def flatten_run_metadata(md):
# Flatten the tags field. Note this sets it to an empty string
# if it does not exist.
return strax.flatten_dict(
md,
separator='.',
keep=[strax.RUN_DEFAULTS_KEY, 'sub_run_spec', 'tags'])
def run_metadata(self, run_id, projection=None):
path = self._run_meta_path(run_id)
if osp.exists(path):
with open(path, mode='r') as f:
md = json.loads(f.read(), object_hook=json_util.object_hook)
if not projection:
return md
md = strax.flatten_dict(md, separator='.')
return {k: v for k, v in md.items() if k in projection}
else:
raise strax.RunMetadataNotAvailable(
f"No file at {path}, cannot find run metadata for {run_id}")
def scan_runs(self, check_available=tuple(), store_fields=tuple()):
"""Update and return self.runs with runs currently available
in all storage frontends.
:param check_available: Check whether these data types are available
Availability of xxx is stored as a boolean in the xxx_available
column.
:param store_fields: Additional fields from run doc to include
as rows in the dataframe.
The context options scan_availability and store_run_fields list
data types and run fields, respectively, that will always be scanned.
"""
store_fields = tuple(
set(
list(strax.to_str_tuple(store_fields)) +
['name', 'number', 'tags', 'mode'] +
list(self.context_config['store_run_fields'])))
check_available = tuple(
set(
list(strax.to_str_tuple(check_available)) +
list(self.context_config['check_available'])))
docs = None
for sf in self.storage:
_temp_docs = []
for doc in sf._scan_runs(store_fields=store_fields):
# If there is no number, make one from the name
if 'number' not in doc:
if 'name' not in doc:
raise ValueError(f"Invalid run doc {doc}, contains "
f"neither name nor number.")
doc['number'] = int(doc['name'])
# If there is no name, make one from the number
doc.setdefault('name', str(doc['number']))
doc.setdefault('mode', '')
# Flatten the tags field, if it exists
doc['tags'] = ','.join(
[t['name'] for t in doc.get('tags', [])])
# Flatten the rest of the doc (mainly in case the mode field
# is something deeply nested)
doc = strax.flatten_dict(doc, separator='.')
_temp_docs.append(doc)
if len(_temp_docs):
new_docs = pd.DataFrame(_temp_docs)
else:
new_docs = pd.DataFrame([], columns=store_fields)
if docs is None:
docs = new_docs
else:
# Keep only new runs (not found by earlier frontends)
docs = pd.concat(
[docs, new_docs[~np.in1d(new_docs['name'], docs['name'])]],
sort=False)
self.runs = docs
for d in tqdm(check_available, desc='Checking data availability'):
self.runs[d + '_available'] = np.in1d(self.runs.name.values,
self.list_available(d))
return self.runs