def get_collection_with_items_for_client(cid, myrefsets, myredis, mydao, include_history=False):
collection_obj = Collection.query.get(cid)
collection_doc = get_collection_doc_from_object(collection_obj)
if not collection_doc:
return (None, None)
collection_doc["items"] = []
tiids = collection_obj.tiids
if tiids:
item_metric_dicts = get_readonly_item_metric_dicts(tiids)
for tiid in item_metric_dicts:
#logger.info(u"got item {tiid} for {cid}".format(
# tiid=item_obj.tiid, cid=cid))
try:
item_for_client = item_module.build_item_for_client(item_metric_dicts[tiid], myrefsets, myredis)
except (KeyError, TypeError, AttributeError):
logger.info(u"Couldn't build item {tiid}, excluding it from the returned collection {cid}".format(
tiid=tiid, cid=cid))
item_for_client = None
raise
if item_for_client:
collection_doc["items"] += [item_for_client]
something_currently_updating = not is_all_done(tiids, myredis)
# logger.debug(u"Got items for collection_doc %s" %cid)
return (collection_doc, something_currently_updating)
def test_build_item_for_client_includes_history_with_arg(self):
response = item_module.build_item_for_client(
self.ITEM_DATA,
self.myrefsets,
self.d,
include_history=True
)
assert_equals(
response["metrics"]["wikipedia:mentions"]["values"]["raw_history"][self.KEY1],
self.VAL1
)
def get_items_for_client(tiids, myrefsets, myredis, most_recent_metric_date=None, most_recent_diff_metric_date=None):
item_metric_dicts = get_readonly_item_metric_dicts(tiids, most_recent_metric_date, most_recent_diff_metric_date)
dict_of_item_docs = {}
for tiid in item_metric_dicts:
try:
item_doc_for_client = item_module.build_item_for_client(item_metric_dicts[tiid], myrefsets, myredis)
dict_of_item_docs[tiid] = item_doc_for_client
except (KeyError, TypeError, AttributeError):
logger.info(u"Couldn't build item {tiid}".format(tiid=tiid))
raise
return dict_of_item_docs
def get_items_for_client(tiids, myrefsets, myredis):
item_objects = get_readonly_item_objects_with_metrics(tiids)
dict_of_item_docs = {}
for item_obj in item_objects:
try:
item_doc = item_obj.as_old_doc()
item_doc_for_client = item_module.build_item_for_client(item_doc, myrefsets, myredis)
dict_of_item_docs[item_obj.tiid] = item_doc_for_client
except (KeyError, TypeError, AttributeError):
logger.info(u"Couldn't build item {tiid}".format(tiid=item_obj.tiid))
raise
return dict_of_item_docs
def test_build_item_for_client_includes_history_with_arg(self):
response = item_module.build_item_for_client(
self.ITEM_DATA,
self.myrefsets,
self.d,
include_history=True
)
assert_equals(
response["metrics"]["wikipedia:mentions"]["values"]["raw_history"][self.KEY1],
self.VAL1
)
assert_equals(
response["metrics"]["wikipedia:mentions"]["values"]["raw_history"][self.KEY2],
self.VAL2
)
def get_items_for_client(tiids, myrefsets, myredis):
item_objects = get_readonly_item_objects_with_metrics(tiids)
dict_of_item_docs = {}
for item_obj in item_objects:
try:
item_doc = item_obj.as_old_doc()
item_doc_for_client = item_module.build_item_for_client(
item_doc, myrefsets, myredis)
dict_of_item_docs[item_obj.tiid] = item_doc_for_client
except (KeyError, TypeError, AttributeError):
logger.info(
u"Couldn't build item {tiid}".format(tiid=item_obj.tiid))
raise
return dict_of_item_docs
def get_items_for_client(tiids,
myrefsets,
myredis,
most_recent_metric_date=None,
most_recent_diff_metric_date=None):
item_metric_dicts = get_readonly_item_metric_dicts(
tiids, most_recent_metric_date, most_recent_diff_metric_date)
dict_of_item_docs = {}
for tiid in item_metric_dicts:
try:
item_doc_for_client = item_module.build_item_for_client(
item_metric_dicts[tiid], myrefsets, myredis)
dict_of_item_docs[tiid] = item_doc_for_client
except (KeyError, TypeError, AttributeError):
logger.info(u"Couldn't build item {tiid}".format(tiid=tiid))
raise
return dict_of_item_docs
def get_collection_with_items_for_client(cid,
myrefsets,
myredis,
mydao,
include_history=False):
collection_obj = Collection.query.get(cid)
collection_doc = get_collection_doc_from_object(collection_obj)
if not collection_doc:
return (None, None)
collection_doc["items"] = []
tiids = collection_obj.tiids
if tiids:
item_objects = get_readonly_item_objects_with_metrics(tiids)
for item_obj in item_objects:
#logger.info(u"got item {tiid} for {cid}".format(
# tiid=item_obj.tiid, cid=cid))
try:
item_doc = item_obj.as_old_doc()
item_for_client = item_module.build_item_for_client(
item_doc, myrefsets, myredis)
except (KeyError, TypeError, AttributeError):
logger.info(
u"Couldn't build item {tiid}, excluding it from the returned collection {cid}"
.format(tiid=item_obj.tiid, cid=cid))
item_for_client = None
raise
if item_for_client:
collection_doc["items"] += [item_for_client]
something_currently_updating = False
for item in collection_doc["items"]:
item["currently_updating"] = item_module.is_currently_updating(
item["_id"], myredis)
something_currently_updating = something_currently_updating or item[
"currently_updating"]
logger.debug(u"Got items for collection_doc %s" % cid)
return (collection_doc, something_currently_updating)
def get_collection_with_items_for_client(cid, myrefsets, myredis, mydao, include_history=False):
startkey = [cid, 0]
endkey = [cid, "zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz"]
view_response = mydao.db.view("collections_with_items/collections_with_items",
include_docs=True,
startkey=startkey,
endkey=endkey)
# the first row is the collection document
first_row = view_response.rows[0]
collection = first_row.doc
try:
del collection["ip_address"]
except KeyError:
pass
del collection["alias_tiids"]
# start with the 2nd row, since 1st row is the collection document
collection["items"] = []
if len(view_response.rows) > 1:
for row in view_response.rows[1:]:
item_doc = row.doc
try:
item_for_client = item_module.build_item_for_client(item_doc, myrefsets, mydao, include_history)
except (KeyError, TypeError):
logging.info("Couldn't build item {item_doc}, excluding it from the returned collection {cid}".format(
item_doc=item_doc, cid=cid))
item_for_client = None
raise
if item_for_client:
collection["items"] += [item_for_client]
something_currently_updating = False
for item in collection["items"]:
item["currently_updating"] = item_module.is_currently_updating(item["_id"], myredis)
something_currently_updating = something_currently_updating or item["currently_updating"]
logging.info("Got items for collection %s" %cid)
# print json.dumps(collection, sort_keys=True, indent=4)
return (collection, something_currently_updating)
def test_build_item_for_client_excludes_history_by_default(self):
response = item_module.build_item_for_client(self.ITEM_DATA, self.myrefsets, self.r)
assert_equals(response["metrics"]["wikipedia:mentions"]["values"].keys(), ["raw"])
assert_equals(response["metrics"]["altmetric_com:tweets"]["values"].keys(), ["raw"])
def test_build_item_for_client(self):
item = {'created': '2012-08-23T14:40:16.399932', '_rev': '6-3e0ede6e797af40860e9dadfb39056ce', 'last_modified': '2012-08-23T14:40:16.399932', 'biblio': {'title': 'Perceptual training strongly improves visual motion perception in schizophrenia', 'journal': 'Brain and Cognition', 'year': 2011, 'authors': u'Norton, McBain, \xd6ng\xfcr, Chen'}, '_id': '4mlln04q1rxy6l9oeb3t7ftv', 'type': 'item', 'aliases': {'url': ['http://linkinghub.elsevier.com/retrieve/pii/S0278262611001308', 'http://www.ncbi.nlm.nih.gov/pubmed/21872380'], 'pmid': ['21872380'], 'doi': ['10.1016/j.bandc.2011.08.003'], 'title': ['Perceptual training strongly improves visual motion perception in schizophrenia']}}
response = item_module.build_item_for_client(item, self.myrefsets, self.r)
assert_equals(set(response.keys()), set(['currently_updating', 'created', '_rev', 'metrics', 'last_modified', 'biblio', '_id', 'type', 'aliases']))
def test_build_item_for_client_excludes_history_by_default(self):
response = item_module.build_item_for_client(self.ITEM_DATA, self.myrefsets, self.d)
assert_equals(response["metrics"]["wikipedia:mentions"]["values"].keys(), ["raw"])
assert_equals(response["metrics"]["topsy:tweets"]["values"].keys(), ["raw"])
def test_build_item_for_client(self):
item = {'created': '2012-08-23T14:40:16.399932', '_rev': '6-3e0ede6e797af40860e9dadfb39056ce', 'last_modified': '2012-08-23T14:40:16.399932', 'biblio': {'title': 'Perceptual training strongly improves visual motion perception in schizophrenia', 'journal': 'Brain and Cognition', 'year': 2011, 'authors': u'Norton, McBain, \xd6ng\xfcr, Chen'}, '_id': '4mlln04q1rxy6l9oeb3t7ftv', 'type': 'item', 'aliases': {'url': ['http://linkinghub.elsevier.com/retrieve/pii/S0278262611001308', 'http://www.ncbi.nlm.nih.gov/pubmed/21872380'], 'pmid': ['21872380'], 'doi': ['10.1016/j.bandc.2011.08.003'], 'title': ['Perceptual training strongly improves visual motion perception in schizophrenia']}}
response = item_module.build_item_for_client(item, self.myrefsets, self.d, False)
assert_equals(set(response.keys()), set(['created', '_rev', 'metrics', 'last_modified', 'biblio', '_id', 'type', 'aliases']))
