)
def data(self):
""" get the 'cached' data which isn't really cached at the moment
once we implement an index for local files then we can hit that
first from here """
# we don't keep two copies of the local data
# unless we are doing a git-like thing
if self.is_dir():
raise TypeError('can\'t retrieve data for a directory')
meta = self.meta
if meta.file_id is None:
raise NotImplementedError('can\'t fetch data without a file id')
#cands = list(self.local_object_cache_dir.glob(self.cache_key))
# FIXME this does not play well with old_id ...
# can probably get away with just globing for the old_id in
# most cases
# TODO where to store the chain of prior versions? i.e. do
# we just keep the xattrs in the object cache? how about file moves?
# sigh git ...
rgen = None
if self.local_object_cache_path.exists():
locsize = self.local_object_cache_path.size
if locsize != meta.size:
msg = (f'Partial download detected {locsize} != {meta.size} at'
f'\n{self.local_object_cache_path}')
log.info(msg)
size = self.local_object_cache_path.size
kwargs = {}
if size > 0:
if (self.local == self.local_object_cache_path
and size > 4096): # FIXME hardcoded chunksize
# XXX there is a fantastic edge case where if
# you try to read and write from the same file
# only the first chunk will be written and if
# you are retrieving from remote then the offset
# would be greater than the chunksize so there
# will be a gap, so we set chunksize here and
# issue a critical log
msg = ('You probably did not mean to do this. '
f'Refetching {size - 4096} bytes.')
log.critical(msg)
kwargs['ranges'] = ((4096, ), )
else:
kwargs['ranges'] = ((size, ), )
if not hasattr(self._remote_class, '_api'):
# see note below
self._remote_class.anchorToCache(self.anchor)
rgen = self._remote_class.get_file_by_id(
meta.id, meta.file_id, **kwargs)
gen = chain((next(rgen), ), self.local_object_cache_path.data)
else:
gen = chain(
(f'from local cache {self.local_object_cache_path}', ),
self.local_object_cache_path.data)
else:
if not hasattr(self._remote_class, '_api'):
# NOTE we do not want to dereference self.remote
# in this situation because we just want the file
# not the FS metadata, so we have to ensure that _api
# is bound
self._remote_class.anchorToCache(self.anchor)
gen = self._remote_class.get_file_by_id(meta.id, meta.file_id)
try:
self.data_headers = next(gen)
except exc.NoRemoteFileWithThatIdError as e:
log.error(f'{self} {e}')
raise exc.CacheNotFoundError(
f'{self}'
) from e # have to raise so that we don't overwrite the file
log.log(9, self.data_headers)
if self.local_object_cache_path.exists():
yield from gen
if rgen is None:
return
yield from self.local_object_cache_path._data_setter(rgen,
append=True)
else:
# FIXME we MUST write the metadata first so that we know the expected size
# so that in the event that the generator is only partially run out we know
# that we can pick up where we left off with the fetch, this also explains
# why all the cases where the cached data size did not match were missing
# xattrs entirely
if not self.local_object_cache_path.parent.exists():
# FIXME sigh, no obvious way around having to check
# every time other than creating all the cache
# subfolders in advance
self.local_object_cache_path.parent.mkdir()
self.local_object_cache_path.touch()
self.local_object_cache_path.cache_init(meta)
yield from self.local_object_cache_path._data_setter(gen)
ls = self.local_object_cache_path.size
if ls != meta.size:
self.local_object_cache_path.unlink()
msg = f'{ls} != {meta.size} for {self}'
raise ValueError(msg) # FIXME TODO
def cleanup_redis(sender, **kwargs):
""" For the time being ensure that any old data about process
state is wiped when we restart. """
log.info('cleaning up old redis connection ...')
reset_redis_keys(conn)
populate_existing_redis(conn)
'--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,
queue_arguments={'x-max-priority': 10},
),
Queue('export', Exchange('export'), routing_key='task.export',
#max_priority=5,
queue_arguments={'x-max-priority': 1},
),
Queue('default', Exchange('default'), routing_key='task.default',
#max_priority=1,
queue_arguments={'x-max-priority': 1},
), # fallthrough