def search_graph_data(request):
username = request.GET.get('username', None)
query = request.GET.get("query", None)
if query == "null":
query = None
date = request.GET.get("date", None)
if date == "null":
date = None
filter = request.GET.get("filter", None)
if filter == "null":
filter = None
start_time = None
end_time = None
if date:
start_time, end_time = DateRangeParser().parse(date)
hist_type, hist = history_search(request.user,
sort="time",
query=query,
filter=filter,
username=username,
start_time=start_time,
end_time=end_time)
if start_time is None:
start_time = datetime.datetime.now()
if end_time is None:
end_time = datetime.datetime.now()
return hist, start_time, end_time
def search_graph_data(request):
username = request.GET.get('username', None)
query = request.GET.get("query", None)
if query == "null":
query = None
date = request.GET.get("date", None)
if date == "null":
date = None
filter = request.GET.get("filter", None)
if filter == "null":
filter = None
start_time = None
end_time = None
if date:
start_time, end_time = DateRangeParser().parse(date)
hist_type, hist = history_search(request.user, sort="time",
query=query, filter=filter,
username=username, start_time=start_time,
end_time=end_time)
if start_time is None:
start_time = datetime.datetime.now()
if end_time is None:
end_time = datetime.datetime.now()
return hist, start_time, end_time
def _fav_site_calc(self, user):
"""
Helper to compute what the most commonly used
site is for a given set of history items.
Returns a url (domain) that is computed
to be the favorite and the associated favicon
"""
item_meta = {}
hist_type, history_items = history_search(user,
sort="time",
filter="",
username=user.username)
if not history_items:
fav_data, created = FavData.objects.get_or_create(user=user)
self.stdout.write('Updated user %s, no history.\n' % user.username)
return
for item in history_items:
domain = item.domain
if domain in item_meta:
data = item_meta[domain]
data["count"] += 1
data["total_time"] += item.total_time
item_meta[domain] = data
else:
item_meta[domain] = {
"favicon": item.favicon_url,
"count": 1,
"total_time": item.total_time,
"domain": domain
}
max_count = 0
max_dict = {}
for k, v in item_meta.items():
if v["count"] > max_count:
max_count = v["count"]
max_dict = v
self.stdout.write('Updated user %s, favorite: %s\n' %
(user.username, max_dict["domain"]))
fav_data, created = FavData.objects.get_or_create(user=user)
fav_data.domain = max_dict["domain"]
fav_data.favicon_url = max_dict["favicon"]
fav_data.total_time = max_dict["total_time"]
fav_data.visit_count = max_dict["count"]
fav_data.save()
def _fav_site_calc(self, user):
"""
Helper to compute what the most commonly used
site is for a given set of history items.
Returns a url (domain) that is computed
to be the favorite and the associated favicon
"""
item_meta = {}
hist_type, history_items = history_search(
user, sort="time", filter="", username=user.username)
if not history_items:
fav_data, created = FavData.objects.get_or_create(user=user)
self.stdout.write('Updated user %s, no history.\n' % user.username)
return
for item in history_items:
domain = item.domain
if domain in item_meta:
data = item_meta[domain]
data["count"] += 1
data["total_time"] += item.total_time
item_meta[domain] = data
else:
item_meta[domain] = {
"favicon": item.favicon_url,
"count": 1,
"total_time": item.total_time,
"domain": domain
}
max_count = 0
max_dict = {}
for k, v in item_meta.items():
if v["count"] > max_count:
max_count = v["count"]
max_dict = v
self.stdout.write('Updated user %s, favorite: %s\n' %
(user.username, max_dict["domain"]))
fav_data, created = FavData.objects.get_or_create(user=user)
fav_data.domain = max_dict["domain"]
fav_data.favicon_url = max_dict["favicon"]
fav_data.total_time = max_dict["total_time"]
fav_data.visit_count = max_dict["count"]
fav_data.save()
