def get_all_app_info(tag, user, nms_url, catalog_url):
if tag not in ["release", "beta", "dev"]:
raise ValueError("tag must be one of 'release', 'beta', or 'dev'")
nms = NarrativeMethodStore(nms_url)
catalog = Catalog(catalog_url)
apps = nms.list_methods({"tag": tag})
app_infos = {}
module_versions = {}
for a in apps:
# ignore empty apps or apps in IGNORE_CATEGORIES
if not a or not set(a.get('categories')).isdisjoint(IGNORE_CATEGORIES):
continue
# add short version of input/output types
for target_type in ['input_types', 'output_types']:
a['short_{}'.format(target_type)] = _shorten_types(
a.get(target_type))
app_infos[a["id"].lower()] = {"info": a}
if "module_name" in a:
module_versions[a["module_name"].lower()] = a.get("ver")
favorites = catalog.list_favorites(user)
for fav in favorites:
app_id = fav["module_name_lc"] + "/" + fav["id"]
if app_id in app_infos:
app_infos[app_id]["favorite"] = fav["timestamp"]
return {"module_versions": module_versions, "app_infos": app_infos}
def _get_app_metadata(self, nb, nms_url: str) -> dict:
"""
Returns a structure containing app metadata and citations.
{
meta: str,
citations: [{
heading: "Released Apps" (some readable str, not just "dev"),
app_list: {
app_name_1: [{
link: 'some link',
display_text: 'publication_text'
}],
app_name_2: [{
link: 'some other link',
display_text: 'other publication'
}]
}
}]
}
"""
# will be tag -> app_id
apps = defaultdict(set)
for cell in nb.cells:
if 'kbase' in cell.metadata:
kb_meta = cell.metadata.get('kbase', {})
if kb_meta.get('type') == 'app' and "app" in kb_meta:
apps[kb_meta["app"].get("tag",
"dev")].add(kb_meta["app"]["id"])
citations = defaultdict(dict)
app_names = set() # the "metadata" is just a list of unique app names.
nms = NarrativeMethodStore(url=nms_url)
for tag in apps:
nms_inputs = {"ids": list(apps[tag]), "tag": tag}
try:
app_infos = nms.get_method_full_info(nms_inputs)
except Exception as e:
app_infos = []
for info in app_infos:
app_names.add(info["name"])
if "publications" in info:
citations[tag][info["name"]] = info["publications"]
parsed_citations = list()
tag_map = {
"release": "Released Apps",
"beta": "Apps in Beta",
"dev": "Apps in development"
}
for tag in ["release", "beta", "dev"]:
if tag in citations:
parsed_citations.append({
"heading": tag_map[tag],
"app_list": citations[tag]
})
return {
"citations": parsed_citations,
"meta": ", ".join(sorted(app_names))
}
def _fetchNarrativeObjects(self, workspaceName, cells, parameters,
includeIntroCell, title):
if not cells:
cells = []
if not title:
title = 'Untitled'
# fetchSpecs
appSpecIds = []
methodSpecIds = []
specMapping = {'apps': {}, 'methods': {}}
for cell in cells:
if 'app' in cell:
appSpecIds.append(cell['app'])
elif 'method' in cell:
methodSpecIds.append(cell['method'])
nms = NarrativeMethodStore(self.narrativeMethodStoreURL)
if len(appSpecIds) > 0:
appSpecs = nms.get_app_spec({'ids': appSpecIds})
for spec in appSpecs:
spec_id = spec['info']['id']
specMapping['apps'][spec_id] = spec
if len(methodSpecIds) > 0:
methodSpecs = nms.get_method_spec({'ids': methodSpecIds})
for spec in methodSpecs:
spec_id = spec['info']['id']
specMapping['methods'][spec_id] = spec
# end of fetchSpecs
metadata = {
'job_ids': {
'methods': [],
'apps': [],
'job_usage': {
'queue_time': 0,
'run_time': 0
}
},
'format': 'ipynb',
'creator': self.user_id,
'ws_name': workspaceName,
'name': title,
'type': 'KBaseNarrative.Narrative',
'description': '',
'data_dependencies': []
}
cellData = self._gatherCellData(cells, specMapping, parameters,
includeIntroCell)
narrativeObject = {
'nbformat_minor': 0,
'cells': cellData,
'metadata': metadata,
'nbformat': 4
}
metadataExternal = {}
for key in metadata:
value = metadata[key]
if isinstance(value, str):
metadataExternal[key] = value
else:
metadataExternal[key] = json.dumps(value)
return [narrativeObject, metadataExternal]